They’re wrong.

At Auth0, we designed our growth org to create conflict on purpose. Not the toxic, political kind. The productive kind that happens when smart people with different objectives are forced to work together.

The result? Better decisions, better products, and better outcomes for the business.

Let me explain how we did it.

The Real Problem: Two Metrics That Always Conflict

Every company struggles with the same tension:

Retention - How do you keep users engaged with your product, whether they’re free or paid?

Revenue - How do you actually make money from all those users?

These objectives are in constant conflict.

Sometimes, a happy retained user on a free plan starts working at a large corporation. Because of their great experience with your product, they want to bring the paid version to their company. But if you’d pushed them too hard toward revenue early, they might never have stuck around to become that champion.

Other times, you have users who could be paying customers right now, but your retention-focused team wants to keep them in the free product experience to maximize stickiness.

So, how do you make these two conflicting objectives work together?

By getting one team to own retention, another team to own revenue, and then making them fight it out.

How We Structured Productive Conflict

We gave each team clear, different objectives:

Growth Product owns retention. Their job is to make sure people stay and engage with the product, whether they’re free or paid. Everything they do should increase stickiness and long-term value. Conversion to paid isn’t their focus; keeping people using the platform is.

Growth Marketing owns pipeline generation. Their focus is bringing in the right sign-ups and qualified leads and converting them to paying customers. For some accounts, that means self-serve revenue. For big enterprise deals, that might mean routing straight to sales instead of the product.

These objectives naturally create tension. And that’s exactly what we wanted.

Instead of trying to eliminate that tension, we built a process around it:

Before any change ships that affects the user journey, growth product must pitch it to growth marketing. After they build it, before the pull request merges, growth marketing reviews it again.

The rule is simple: they fight until they reach a good idea outcome together. If they can’t decide, the managers step in as tie-breakers.

Before merging the pull request so that the experiment gets deployed, both teams review together the final UI to see if it’s all good.

This isn’t conflict for conflict’s sake. It’s a structured disagreement that forces both perspectives into the solution.

Where the Conflict Shows Up Most: Onboarding (And Everywhere Else)

Onboarding is where this conflict is most obvious. It’s where growth marketing hands off to growth product. It’s where both teams have legitimate input. And it’s where their different objectives collide first.

But this conflict should exist everywhere, not just onboarding.

Example 1: Growth marketing wants to highlight an expensive feature because it drives conversions. Growth product doesn’t want to show it because it’s really hard to use and could hurt retention.

The best outcome? Show it only to people who have demonstrated specific usage patterns and who likely need this feature right now. Target those users. Skip everyone else.

Example 2: Growth marketing wanted to add a checkbox during signup: “I want to talk to sales.” Check it, and you skip the product entirely, straight to a sales conversation.

Growth product hated this idea. Their whole job is retention. If people never experience the product, they never get retained. The checkbox would kill their core metric.

So they fought about it.

Marketing’s case: Big enterprise accounts don’t need to self-serve. A director of engineering isn’t going to be hands-on with the product anyway. Getting them to sales faster is better revenue.

Product’s case: But small company developers absolutely need the product experience. Showing them a “skip to sales” option degrades the whole flow and tanks retention.

They debated. They pushed back. They challenged each other’s assumptions.

And they arrived at a solution neither team would have reached alone:

Show the checkbox to non-developers. If you’re a director of engineering, you see it. If you’re a developer, you don’t.

Show it to people from large companies. Developer from a 3,000-person company? Show it. Developer from a 30-person startup? Skip it.

This was better for everyone. Better experience. Better conversion for the right accounts. Better retention because you’re not forcing the wrong people through the wrong flow.

It wouldn’t have happened without the fight.

The conflict is useful for growth all around. Onboarding is just the first step, but then it’s everywhere: feature visibility, upgrade prompts, email campaigns, product changes. Every touch point is an opportunity for this productive tension to create better outcomes.

Why Structured Conflict Works

When you design for productive conflict, three things happen:

1. Both perspectives get heard. You prevent unilateral decisions that optimize for one metric at the expense of the business.

2. Teams anticipate each other’s concerns. After a few rounds, growth marketing starts thinking about retention before they pitch. Growth product starts thinking about revenue before they build.

3. Better solutions emerge. The tension forces creativity. You can’t just bulldoze your way through; you have to find an answer that actually works for both objectives.

The key is making the conflict structured and productive:

• Both teams own their metrics clearly

• There’s a defined process for resolving disagreements

• Managers are tie-breakers, not first responders

• The goal is collaboration through challenge, not compromise through committee

The Bottom Line

Stop trying to eliminate conflict between teams. Start designing systems where teams with different objectives must pitch, review, and challenge each other.

The friction isn’t the problem. Avoiding the friction is.

Build the structure. Define the ownership. Let them fight. Watch better outcomes emerge.

Where in your organization are you trying to eliminate conflict when you should be designing it instead?

We are all seeing the shift in traffic to ChatGPT, and starting with consumers. Here’s my take on how it’s going to play out:
We’re currently standing at about 10% of traffic with LLMs of one kind or another, with ChatGPT obviously being the forerunner.
40-50% of consumers say they’re already using gen-AI for shopping tasks (research, recs, deals). Meanwhile, when Google shows an AI summary, users click less. That’s demand capture moving from websites to answers.

The Monetization Play (and why it touches your P&L)

LLM platforms don’t have Google’s AdWords print-money machine. They are losing money, and badly so.
They need revenue, and aside from subscription, the next logical place is transaction revenue. Reporting indicates OpenAI is building checkout inside ChatGPT with a commission model. Perplexity already switched it on in the U.S. with PayPal/Venmo.
I started sharing that thought in podcasts in June/July, reality has caught up pretty fast!

That monetization pressure will have an impact on the e-commerce industry, and this is its possible future.

Take a jewellery online shop.
Historically: Google → ads → 6 tabs → Front End Ecommerce → cart.

It’s a high-friction, low-conversion storyline (only 1 in N ecommerce stores gets the conversion).

In an LLM world: “Need a modern necklace under $300, minimalist.”
The agent returns options with provenance and reviews.
Buyer: “#2 looks great! Let’s get it”.
Done. No category page. No checkout form. Low friction.

Would people let an AI place the order? Some will. Surveys showed ~34% are already willing (back in the ye old days of March 2025…).
It won’t be 100% of buyers; it doesn’t need to be. Even 20-30% of purchase volume shifting to agentic flows bends the stack.

Everyone assumes the future means ChatGPT shows the customer a carousel, then they click a link to complete the purchase.

I disagree. Why can’t they just say, “Option B looks great, order it for me”? The AI already knows the customer’s address and payment details.

I know I’d be happy to do that in many of my day-to-day purchases. I don’t really need to see the storefront if I trust the LLM.

If this happens, the entire concept of a marketing funnel disappears. Because e-commerce shops no longer engage with humans, they only interact with AI agents executing purchases.

And you have no sway over that agent.

The Monetization Play

Here’s how OpenAI actually makes money in this new business model: transaction fees on every purchase their AI completes.

You describe what you want, ChatGPT shows you options, and you pick one. For a small fee, it handles the entire purchase without friction.

This is OpenAI’s real monetization play. Not subscriptions or API calls. A 10% cut on every transaction that flows through their platform, just like the Apple app store (yes, Apple takes 30%, but margins on software are significantly higher).

I know that if I were working on monetization at OpenAI, I’d go all in on that project.

The B2B opportunity would work the same way. Imagine you’re a marketer at Cursor who needs contact data for 50,000 CTOs at the top engineering companies in the US. Instead of logging into ZoomInfo, navigating their interface, building lists, and managing credits, you just tell ChatGPT: “Pull me 50,000 CTO contacts from companies with the most engineers.”

The AI connects to ZoomInfo through Model Context Protocol (MCP), charges you $9, and delivers a clean CSV.

The Death of Digital Marketing as We Know It

When AI agents become the primary purchasing interface, the marketing stack you’ve spent years building becomes worthless. I’m not talking about optimizing for a new channel; I’m talking about the complete elimination of channels as a concept.

Channels are avenues to engage with your prospective audience wherever they might be.

Of course, they’ll still be on Facebook and on TikTok, and you can influence their desires over there.

When people stop opening browsers, every channel you’ve built your business on becomes irrelevant overnight.

No more searches because customers aren’t Googling products, they’re describing needs to ChatGPT. No more Google Ads because there’s no search results page. No more retargeting because there are no website visits to track. No more email newsletters because the AI agent doesn’t have an inbox.

The entire concept of a funnel disappears. There’s no awareness stage, no consideration, no decision journey. The customer describes what they want, the AI presents options, and they pick one. Done.

The shopping experience gets compressed into a single chat interaction, with no product pages, cart abandonment, or checkout optimization. The entire “customer journey” becomes a 30-second conversation.

And if that happens, 90% of today’s e-commerce and B2B marketing stack will be worthless.

Resistance is Futile

Not everyone will adopt this immediately. Like every major change, resistance comes from two places: fear and preference. This one will be no different.

A large part of the population will resist putting their credit card details in the hands of a semi-autonomous AI agent. That’s a reasonable fear.

But some of us will find ways to mitigate the risk. Give the AI a locked $500 card. Set spending limits. Reduce exposure while capturing the convenience. The early adopters will figure out the safety mechanisms.

The other argument I hear is that people enjoy the shopping experience. The CMO of Klaviyo told me that consumers like browsing websites because it’s a virtual form of window shopping. I know many people do.

There is an alternative future

I’m a big fan of dystopian futures, but truth to be told, there is an alternative possible future that is likely. Thanks to Noah (check out his Substack ) for sharing his thoughts.

A different path would be that a subset of brands with deep first-party data and real loyalty keep purchases on-site by shipping best-in-class embedded LLMs.
Think Nike: fit/size history, returns, inventory, and a style graph power a concierge on nike.com/app that beats a generalist model and ends in one-click buy.

No detour through a third-party agent. If this plays out, it becomes a race to operationalize proprietary data fast, likely accelerating brand consolidation around those who can.

B2B mirrors it: “agentic websites” replace SDR demos; you interrogate the product, spin up synthetic demos, and get an implementation plan in chat.


Maybe that’s true for some part of the population. But there’s another part, like me, who don’t want to spend time shopping, especially not scrolling through endless Amazon options trying to figure out which seller is legitimate.

This creates a clear split. Traditional shoppers will continue browsing for discovery and enjoyment. Efficiency-focused buyers will adopt AI-mediated purchasing immediately.

The question isn’t whether everyone will switch. The question is: what percentage of purchase volume moves to AI agents? Even if it’s only 30%, that’s enough to end most of the traditional marketing infrastructure (VCs would walk away immediately).

To be transparent, there is yet another different possible outcome: Shopify and the rest of the stack build shopping agents that are really good and trusted by the buyers. I’m not holding out on that one, but I wouldn’t want to be against Tobi either.

=> These are possible futures:

• E-commerce front-end disappears. A few large consumer-facing LLMs take over, Shopify becomes a headless/ backend platform. That’s my bet.

• Large brands build out their conversational agents with first-party data. The experience is excellent. E-commerce shrinks to fewer fronts where this can happen (amazon + a few thousand great brands).

• The E-commerce stack can create something at scale that performs as well as 1 or 2 above. Shopify + the likes of Gorgias and Klaviyo enable this future.

The Strategic Question Every Marketer Should Ask

Are you preparing for a world where your customers never visit your website?

In an AI-first world, when that customer makes that purchase request through ChatGPT, the AI agent becomes the entire customer relationship.

Your E-commerce company still makes the sale, but you lose everything that comes after: the brand interaction, the data collection, the opportunity for retention marketing. You become a pure product supplier in a system where the AI controls the customer relationship.

The channels that stay relevant are those where there is trust left or where people spend time outside of LLM. Obviously, that’s going to be influencer marketing, TikTok, Facebook, and whatever comes up next. Word of mouth, referrals, and branding are probably going to be more important in the future than they have been up to now.

This is the future that’s emerging right now. The technology exists today. Consumer adoption curves and payment integrations are the only barriers left.

The companies that recognise this shift and adapt their strategies accordingly will thrive in the post-browser world. Those that don’t will find themselves competing for an ever-shrinking pool of traditional shoppers, while the majority of commerce moves to AI-mediated channels they can’t access or influence.

There’s a post-browser world looming over us. It’s no longer just zero-click internet, but a world where people no longer use browsers at all.

The question isn’t whether this future arrives; it’s whether you’ll be ready when it does.

Those black-and-white portraits of Einstein, Gandhi, and Picasso with that unique voiceover: “The people who are crazy enough to think they can change the world are the ones who do.”
Growing up during the Matrix era, when questioning reality wasn’t just philosophy but pop culture, shaped a lot of how I think about growth today.

Twenty-five years later, I get asked the same question weekly:
“What profile should I hire for my head of growth role?”
After working with over 100 companies, I’ve learned that founders are looking for the wrong thing entirely.

They’re hiring from within the same industry, with the same playbooks, expecting different outcomes.
It’s the exact opposite of what Apple did with the iPhone. They didn’t hire from Nokia or Motorola. They hired people who thought phones could be something completely different.

Founders want a marketing leader who succeeds 100% of the time.
I want someone who fails 80% of the time because that’s actually what success looks like in growth.

The person you’re looking for has 3 distinct skills, though they rarely coexist in one individual.

Ideally, you’re finding an effective schizophrenic person who balances insane creativity with a rigorously scientific approach.

The Creative Core: Artists Who See Differently

At the core, you need an artist who sees the world differently enough to generate solutions others won’t attempt.
You’re not hiring a marketer. You’re hiring an artist who happens to work in growth. This person needs to see solutions that don’t exist yet, connections others miss, and approaches that feel uncomfortable to leadership.

A huge part of that art is understanding the buyer at a level most teams overlook. The best growth leaders don’t just generate wild ideas; they land on the right experiments faster because they deeply understand what makes their audience tick. Creativity without that empathy is random noise.

The best growth people I know share something counterintuitive: they’re comfortable with chaos. Not the kind of chaos that leads to missed deadlines, but the intellectual chaos that comes from questioning every assumption. They don’t follow frameworks because “that’s how it’s done.” They ask whether the framework applies to their unique situation and audience.

This is someone who looks at a failing outbound email campaign and questions how to separate the underlying assumptions… Do we have the wrong audience, the wrong message, the wrong offer, or are the emails just not getting through? How do you test those questions? To test out the audience and infrastructure, I asked the following question: “What if we don’t try to get them interested in our product at all?”

That’s the mindset that led me to test telling recipients we had a gift being delivered… intentionally using the wrong address number. Everyone hates dealing with packages delivered to their neighbor’s office. The psychological friction is enormous. The response rate jumped to 12%. Tell me that’s not Art.

Execution Speed: The Angel Investing Portfolio Approach

Creativity without execution is just daydreaming. The second capability is ruthless speed of implementation, someone who can take a wild idea and get it to market before logic talks them out of it.

Of course, this level of velocity only works if the systems are strong enough to support it. Teams that can spin up landing pages in hours and launch outbound campaigns in a day have invested in infrastructure that makes speed possible. Without that foundation, the portfolio mindset collapses under operational drag.

Here’s where the portfolio mindset becomes critical. Most marketing teams run one campaign per quarter and expect it to work. When it fails, they spend months analyzing what went wrong. My teams can run dozens of experiments in that same timeframe because we need signal density to find what works.

Think about it like venture investing. Early-stage investors know 97% of their bets will fail or underperform. But they don’t try to reduce their failure rate to zero, which would mean taking no risks and missing the 3% that generate all the returns. They optimize their portfolio to increase their chances of finding those breakthrough outcomes. Then they double down on winners.

Growth works the same way. That wrong address delivery experiment? It was one of many tests we ran that quarter. Most failed completely. A few showed modest results. That one showed us how to move forward and changed how we thought about that audience psychology. The high velocity angel investing approach made the 80% failure rate the best path to success.

Scientific Rigor: Kill Your Art When It Fails

The third pillar is the hardest: scientific rigor.
All experiments should start with a good hypothesis (why would it work?), a baseline of the current situation (metrics matter), and a rigorous analysis of the outcome.

Good hypotheses rarely appear out of a blank canvas. They come from the exploratory chaos of testing many things, analyzing wins and losses, and spotting patterns worth formalizing. The chaos is not opposed to rigor; it’s the raw material rigor shapes.

This also means being willing to trash your masterpiece if the data says it doesn’t work.

For every growth experiment, I ask three questions:

• What psychological principle am I leveraging, and does it match my audience?

• What’s the potential lift if this works perfectly?

• Is the experiment designed well enough that we’ll know if it failed because of the idea, not because of poor execution or sample size?

If you can’t answer all three, you’re running art projects, not growth experiments. If you can’t answer, DO NOT ATTEMPT the experiment.

At the same time, rigor doesn’t mean dismissing a project too quickly. Sometimes impact is underestimated, or an audience turns out to be more responsive than expected. Home runs are often more impactful than the initial model predicts, so you need space in the system for positive surprises.

The psychological component is crucial because that’s what AI can’t replicate yet. AI can optimize within existing frameworks but struggles with the intuitive leaps that create breakthrough campaigns. Understanding that people hate the inconvenience of neighbor deliveries and that you can leverage it to measure audience quality requires human insight into social dynamics and personal frustration. I haven’t seen an LLM do that yet.

The math (lift & stat-sig estimate) matters because growth is ultimately about scalable impact. That offset delivery experiment taught us our infrastructure worked, and our audience was reachable. We had a product-market fit problem, not an execution problem. Without rigorous analysis, we would have kept optimizing email copy instead of fixing the fundamental issue.

What Success Stories Actually Hide

The problem isn’t finding talented people. It’s that the traditional growth hiring process selects for the wrong traits entirely. Founders look for someone with a track record of “successful campaigns” when they should be looking for someone comfortable with consistent failure (as long as we are learning something).

And they need to create a culture that favors that within their company.

Most growth candidates come with a portfolio of wins and case studies. They’ll show you the webinar series that generated 500 leads or the content campaign that drove a 20% increase in traffic. What they won’t show you is the 50 experiments that failed to get those results, because admitting failure is career suicide in traditional marketing.

But here’s what I’ve learned from working with hundreds of companies: the person who can show you their failures is more valuable than the person who only shows successes.

How to Implement Effective Craziness

The methodology itself requires systematic chaos. Here’s how to structure it:

Phase 1: Hypothesis Generation (Weekly) Your growth manager (the artist) comes up with 10-15 experiment concepts weekly based on psychological insights and audience research.

Phase 2: Rapid Prototyping (48-72 hours) Your growth team (execution specialists per channel) builds minimal viable tests for 3-5 concepts weekly. Landing pages should be done in hours, and outbound campaigns in a day.

Phase 3: Scale the Winners, Learn from Failures (Monthly) Your analyst reviews all active experiments against the initial hypothesis. Kill everything that doesn’t meet the thresholds, regardless of how creative or beloved it may be. The 20% that work get full resource allocation and systematic optimization. But never at the expense of generating new experiments.

This creates a continuous pipeline where creative ideas flow into rapid testing, failed experiments get killed quickly, and breakthrough concepts receive scaling resources. The methodology protects creative risk-taking while maintaining scientific discipline.

The Competitive Advantage of Systematic Chaos

Companies that implement effective craziness methodology create sustainable moats because competitors can’t easily replicate the systematic approach to breakthrough thinking.

When they see your successful campaign, they copy the tactic. They miss the deeper system: the weekly hypothesis generation, the rapid prototyping cycles, the portfolio analysis that killed 30 other experiments to find the one that worked.

Most marketing teams operate in a reactive mode: launch a campaign, wait for results, analyze what happened and plan the next campaign. The effective craziness methodology creates proactive learning loops where insights compound across experiments.

Your artist learns which psychological principles work with your specific audience. Your executor develops templates and systems that decrease time-to-market. Your analyst builds models that predict which experiment types have the highest probability of success.

In six months, this system generates insights competitors can’t access because they lack the methodology to create them. They’re still optimizing email subject lines while you’re discovering entirely new channels and approaches that didn’t exist before.

In a world where everyone has the same tools and data, the only sustainable advantage is the willingness to be systematically unreasonable.

Just like those crazy ones who thought they could change the world… and then actually did.

I would like to thank Noah Adelstein and George Bonaci for their contributions to this content.

Talk to most earlier stage startup founders and you'll hear the same story:
"We need to find a better CMO."
Your last one lasted 14 months. The one before that made it 18 months.
The search firm promises this time will be different.

It won't be.

CMO tenure averages 1 year and 2 months in tech — the shortest of all C-level executives. That's barely enough time to vest equity, let alone build anything meaningful. Companies spend months recruiting, pay search firms $50K+, offer $500K+ packages, and watch the same failure pattern repeat.

Working with 30+ companies annually, I see this across every vertical. The pattern is identical: hire CMO with initial optimism, slow realization they can't execute, quiet departure (bonus if you make them an advisor for the off-ramp), cycle repeats!

But here's what we miss: it's not the people. It's the system.

The Root Cause: No L8 Track for Marketing

You can be an L8 engineer at a FAANG — highly respected, great compensation, doing actual technical work. No one forces you into management. You can build systems, architect solutions, and solve complex problems for your entire career. The technical track runs parallel to management, not underneath it.

There's no equivalent IC track for marketers.

In marketing, the progression is brutal in its simplicity. You do 4-5 years of actual marketing work. Growth hacking, running ads, Inbound/SEO, building funnels, optimizing conversion rates. Then you get promoted to managing a team. By year 6-7, you stop doing marketing completely. You're managing people who manage people who do the work you stopped doing.

This isn't a choice — it's the only path forward. Want to make more money? Manage more people. Want respect? Build a bigger team. Want to advance? Stop executing and start strategizing.

So every CMO with 10-12 years of experience hasn't touched actual marketing execution in half a decade. They've been in conference rooms talking about marketing while other people do it. They know what good creative looks like, but they haven't written copy themselves in years. They understand attribution theory, but they haven't set up tracking themselves.

If you think I’m wrong, give your CMO any of the modern GTM tools and let me know how it went!

AI Has Accelerated the CMO Crisis

This CMO approach worked fine when marketing changed slowly.

Learn the fundamentals, manage people who execute variations of what you used to do. Get credit for your team impact (and truthfully its size too, which is dumb but true).

But now AI workflows are reshaping everything monthly. These leaders can't implement what they don't understand. They are disconnected.

The pace of change is unprecedented. A year ago, most marketing teams were manually writing ad copy and email sequences. Today, they're building AI workflows that generate hundreds of personalized variants, A/B test them automatically, and optimize based on real-time data.

But to build a great workflow, you need to know how to do the actual work. What are you trying to automate?

I see this issue across every company we work with. CMO joins, promises transformation, spends months "assessing the team" and "developing strategy."

Meanwhile, the actual work — setting up new AI tools, optimizing workflows, testing new channels — happens without them. Or worse, doesn’t happen at all.

The Incentive Misalignment

Marketers can't afford to fail. Miss one product launch and you're damaged goods. Miss two launches and you're out.

So you optimize for safety, not results.

The CMO playbook becomes predictable: hire expensive agencies, follow "best practices," run safe campaigns that won't embarrass you. Never take the bold creative risks that actually move metrics. Better to deliver mediocre results than risk spectacular failure.

But here's the deeper problem: marketing success isn't even objective. Engineering has tests that pass or fail. Product delivers features following a spec. Sales has deals that close or don't. Marketing has the CEO's taste.

"I don't like this creative." "This messaging doesn't feel right." "The brand voice is off." These are opinions, not metrics. And when the opinion comes from the person who controls your budget and your job, you optimize for their taste, not market response.

What Happens Next

Here's what I think happens in the next 24 months: 50% of early-stage companies (Series A → C) skip the CMO hire entirely (Also happening at a late-stage fintech company with no CMO…).
They'll build VP-level execution teams instead. And I think it’s the right call.

Why this prediction?

• First, we’re back in a high-growth era. Companies must grow fast, and grow now. A CMO who can't generate immediate growth is harder to justify than a VP who can. When timeline is tight, execution beats strategy.

• Second, AI reduces team size requirements. The workflows that used to need 10-person teams now need 2 people and AI tools. You don't need management layers when you don't need large teams. You need senior ICs who can build and optimize these hybrid systems.

I could be wrong. Maybe marketing leadership figures out how to stay connected to execution. Maybe the L8 track emerges within traditional marketing career paths. Maybe AI creates new roles that bridge strategy and execution.

But here's what I see: the companies that adapt first will have a massive advantage. While competitors cycle through expensive CMOs, they'll have senior ICs building growth engines. While others manage management layers, they'll be optimizing actual workflows.

The future belongs to people who can do the work, not just talk about it. Marketing is finally catching up to what engineering learned decades ago: the best individual contributors are worth more than average managers.

High-growth SaaS firms rely on a living library of evergreen content to drive a steady pipeline, yet those libraries can grow to tens of thousands of pages — far faster than teams can manually maintain. The AI revolution only accelerated net-new content generation velocity, putting pressure on teams to create more content than ever, which ultimately lands on the desk of organic growth teams to maintain, refresh, and update (or prune!).

However, each refresh still demands hours of keyword research, competitor analysis, writing, reviews, manual interlinking decisions, as well as CMS wrangling — capping each refresh velocity at roughly 40 articles a year, and leaving huge SEO wins untapped.

An underlying question arises: How can marketing teams update alleviate this operational burden and leverage the untapped value of content refresh by updating hundreds of pages in minutes. And doing so without diluting brand voice or quality?

Intelligent, CMS-native automation (as Webflow achieved with AirOps) turns refreshes into a one-click workflow that scales quality updates 5× and lifts traffic 40% within days — setting a new operating model for content-led growth.

1) The hidden cost of manual refresh at scale

Webflow’s content library is a growth engine — 44000 live pages and 1000+ blog posts that deliver more than 80% of new customers through SEO and direct traffic.

Yet engines that size can easily stall when every refresh demands three-plus hours of keyword research, rewriting, reviews, and CMS gymnastics.

At that scale, Webflow’s SEO team can only update about 40 articles a year, leaving thousands of assets to age out of alignment with algorithm shifts and evolving search intent.

Also, stale posts slip down the rankings, category pages stay thin, and high-value queries pass Webflow by, all while seasoned SEOs like Vivian spend their time copy-pasting instead of steering strategy.

The opportunity cost isn’t just lost traffic; it’s momentum, mindshare, and a compounding pipeline gap that widens with every unrefreshed URL. To solve this gargantuan challenge, Josh and his team turned to AirOps.

2) Automating the workflow with AirOps + Webflow CMS

AirOps turns Webflow’s refresh grind into a precision workflow that thinks like an SEO strategist and ships like a developer. Let’s dive into it step by step.

A. AI-powered keyword and competitor analysis

First, an AI-powered scan grabs the article’s primary keyword, benchmarks it against the current top-ten SERP results, and builds an SEO brief — complete with brand guidelines and a “small / medium / large” update scope — within seconds. No manual Ahrefs or SEMRush dives required — they’re not skipped, but all embedded in AirOps research powersteps.

The workflow flags missing sub-topics, intent mismatches, and backlink gaps, then wraps those insights into a one-page brief that tags the effort needed (small / medium / large) so editors can triage work in minutes, not meetings.

B. Dynamic, answer-engine-ready content recommendations

From there, these dynamic recommendations upgrade every layer of the article:

• Updates title tags, meta descriptions, and Open Graph fields to align with new search intent.

• Refreshes outdated statistics, and internal examples, while preserving brand credibility.

• Rewrites or expands thin sections to hit optimal word-count ranges and satisfy Google’s helpful-content guidelines.

During the process, the team optimizes for conversational and natural active voice, short and clear declarative sentences, while balancing related keywords like synonym and entity coverage. All of this within defined content structures like headings, numbered lists, and bullets to match the AI Overviews snippets. In simple terms, these are the things that boost the odds of landing “position zero”.

C. Human-in-the-loop editorial oversight

Vivian Hoang — Webflow’s SEO Lead — still runs the editorial console behind AirOps machine. She reviews the AI-generated brief, green-lights or tweaks recommendations, and sets the update scope.

And after AirOps generates edits, she can accept or reject each change inline — no messy Google Docs reconcile. Just grasp how much context switching the team saves by just reviewing content refreshes in one, single pane of glass.

D. Smart prioritization and automated internal linking

How do you prioritize which articles to refresh if you have 44000 live pages and 1000+ blog posts? We’re glad you asked.

• A new ruleset of the workflow grades each post’s staleness so AirOps can prioritize low-risk, quick wins first while resource-heavy rewrites are scheduled later.

• The workflow also knows when to suggest a light polish versus a deep rewrite, and automated internal-linking logic is next on the roadmap.

• The next release will auto-inject links to newer cornerstone pages, tightening topical clusters and lifting crawl efficiency.

E. One-click CMS publishing

Finally, one-click native integration with Webflow — or other CMSs — sync ships updates straight to Webflow, wiping out staging queues and copy-paste errors.

• Approved updates sync directly into Webflow — eliminating copy-paste errors, staging queues, and developer tickets.

• Publishing cycles compress from days to seconds, letting the team push fresh, optimized content while competitors are still writing briefs.

• And last but not least, a final pass on staging lets Vivian adjust structure and add contextual internal links before hitting publish (as mentioned before, this can and will be also automated soon within AirOps).

The result is a refresh machine that keeps humans steering strategy while software handles the heavy lifting.

3) The business impact of velocity

When operators have enough mindspace to shift from manual refresh queues to big-picture process architecture and debugging, it’s not just about speed and time savings. But speed converts straight into revenue leverage:

• 5X capacity expansion: Webflow’s team now pushes hundreds of article updates a year—five times their old pace — keeping every high-intent page current and extending their total addressable keyword surface without adding headcount.

• 40% traffic uplift in days: Fresh, intent-matched copy wins back lost rankings fast; early cohorts saw organic sessions jump 40% within the first week. Because each update compounds on the last, those gains snowball quarter over quarter into a resilient, low-CAC pipeline.

• Time redeployed to growth experiments: Hours once burned on keyword research and CMS gymnastics are now freed for strategic sprints—think net-new content, CRO tests, and cross-domain experiments that move revenue needles instead of pixels.

Altogether, this impact redefines a new operating standard for repeatable and modern SEO. With execution automated, the content org finally runs on OKRs rather than reactive firefighting, perfectly echoing Webflow’s promise to help teams “build better, faster.” The result is a distribution muscle and flywheel competitors struggle to match.

4) The new SEO operating model: automate, publish, win

This playbook shows how new, programmatic models are emerging in organic growth — especially where no one is looking.

Organic growth is evolving from rigid editorial calendars to a signal-driven system where rank drops, traffic decay, and intent shifts instantly fire off automated content refreshes across metadata, copy, and even other modals like video.

While most teams chase shiny net-new AI-generated LinkedIn or blog post, the real edge now lies in programmatically pruning thin pages, consolidating cannibalised clusters, and continuously re-optimising proven winners — especially for domains with deep footprint and solid authority.

This real-time, “if-this-then-update” loop turns every high-value URL into a living asset that adapts faster than Google’s algorithm, letting lean content teams compound traffic gains while competitors are still diagnosing what changed.

And this becomes even more relevant if you have a large website footprint with a decent DR. That’s when you ought to gain more from optimizing your existing properties than creating net-new content.

Closing thoughts

Webflow’s journey proves that the next SEO frontier isn’t publishing more — it’s wiring content growth loops that turns every existing page into a self-healing growth asset.

By fusing AI-driven research, human editorial checkpoints, and one-click CMS delivery, they lifted refresh capacity 5X, recaptured 40% more organic traffic in days, and freed talent to hunt net-new experiments instead of wrestling spreadsheets.

As our Founder Guillaume Cabane likes to say:

“In 2025, top 1% marketers won’t be running campaigns — they’ll be debugging workflows.”

This playbook embodies just that. If your domain already carries authority, the fastest route to the compounding pipeline isn’t another blog post — it’s automating the upkeep of the ones you’ve got.

Start small: pick ten decay-flagged URLs, plug them into an intelligent workflow, and measure the lift. Only once you see velocity converted to an uplift, you’ll wonder why you ever let stale content throttle growth.

Every B2B SaaS team is fighting for the same, ever-so-budget-strapped buyers in an ever-so-crowded market with thousands of competitors.

Especially if you’re in MarTech, you’re entering an industry growing new players 100X in 10 years at a 39% CAGR. With demand being tight on budget, and your competitors scaling up quickly, it’s harder than ever to stand out.

But also, even the smallest competitive edge in go-to-market execution can make the difference. Here’s the challenge though: most competitive-intelligence workflows are still manual — a Slack channel full of random links, a never-maintained spreadsheet of half-baked notes, or pricey SaaS point solutions that only track narrow slices of the market and serve you “intel” back in the aforementioned Slack channel.

In the best scenario, you have a series of competitor pages to show your buyers how you’re better, but that won’t get you actual competitive intel to inform your product and marketing strategy.

An underlying question arises: how can GTM leaders and their squads unlock always-on, full-funnel intelligence without hiring an army of analysts?

Enter AI agents that continuously scrape and synthesize every public competitor signal — from blog posts to ad creatives to Glassdoor reviews — and pipe structured data straight into product strategy, demand generation, and pipe-building motions.

Why competitive intelligence? And why now?

In the post-ZIRP, AI era, the productivity miracle of LLMs is adding immense pressure to deliver to GTM teams, making revenue targets constantly growing while budgets and teams keep shrinking. In this scenario, CMOs must squeeze as much pipeline as possible from fewer and fewer dollars.

Meanwhile, ever-crowded categories and “AI-powered” feature parity mean a hard-won product differentiator can disappear in weeks, not quarters — making speed of execution and adaptability the actual differentiator of this generation. That is, if you have the right intel to execute on.

In this hyper-paced environment, traditional “intent” data or quarterly analyst reports only tell you what happened after the deal already slipped away, yet teams are already drowning in raw activity logs with zero context.

That’s why competitive intelligence can flip that script; autonomous agents continuously scrape every public signal — not just black box intent, but new blog posts, webinar decks, pricing tweaks, ad creatives, regional spend shifts, hiring surges, one-star G2 reviews. And then translate the noise into real-time insight.

1) Turning competition into an ongoing intel channel

AI-powered competitive-intelligence agents turn scattered market noise into a living, searchable feed that every GTM leader can use with confidence.

In a market where competitors abound — and their activity across product, marketing, and sales — competition itself becomes a live intel channel that can power three high-leverage plays: sharper product and PMM strategy, smarter demand-gen distribution, and high-intent prospecting for buyout or switch campaigns.

They wipe out the Monday-morning slog of copy-pasting links into a “CI roundup,” then widen the lens far beyond social mentions by scraping blogs, webinar decks, pricing pages, release notes, ad libraries, regional spend shifts, hiring trends, and even one-star G2 reviews.

Because the system cross-references those signals, it surfaces patterns a single-point tool can’t catch — think messaging pivots that precede a price hike or a surge in EU ad creative that hints at a regionally focused land-grab.

And unlike DIY sleuthing or single-thread social-listening tools, and even modern “competitive intelligence platforms” like Crayon, or Klue, today’s CI stack stitches all those inputs into one actionable feed, letting you act before rivals even hit publish.

This approach is different also from intent signals tools for outbound such as Common Room, Attention, or Regie — these agents structure every insight around your roadmap and campaigns beyond just pipe generation or deal closing use cases. They can surface which features to prioritise, which claims to defend, which personas to target, and which channels to double-down on.

The payoff is clear: faster product bets, sharper positioning, and a demand-gen engine that reacts in hours, not quarters.

2) Fueling product, marketing, and positioning decisions

Competitive-intelligence agents turn a chaotic firehose of market signals into a single source of truth your product and PMM teams can act on daily.

Some of the powerful product and PMM use cases include:

• Auto-summarizing every competitor launch to monitor feature parity and direction

• Scraping job boards — or LinkedIn posts of people leaving — for hiring bursts or layoffs that hint at company health, roadmap bets, or pivots

• Highlight sudden tweaks in packaging or pricing that could unsettle your own tiers

• Scrape AI-tagged sentiment from thousands of one-star G2 reviews to expose feature gaps buyers feel — or segment sentiment across industry, buyer role, and more

• Add in constant monitoring of webinars, landing pages, blog themes, changelogs, and product documentation to spot messaging and content pivots weeks before they hit the front page of LinkedIn.

The result is faster, evidence-based prioritization and strategy, at massively cheaper gathering and processing costs — so you ship the right capabilities, sharpen positioning before the market narrative shifts, and carve out points of differentiation that stick even in an AI-accelerated arms race.

Want to setup competitive intelligence as your fresh strategy channel? Book a call with one of our seasoned advisors to discuss your challenges.

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3) Multiplying demand gen and lead gen impact

Armed with the knowledge of the right things to track — and AI agents set up correctly — competitive intelligence isn’t a rear-view mirror anymore. And whether product and PMM use cases we’ve seen above are key for broader strategy, this channel is also fresh fuel for repeatable, high-conversion plays that move pipeline.

With an always-on feed of ad libraries, keyword moves, customer sentiment, and hiring signals, high-performing teams orchestrate can orchestrate two core motions:

1. Distribution intelligence for demand gen teams: by scraping every piece of a demand gen play across ads, content, and lifecycle, plus the profiles of who click or comment, you can spin up counter-campaigns or fill in white-space content before they cement momentum. Imagine the following:

   • Track every creative across LinkedIn, YouTube, Meta ad libraries, channel presence, headlines and creatives, spend surge, regional competitors test, plus who clicks or comments.

   • Layer (gated) content topic and keyword analysis, landing pages, newsletters, and organic social from thought leaders accounts and company pages to identify correlation across reactions, sentiments

   • Spin up ad counterattacks and current responses with dedicated landing pages, ads, or tailored creatives that spotlight your edge before rivals scale budget.

   • Identify white-space keywords or keywords hijacks by monitoring SEO/SEM shifts, then intercept emerging terms with optimized content and paid bids to own share-of-search.

   • Build competitor-intent retargeting audiences from users engaging with rival ads, then serve tailored proof-points like industry-adjacent testimonials, case studies, or ROI calculators to help reframe the evaluation.

2. Prospecting and retention fuel for outbound and success teams: flag accounts lurking in a competitor’s community, disgruntled G2 reviewers, or buyers stuck in legacy contracts and trigger plays while intent is hottest. The best teams package these raw signals into these plays:

   • Scrape in-demand prospects from one-star reviewers or negative segment social comments to time tailored outreach that is backed by superior performance, pricing, or proof points.

   • Combine the above sentiment with abrupt pricing hikes with your new product releases to time tailored outreach that suggests how well you’ve done your research, and how your product can better match customer needs compared to alternatives.

• Equip sellers with segment-specific battlecards that update in real time and show how the competitor pitches RevOps vs. Marketing vs. other personas, the feature gaps to press, the best pricing, packaging and negotiation levers, and the poof points to share.

• Trigger switch-incentive plays across landing pages and outbound bundle migration help, ROI calculators, and time-boxed discounts that create urgency while intent is hottest.

• Last but not least, tie those plays together with playbook inversion: reverse-engineer public onboarding docs, product tours, and case studies to expose cracks in how competitors sell and deliver, then feed the insights back into your own nurture flows and demo scripts. The result is a bigger, faster pipeline—powered by actionable context that no spreadsheet could surface.Run a churn-surge monitor for leadership exits, Glassdoor declines, or surprise price hikes, and immediately outbound with stability-first messaging.

The result? Larger, faster pipeline, delivered by insights no spreadsheet could surface and no quarterly report could catch in time. Bonus: layer these plays one on top of the other to see your pipeline and strategy differentiator compound across the entire GTM motion.

Closing thoughts

To wrap, competitive intelligence is no longer a static slide deck — it’s a living dataset that can sharpen every product decision, amplify every campaign, and pinpoint prospects the moment they’re ready to switch.

By wiring AI agents into the public exhaust of your rivals — ads, reviews, pricing pages, changelogs, even Glassdoor chatter — you trade lagging indicators for real-time cues.

Product and PMM teams spot roadmap gaps before they make the roadmap; demand gen leaders launch ad counterattacks and keyword hijacks while competitors still A/B-test headlines; sellers trigger switch-incentive plays the minute a one-star review drops or a pricing hike lands.

Layer these moves and the effect compounds: faster differentiation, bigger pipeline, and a GTM engine that learns as quickly as the market shifts.

Ready to use agents to turn competitors into your most reliable and current strategy source AND growth channel? You better be, otherwise your competitors will.

As this discipline emerges — like every time new trends do — it’s normal to hear contrasting opinions and misconceptions. We’ve seen it 10 years ago with the growth hacker, and even with RevOps. Companies confuse GTM engineers with RevOps, growth PMs, and even CRM admins.

So, what is GTM Engineering? First of all, we feel more needs to be said about the origins of the term, how it came to be, and why now. With that in mind, we’ll have more context to define it. But while everyone is trying to give a positive definition of the term, we’ll do the opposite and unpack our anti-definitions instead — what GTM Engineering is not.

Then, in the next post, we’ll lay out a first principle thinking framework and primitives to help people get their heads around this emerging, multi-disciplinary position.

The origins of GTM Engineering

Let’s first trace back the DNA of GTM Engineering. While the first job titles were made popular in early 2024 by Clay, the role isn't as new as it seems.

Companies like Ramp, Gorgias, and Rippling — considered among the golden standard of growth teams — have been the pioneers of embedding engineering talent and skill sets within the go-to-market function.

Whether they were called growth engineers or growth ops, these roles shared the usage of automation, data, and APIs to scale pipeline and revenue without increasing the size of the go-to-market team.

Growth engineers were the technical counterpart to growth marketers — building technical solutions to solve GTM use cases in an efficient and scalable manner.

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But, where does this need for GTM efficiency suddenly come from? There are two key factors contributing to the rapid rise of GTM engineers last year.

First, When VCs pulled back their money in 2023, startups had to keep scaling revenue without expanding headcount — or actually firing half of their teams — making GTM efficiency the name of the game. In other words, they needed to:

• Maintain or speed up time to market, pipeline and revenue velocity

• While improving efficiency and costs metrics (eg. cost per opportunity, ROI, etc.)

• And keeping headcount constant (eg. revenue per employee)

However, “automation” still meant getting an engineer to work on building custom scrapers, ETLs and data warehouses connections — strategies that were not that accessible to most teams, especially given growth engineers were not just rare, but also expensive!

That’s where the promise of the last generation of LLMs comes in. New SaaS categories quickly emerged between 2023 and 2024 wrapping few critical ingredients of growth engineers into low-code SaaS and making them A LOT more accessible to non-technical users. Think like:

1. Custom waterfall enrichment to make prospecting and research dead easy

2. Workflows UI to line up multi-step, tailored GTM operations across the lifecycle

3. LLM capabilities and prompt engineering to scale research and personalization

4. Robust integration layer with built-in APIs

The result has been a fervent and colorful GTM tech industry popping up in the last 24 months — with plenty of leading players like Clay, Cargo, Lemlist, LaGrowthMachine, Common Room, and AirOps, blending or specializing in the above capabilities.

Suddenly, you could do the advanced work of a growth engineer with just a few lines of code — and you could actually code the rest by just prompting ChatGPT.

And in late 2024, the concept of “founder mode” popularized by Brian Cesky compounded this efficiency mantra of “doing more with less” into the industry. This — coupled with the ever-present promise of AI — made the idea of small, lean, and technically-enabled orchestrators too compelling to ignore.

With the entire industry on founder mode — and the right AI tools for the job — there were no more excuses. A new storm was coming.

Service-as-Software and the wave of Clayagencies

In this new hyper-competitive arena, everyone needed to keep scaling GTM operations efficiently but no one could afford in-house growth engineering that would do custom “integration or automation work”.

That’s why GTM Engineering started to become popular; because it’s a role that blends capabilities and knowledge across sales, marketing, customer success, and engineering in one person.

A cross-functional GTM skillset alone yields massive efficiency gains — because you have one person doing the job of three — and when enabled by the right AI tools and workflows, its promise scales 10X.

With that in mind, it became clear that a growth engineer wasn’t a must-have to build complicated workflows in your GTM motion. But a hands-on, semi-technical GTM operator with Clay and an email sequencer could do it. And as more non-technical teams got exposed, the promise of GTM Engineering grew, and the overall movement accelerated.

However, it’s still very rare to find this type of talent; and even when you do, it can be expensive and risky, especially as a full-time hire.

That’s why we believe one of the first cohorts to really help grow awareness of the role were the so-called Claygencies. These were outbound agencies (and consultants) that embraced the GTM Engineering principles on various fronts. These operators consistently:

• Use AI-powered GTM tech to scale beyond $1M ARR with extremely lean teams, really embodying the core premise of GTM Engineering.

• Provide expert GTM services in the form of process workflows, and have strong incentives to productize these workflows with software and AI.

• Use AI and their toolstack as a core differentiator from ‘traditional agencies’, and to showcase their differentiators via the creative workflows they build.

• Offer a more specialized and lower-risk alternative to full-time hires, who are harder to find and more costly to onboard.

And that’s why we’ve seen Clayagencies take over LinkedIn with tool- and workflow-related content on LinkedIn. All the tools, plays, and workflows were all over Linkedin for grab — an insane awareness machine for the entire GTM tech industry and players alike.

Emerging vendors seized this moment to carve their spot in the industry and spin up their agency programs, each for their own ‘niche’ — RB2B for website deanonymization, AirOps for content and SEO, and so on and so forth.

And a new category of agencies and consultants was born — service-as-a-software. A new business model — with AI and GTM Engineering capabilities at its core — that would answer both calls — GTM efficiency and ‘founder mode’.

Stack vs. Expertise — what matters most?

At this point, the underlying question emerged:

“What matters most to drive GTM efficiency? The tool stack or the GTM Engineering execution expertise?”

The Clayagencies buzz initially took off largely due to capabilities that these new tools democratized — waterfall enrichment, AI workflows, and the likes. If you step back, the underlying tech primitives haven’t changed — we had Clearbit for enrichment, workflow editors like Zapier, and ETLs like Segment even before 2020.

The real spark came from making these capabilities more accessible to semi-technical users, and layering the LLM component and prompt engineering on top — which shortened the learning curve and opened the door for more niche, specialized platforms.

Look at any feed and you’ll see a flurry of tactics and workflows touting a slick, new specialized data provider or email sequencer — often packaged as plug-and-play workflows you can easily copy and paste.

“Just comment ‘workflow’ and you’ll scale 1000 meetings in 1 day.” We’ve all been there!

Yes, it’s hard to resist the allure of a “silver bullet,” and yes, these new workflow tools really make it super easy to achieve results. However, now that the cat is out of the bag and these tools are known and accessible, we believe that the elephant in the room comes down to one thing — execution.

So, to answer the question, we believe expertise matters A LOT more than the underlying stack.

Which brings us to the next topic.

What GTM Engineering ISN’T all about

Armed with this additional context, let’s move onto our anti-definition(s) of GTM Engineering.

1) GTM engineers don’t define the entire go-to-market

First and foremost, we believe GTM engineers scale an existing strategy instead of inventing one. GTM engineers don’t select markets, craft messaging, or determine positioning — those decisions come from leadership, marketing, and product. Instead, GTM engineers enable these teams to streamline scattered processes into scalable workflows.

And while GTM engineers don't set strategy, their technical expertise can help uncover new, creative, and performant ways to achieve an outcome. For example:

• The Marketing Director doubles down on LinkedIn Conversation Ads, targeting Salesforce users, who drive higher retention, engagement, and ACV.

• Instead of relying solely on the TAM list and CRM contacts, GTM Engineering proposes to expand the audience by pulling recent companies using Salesforce as their CRM.

• The Marketing Manager runs a focused Salesforce campaign, while the broader team targets US-based SaaS companies (50-500 employees).

• The GTM Engineer then helps build an automated system to enrich incoming LinkedIn leads with technographic data, analyze intent signals, and use an LLM-driven scoring model to prioritize leads.

• For Salesforce campaign users, reps receive key insights enriched with real-time news, promotions, new hires, and job postings. An LLM like Claude 3.7 Sonet summarizes the data, making it ready for outreach.

• For high-intent leads, the system automatically triggers personalized outreach emails from the AE’s account, tailored to the prospect’s tech stack and company news. AEs get timely notifications for follow-ups, turning hours of manual work into a streamlined, data-driven process, while still supporting the original marketing strategy.

Marketing still owns the ad strategy and Sales the engagement process, but the GTM engineer's technical capabilities opened up new, scalable ways to achieve the same outcome.

2) GTM Engineering are not Growth PMs or Growth Engineers

We’ve made this distinction before, but we want to make this even clearer.

Growth PMs and engineers emerged to drive activation, retention, engagement, and monetization metrics through experimentation. While there are overlaps with GTM Engineering, there are two crucial differences:

• Technical execution: While growth PMs coordinate with growth engineering teams, GTM engineers directly build and implement technical solutions.

• Scope of impact: Growth PMs and engineers typically own specific product metrics like activation, free-trial to demo conversion, or sign up page conversion improvements, while GTM engineers influence and contribute to pipeline and revenue metrics across the entire customer lifecycle.

And whether there’s a lot more overlap between Growth engineers and GTM engineers, let’s just say that:

• Growth engineer is more involved in the dev work based out of experiment hypotheses — and work with PMs and designers to pull it off — less so in the business understanding.

• GTM engineers are more involved in the business, work primarily with the business teams, and are proactively influencing those decisions.

3) GTM Engineering are not RevOps

Out of all the mixed-up roles, RevOps is probably the most often mistaken for a GTM engineer, probably because RevOps and GTM Engineers work closely together. However, they serve fundamentally different purposes. The key difference is that:

• RevOps focuses on the foundational strategy and pipelines — like setting up the CRM instance, decide which fields to report, the pipeline stages, foundational journeys, and reporting layer across marketing, sales, and success.

• GTM Engineering scales the efficiency of these foundations via tailored, automated and technical solutions.

So while RevOps helps decide where to allocate budgets, GTM engineers focus on maximizing that ROI through technical innovation. And when GTM engineers identify successful experiments, they work with RevOps to roll them out to the entire organization.

For example:

• RevOps might suggest increasing ad spend on Google because they mined as an insight from their attribution model.

• GTM Engineers work with RevOps and Demand Gen to implement server-side tracking enriched with firmographic data and feed that data back to Google to improve Performance Max campaign results, therefore, achieving higher conversion rates and more pipeline.

Let’s see some more aspects where these two compliment each other:

If nothing else, GTM engineers are probably the wingman RevOps has been searching for.

4) GTM Engineering isn’t just about new business and outbound

The common misconception is that GTM Engineering primarily serves top-of-funnel activities — building outbound automation, enriching lead data, or creating target account lists. And while a lot of GTM engineers and agencies made the movement famous with list building and personalized outbound, reducing them to these tasks misses their potential.

Think of a GTM Engineer building an intelligent early warning system for customer success.

• Rather than just tracking basic usage metrics, this system combines multiple signals: product engagement patterns, job changes across multiple stakeholders — like when champions leave or new executives join — sentiment indicators like NPS scores, support ticket tone; and renewal context.

• When the system spots potential churn patterns — such as a champion departing followed by declining feature usage — it automatically triggers the right messages to the right stakeholders on the right channels.

• This proactive approach helps teams prevent churn before the obvious signs appear, turning potential risks into retention opportunities.

GTM engineers don't just support existing processes — they reimagine what's possible through the lenses of the new AI tooling available today.

Good GTM engineers ask: "How do we automate this manual process?" But the best GTM engineers ask: "How can we use AI and tech to give our GTM teams superhuman capabilities?"

Again, consider these other use cases way beyond just list building and outbound:

• Automating win/loss case analysis from customer calls to refine ICP, messaging, and trickle those updates into multi-channel campaigns.

• Programmatically score and route prospects into tailored nurturing journeys with AI without SDRs doing manually allocation and CRM hygiene.

• Create an automated workflow to turn customer calls insights into long-form content that can be repurposed across blog, LinkedIn organic, and retargeting ads.

These use cases show the potential for scaling GTM efficiency across teams, and stresses how GTM engineers build the technical bridge between strategy and efficient execution — and not just for generating new business, but also expansion!

Most importantly, this approach creates a compounding, ripple effect across the organization:

• Sales teams ask "Could we build something to predict which deals need attention?"

• Marketers think "How can we automatically personalize campaigns based on intent data?"

• CSMs wonder "What if we could predict churn before it happens?"

It's this cultural transformation that empowers every GTM team to think creatively and consider technically-enabled capabilities to achieve specific outcomes that makes GTM Engineering truly powerful.

5) GTM Engineering isn’t just about technical execution

While most of today's LinkedIn discourse suggests GTM Engineering success comes from simply connecting tools and writing code, once again, this view dangerously oversimplifies the role.

GTM Engineers are, yes, technical strategists who build integrated systems across the entire revenue organization, but they’re not like “developers taking requirements” from business teams. They translate business context into technical architecture while orchestrating solutions across teams — making the role both challenging and critical for scale.

Technical skills are crucial, but they're just one piece of the puzzle. The reality? GTM Engineering requires three-dimensional thinking.

1. Business understanding. Instead of just automating a process because "we can," top GTM engineers go deeper, and probe things like:

   • Why does this matter to revenue?

   • How does this scale with our existing GTM motion? (eg. given our PLG or SLG)

   • What are the short- and long-term impact of this on adjacent teams?

2. Systems thinking. Rather than building isolated solutions, they consider:

   • How do marketing, sales, and product workflows intersect?

   • How does this affect the customer journey, experience, and buying process?

   • Where are the data inputs, outputs, and handoff points?

   • What breaks if we scale 10x?

3. Technical excellence. Beyond just making things work, GTM Engineers build for resilience:

   • How can we intentionally vet, test, and integrate new GTM tools?

   • How do we plan for gracefully retiring them if they no longer fit?

   • What happens when data quality from different vendors degrades?

   • What metrics or logs can detect performance issues, data synchronization failures, or API outages in real time—and who gets alerted

So, what does GTM Engineering mean then?

Taking all into consideration, here’s our best definition of a GTM Engineering.

GTM Engineering blends technical expertise — waterfall enrichment, AI workflows, prompt engineering, and custom APIs connections — with system thinking and business understanding across the entire funnel. Based on this skillset, GTM Engineers build systems that open up new, creative ways of scaling business outcomes and GTM efficiency without the scale headcount proportionately.

This definition highlights how GTM Engineering isn't a one-size-fits-all toolkit you can simply plug in and play — it requires careful customization to your organization's GTM motion, product complexity, and team setup.

If you look at Unify and Common Room, you’ll see playbooks are already becoming productized and will soon be commoditized. The real value comes from using these unique skills to unlock creative ways that give your company unique advantages in how you engage and grow your market.

In the next post of this series, we’ll dig deeper into the lifecycle of GTM Engineering, and reveal our framework to execute on these problems.

The underlying principle is that Google rewards any experience that allows users to "do the thing" instead of "learning about how to do the thing".

By shifting from text-based content to interactive utilities like calculators, generators, and quizzes, growth teams can deliver real value, capture high-intent traffic, and guide prospects down the funnel more efficiently.

We’re calling this use-case-led SEO — an approach that aligns product marketing insights with the rigor of SEO (keyword) research, helping teams identify the right use cases, measure ROI by projected search volume, and adopt a fail-fast experimentation mindset.

• Use-case-led SEO can work as both lead generation and brand differentiation asset in the age of AI and LLMs. As AI chatbots and agents continue to advance and provide accurate answers to a vast array of questions, these tools can maintain, and even grow, their value by offering users a direct path to solutions for their problems, incentivizing them to visit your website with superior UX/UI for problem resolution and higher chance to stand out from others and become top-of-mind.

• Furthermore, use-case-led SEO tools have the potential to evolve into AI agents themselves. This transformation could take various forms, such as integrating with AI chatbots to offer personalized product recommendations or using AI to analyze user behavior and tailor lead-generation strategies accordingly. By embracing these advancements, these tools can become important PLG or prospecting entry points, even standalone product wedges with their own monetization streams. Think of an AI content generator with a free or paid tier that can funnel prospects into the core SaaS enterprise offering.

Equipped with no-code solutions and backed by cross-functional resources — such as engineering, product, and design — organizations can swiftly develop, launch, and iterate on these lead gen tools.

In this post, we’ll see how companies like Ramp, AirOps, and more showcase their use-case-led SEO, and how you can do it too!

By combining SEO, product thinking, and product marketing depth, growth teams can create a sustainable edge and transform organic channels into scalable revenue engines.

1. Understanding the shift from static SEO content to interactive solutions

One of the most significant changes in modern SEO strategy is the move away from static, keyword-driven content toward interactive, problem-solving solutions that go far beyond a typical blog post or landing page.

Rather than simply answering questions with passive text, well-crafted lead gen tools actively guide users toward solving a tangible challenge — like calculating budgets, automating data workflows, or creating personalized strategies.

This approach not only captures higher-intent traffic and engagement as per the latest Google algo changes but also significantly increases conversions by offering direct value from the moment a user lands on your site.

When planning these interactive solutions, it’s crucial to align keyword research with authentic user needs. Instead of focusing solely on high-volume search phrases, you can tap into product marketing insights to get deeper into customer pain points that arise in real-life contexts.

As discussed in Kevin’s latest Growth Memo on product marketing and SEO, you can use AI to mine any of these sources for these insights:

• Customer calls and support ticket transcripts: Front-line teams regularly interact with customers and can offer valuable insights into common questions, concerns, and frustrations. You can use ChatGPT or Humata to mine customer calls at scale and extract structured insights.

• G2 reviews: Customer reviews can also provide insights into product strengths, weaknesses, areas for improvement, and even competitive alternatives. Scraping these websites and related insights can also be a goldmine of insight for use-case-led SEO.

• Focus groups & surveys: Moderated discussions with a group of customers as well as targeted surveys, can gather quantitative and qualitative data on customer satisfaction, preferences, and needs, revealing shared pain points and opinions.

This customer-centric approach ensures that marketing efforts are aligned with genuine user needs, leading to a more prioritized and focused use-case-led SEO roadmap.

For example, if you find that “financial forecasting software” is a popular keyword in your niche, don’t stop at a generic blog post — create a tailored financial calculator or an interactive budget planner that walks prospects through the process.

Another key benefit of these immersive use-case-led SEO experiences is their potential to shift users immediately into a demo or a live product environment. Whether you’re showcasing a snippet of your SaaS platform or offering a limited hands-on trial, that seamless transition accelerates the buying journey by letting visitors “try before they buy.”

The result? More meaningful conversions and a sustainable, high-value acquisition channel that builds both authority and pipeline in the long run.

2. Sustainable differentiation and long-term growth

With use-case-led SEO, your content doesn’t just directly solve your audience problem, but it does so in a bespoke, interactive, and branded way. Classic SEO articles can be easily copied by competitors, but custom, interactive solutions are far more complex to duplicate.

By investing in building these specialized tools, you create a competitive moat that not only sets your brand differentiation apart but also sustains long-term growth.

Moreover, once the process of crafting effective lead gen tools is mastered, the opportunity to expand into a suite of such tools emerges.

• Each new asset can target a different stage of the customer journey, reinforcing your brand’s authority, enhancing user satisfaction, and contributing to a compounding effect on organic growth over time.

• This approach transforms static content strategies into dynamic and compounding ecosystems that continuously attract and engage your ideal customer profile from multiple angles — or different roles (eg. buyers and champions) and segments (eg. SMBs and mid-market) of your SOM in tandem.

• Scaling across tool types further amplifies this effect. Take Shopify as an example: they maintain a flagship business name generator landing page while also creating individual pages for long-tail keywords like “candle business name generator” or “t-shirt business name generator.”

• This tactic not only taps into niche search volumes but also demonstrates a deep understanding of varied customer needs, reinforcing brand authority across multiple segments.

By blending product marketing with advanced SEO tactics through free, use-case-led SEO tools, companies can build sustainable growth engines that are difficult for competitors to replicate and that generate lasting organic value.

3. Integrating examples and proven tactics

Let’s look at examples of use-case-led SEO from companies like Ramp, AirOps, Deepgram, Shopify, Riverside FM, and Storylane.

Ramp’s free growth tools

Ramp’s suite of free calculators and growth tools are a prime example of use-case-led SEO. They include a set of:

• Calculators — per diem, burn rate, reimbursements, etc.

• Generators — expense policy templates, mission statement

• Databases, directories, and libraries — vendor search, accounting docs

  

They even segmented the tools by a mix of customer segments — eg. Investors, procurement, accounting, and founders.

Specifically, their mission statement generator has been continuously capturing high-intent traffic since its inception. These interactive resources not only drive organic visibility — by being extremely SEO optimized pages complete with keywords, FAQs, examples, and product marketing CTA doors, but also build trust in the brand by providing tangible customer value from the outset.

AirOps AI strategy generator

Similarly, AirOps’ AI strategy generator goes beyond static text, offering a custom-tailored plan that resonates with users’ unique businesses based on their website and a full-blown, automated SEO research of adjacent players and competitors.

This input-based, use-case-led approach differentiates it from standard SEO how-to content, turning visitors into engaged prospects by solving specific problems in real time — and with clear entry points into nurturing and prospecting.

Deepgram AI tools database

Deepgram takes this a step further with its AI-generated database of thousands of AI tools — an industry-leading resource that attracts users actively exploring new SaaS solutions, thereby positioning Deepgram as an authoritative brand in this rapidly evolving market.

Shopify’s business name generator

Another example we love is Shopify’s business name generator, which captures niche search queries, reinforcing brand visibility while providing immediate utility.

As mention

Riverside recommended tools

Riverside FM curates an extensive tool collection that serves creators with tailored resources, enhancing user experience and brand loyalty — and funneling straight into their product signup form.

Storylane interactive demos

Another great example is Storylane — a fast-growing startup that has taken use-case-led SEO by storm and scaled it (they called it demo-led SEO, but hey!).

• They created 100 pages for use-case queries like “How to understand if your influencer's audience is the right fit? (using LinkedIn)” that cover “how to do X with [tool]?” — think Ahrefs, Mixpanel, Canva, etc. — working back from real ICP problems and insights aligned with keyword opportunities.

• They’ve done so by minimizing the amount of text on each landing page and putting their product front-and-center — it helps that their product is about creating (product) demos in 10 minutes! — making the content experience highly interactive.

• They manage to go from 700 to 70,000 monthly traffic and outranked all support articles from the actual domains (yes, they’ve outranked Canva, Slack, Ahrefs, and all the pages they created these demos for!) — proving once again that SEO can work really well with (almost) no text!

  

Together, these examples illustrate that by offering free, interactive tools, you can achieve sustainable and organic lead gen while also differentiating your brand!

A cautionary tale — keep in mind that initial spikes in results are not the end goal. In fact, it's quite the opposite. Without consistent effort and ongoing optimization, those initial gains can quickly disappear.

It's like a plant that thrives after the first rain but withers and dies without consistent watering and care.

4. Adopting a multifaceted mindset that blends product, marketing, and SEO

Now, let’s get into the execution part of use-case-led SEO. Creating effective use-case-led SEO demands a cross-disciplinary mindset that fuses SEO expertise, product marketing insights, and good UX/UI.

• Product marketers mine opportunities from customer research and optimize for high intent and recurrence in customer touchpoints.

• SEO experts ensure that these tools correspond to high-traffic opportunities and rank high in search results

• PMs and designers focus on making UX/UI and signup onboarding as seamless as possible.

Quantifying the return on investment for such tools is as crucial as their creation. Similar to how SEO data informs content strategy, marketers can leverage search volume, recurrence of use-case in customer calls, and analysis of competitive gaps to estimate a tool’s potential impact.

This methodical evaluation ensures that resources are allocated to projects likely to yield substantial organic visibility and sustainable growth, turning insights into actions that drive both short-term wins and long-term brand differentiation.

5. Strategies and resources for building effective SEO mini-products

Choosing the right tool format begins with aligning your solution to the user’s core needs and pain points.

• Calculators: sometimes, a simple calculator is ideal for providing actionable answers — think cost estimators or ROI calculators. This can start off with scrappy Excel sheets on the back-end that get embedded into a more dynamic iFrame of a branded landing page with a few tools mentioned below.

• Generators: In other cases, a generator that creates customized outputs adds more value, such as name generators or personalized strategy builders. In most cases, these are GPT wrappers — think customGPTs — trained for specific use cases and parameters.

• Quizzes & Chatbots: Meanwhile, a quiz can deliver an engaging, interactive experience that invites users to learn about themselves or their business while connecting them directly to your product offering. This can be a form or also a customGPT guiding the user towards specific paths and decision trees.

• Databases, directories, and libraries: even dynamic CMS collections of tools, templates, services providers and similar can have real impact on users. These collections effectively build a bridge for customers from their how-to query to concrete solutions and details or recommendations on which one is best for which use case.

Once you’ve decided on the tool type, leverage no-code solutions for rapid development. We recommend looking into software like the below to make it easy to spin up prototypes quickly.

• Cursor AI: An AI-powered code editor that helps developers in writing, editing, and debugging code — better for your engineering team.

• 0dev: A platform that allows developers to build and deploy full-stack web applications quickly.

• Outgrow: A no-code tool for creating interactive content like quizzes, calculators, and assessments.

• Stackblitz: A new way to build and deploy web applications with natural language.

• Softr: A no-code platform for building web applications and internal tools without code, using Airtable as a backend.

• Bubble: Another great Softr alternative to build full-stack applications with no code.

• Ucalc: A service for creating and embedding advanced calculators and forms on websites.

Keep in mind that turning your tool into a genuine pipeline driver may involve gating tactics. There are two ways of going about this:

• Hard-gate: you could require email sign-up before any interaction

• Soft-gate: you can allow some usage, then asking for email before revealing the results.

Which approach should you go for? The more you have a unique offer, the more you can hard gate. However, most companies should start with soft-gating in order to spread word-of-mouth and virality, then experiment with progressively harder gating.

Putting all this into practice demands a lean growth squad approach. Assemble a small, agile team that includes an engineer, a product manager, and a designer. Move fast, rely on no-code platforms to iterate, and concentrate on deploying a minimum viable version to gather real-world data.

Throughout this process, embracing experimentation is crucial: treat each lead gen tool as a growth test. Launch, measure, and be prepared to pivot based on user behavior.

Failure in one iteration simply provides the insight to make the next version even more effective, guiding you toward a sustainable, high-converting organic acquisition channel.

Closing thoughts

By blending product marketing acumen with SEO rigor, and by leveraging agile development strategies, use-case-led SEO can turn a static website into a high-converting interactive content ecosystem powered by valuable, differentiated lead gen tools.

Instead of simply chasing keywords, focus on delivering meaningful experiences that resonate with prospects and customers alike. As you scale this approach, you not only strengthen your brand’s presence in organic search but also build a sustainable foundation for long-term revenue growth.

Gen AI and Gen UI are driving product commoditization faster than ever, making SaaS websites and apps virtually indistinguishable. If you add that saturated, costly acquisition channels have eroded traditional differentiation, the real challenge becomes how to break through the mold and build a distinctive brand.

One answer lies in embracing community-led growth, a strategy that moves beyond one-size-fits-all marketing to harness the power of authentic user relationships and third-party endorsements.

By creating a vibrant community around your product, you foster deep connections, cultivate loyalty, and earn trust from users who become your most passionate advocates. It’s easier said than done, but what it means is that instead of competing for attention in crowded channels, you stand apart through genuine engagement, meaningful dialogues, and long-term value exchange — ultimately establishing a brand identity that thrives on collaboration and shared purpose.

In this post, we’ll dive deeper into community-led growth, frameworks to think through your community strategy, and execute on it with top-tier, real-world examples from leaders like GitHub, SaaStr, Clay, Strapi, and more.

1. Understanding Community-Led Growth

By shaping a go-to-market strategy that places a passionate network of users at its center, community-led growth aligns a company's goals with the needs of its members, creating a self-sustaining flywheel where every participant is motivated to contribute and collaborate.

Imagine a new member joining your community, immediately feeling welcomed, and soon finding ways to exchange value — whether by sharing insights, leveraging new templates and integrations, or offering support.

This steady stream of member-driven content and resources naturally attracts fresh participants, further strengthening the entire ecosystem. To understand this, we can reference the CAMPER framework by Lloyed Lobo, which he used to bootstrap his startup boast.ai to $10M ARR.

• Connection: Build strong relationships through shared values and goals.

• Autonomy: Empower members with control over their contributions and growth.

• Mastery: Foster learning and skill development.

• Purpose: Align the community with a clear mission and vision.

• Energy: Infuse enthusiasm into every interaction.

• Recognition: Celebrate member efforts and successes.

Another similar framework to understand community is Late Checkout’s founder Greg Isenber TRIBE:

• Togetherness: How (often) are people coming together?

• Rituals: What are people coming here to do? Are there recurring things they do?

• Identity: How do people identify in the community? (eg. name, image, aesthetics)

• Belonging: What are people's shared goals, interests, beliefs, and status?

• Engagement: How do people engage with each other?

As Greg mentions:

“The difference between an audience and a community is which way the chairs are facing”

What Greg means is that, unlike audience-focused social channels where you broadcast content one-way via platforms like Twitter, or Substack, a true community thrives on two-way dialogue, making tools like Discord, Slack, WhatsApp, Circle, or Telegram better suited for active engagement and networking.

2. Why Community now?

In an era where growth hinges more on authentic relationships rather than brute-force growth tactics, investing in community-led strategies has never been more important.

As we return to more and more in-person work environments and seek IRL human contact, communities that foster shared identities and enable strong emotional ties can evolve into brands people genuinely care about.

Additionally, as AI and bot activity is emerging in content and search engines, people are hinging more and more towards peer-to-peer feedback — they seek human thought and conversation.

Especially if you’re targeting younger, Gen Z audiences value authenticity and peer insights over traditional review platforms — engaging them through community, bottom-up narratives resonate far better than stale, top-down ones.

And regardless of demographics, with innovation moving at a breakneck pace, members increasingly look to communities of peers for education, mentoring, and skill-building that keep them ahead of the curve.

Communities are not just about targeting and branding, but also about business impact. Well-executed and managed communities can massively increase brand awareness and deliver higher conversion rates at lower CACs, relying on member enthusiasm rather than expensive paid channels. And unlike AI-generated content and advertising, communities built on deep trust and active participation become incredibly harder to replicate, granting you a lasting moat that nurtures retention and compounds back into acquisition.

Harnessing collective knowledge from members doesn’t just contribute to growth, but can also offset support, R&D, and hiring costs, transforming your community into an engine of growth, feedback, and an extended workforce of product advocates.

3. Community Types

Communities aren’t one-size-fits-all; they can embody different forms to meet different goals. When thinking through your community-led growth strategy, consider how each type can fit into your go-to-market strategy:

• Community of product: Centers around a specific product, where members share insights, solve issues, and offer feedback. By enabling community-led support, content creation and product insights, they continuously differentiate their products and brands, while building a strong acquisition and activation muscle.This approach not only reduces support costs but also builds trust among users, just like Strapi, Clay, Webflow, Salesforce Trailblazers or GitHub’s community.

• Community as the core product: Platforms like Circle, Discord, Skool, Stack Overflow, or Dev.to position community at the heart of their value proposition, enabling users to co-create the product experience through active participation and peer-to-peer knowledge sharing.

• Community of practice: Focuses on shared professions or skills, helping members advance their expertise through collective learning and knowledge exchange. Established references of this category is Reforge for growth and product, whereas a more emerging one is Cargo’s GTM Engineering community.

• Community of interest: Revolves around shared passions or hobbies, allowing users to connect and learn organically. These communities foster brand trust and attract like-minded customers — think of any subreddit for the most niche or disparate topics.

As we’ve seen in these examples, communities aren’t just for one type of company; they bring value across various sectors by forging trust, sparking dialogue, and encouraging members to invest emotionally in a brand’s journey.

• Developer tools: Enable developers to share expertise, solve complex problems together, and gain recognition for their work, making your product ecosystem more appealing and resource-rich.

• SaaS & Enterprise software: Scale user onboarding and training efficiently through peer support, expert Q&A sessions, and user-generated tutorials, reducing reliance on high-touch sales and support channels.

• Consumer products with cult followings: Cultivate brand advocates who share testimonials, tips, and inspiration, much like dedicated communities around Peloton or Duolingo, amplifying word-of-mouth growth.

• Crypto & Web3: Nurture trust, transparency, and active participation by involving members in governance and decision-making, driving engagement and stable token economies.

• Creator economy & education platforms: Empower creators, learners, and mentors to network, collaborate, and showcase achievements, keeping audiences engaged longer and encouraging sustainable growth for your platform.

Now that we’ve covered the basics of community-led growth, we can move onto the strategy and execution part of the playbook.

4. Setting clear Community goals

To build a thriving community that truly drives growth, start by setting clear goals. In the context of community, that means guiding members from low affinity to high affinity, gradually building trust and commitment over time.

Consider trust as a rechargeable “battery” that fills slowly with every positive interaction and can deplete quickly when expectations aren’t met. Inspired by community practices at Shopify, the battery framework highlights the importance of nurturing credibility and reliability.

To get started with your community goals, you have to also define a minimum viable community that articulates who you’re bringing together, what support or knowledge you’re offering, and why they should care.

Strengthen this foundation by crafting a powerful manifesto that speaks to your community’s purpose, its overarching vision, and a compelling backstory that resonates emotionally. By aligning these elements, you create an environment where members feel genuinely welcomed and intrinsically motivated to engage, support each other, and ultimately help drive sustainable growth.

• Who: Identify your target members, such as entrepreneurs or developers, so you can tailor content and discussions to their needs.

• What: Define what the community offers — support, knowledge sharing, or mentorship — so members know exactly what to expect.

• Why: Articulate the benefits of joining, like accessing resources, networking, or even influence within the industry.

Once these basics are established, this guiding narrative will not only inspire potential members but will also provide existing ones with a sense of purpose and belonging, setting the stage for long-term engagement and sustainable growth.

5. Choosing your community platform

Once you have the foundations laid out, the big next steps of your community strategy is about selecting the right platform to host your community.

Doing so effectively means understanding your audience’s needs, familiar stack and UIs, communication styles, and desired level of interaction. For example, if you’re targeting enterprise software business users, they might be more used to MS Teams as a chat platform and rely on multiple Zoom meetings for sync interaction. On the other hand, if your community is made of indie developers, they might prefer more async comms styles like Slack, Discord, or Reddit to share info and keep in touch with each other.

Before committing to any platform, work back from your community MVP outlined above:

• Member needs: Determine if members require support forums, content libraries, peer networking, or event hosting.

• Engagement frequency: Consider how often your community engages and pick a platform that accommodates their rhythm.

• Affinity spectrum: Factor in the level of affinity you want to build within your community, and work back which medium of communications is best for that.

• Synchronicity preferences: Decide between real-time conversations or asynchronous threads to match member schedules.

• Openness: Also think about openness — do you envision a public forum or a private, invite-only group? Pick your balance of accessibility, privacy, and exclusivity.

• Cost: Factor in platform pricing models to align with your budget without compromising member experience.

By carefully evaluating these elements, you ensure that your chosen community platform not only resonates with your members but also fosters meaningful connections that drive sustainable growth. Also, your community could run on multiple platforms — you could have a Discord or Slack for synchronous communication, a Discourse Forum for async knowledge transfer, and a public GitHub repository for actual product feedback and contribution — all running in parallel.

Just make sure each has a different purpose or serves a specific segment so you don’t dilute your growth loops and increase operational complexity too much.

6. Building community engagement

Community Engagement Cycle

Crafting a thriving community means not only attracting members but also guiding them through a journey of increasing involvement and value creation. Think of your community-led growth strategy like a growth loop, or as CMX defined it, a community engagement cycle. This is another great framework that helps to break down and execute on a community-led growth strategy.

1. Define community identity: help users find their role, identity and sense of belonging within their community. Tactics like introducing themselves publicly, setting their handles, PFPs (profile pictures), recommending specific channels based on interest, language or geography, use shared vocabulary, or using role tags are great for establishing strong community identities.

2. Earn community trust: once your members know their role in the community, you need to build trust with them quickly to avoid churn. To do so, ensure you have a strong yet simple onboarding, and provide free upfront value to hook them in.

3. Encourage participation: after building trust, you have to keep your members engaged and expand their level of engagement within their community. Use rituals, games, challenges, hackathon or quests — and hint the promise of rewards — to get them to action.

4. Give recurring rewards: blend a mix of social, financial, knowledge, time, mastery, or joy incentives as rewards for their participation. This reward is the basis for strengthening their community role and identity, and going through the loop once more.

As with every framework, this is just a simplification of reality to guide your strategy and execution. Where in real life people move fluidly along this journey as their interests and schedules, it’s important for your team to think through each engagement state deeply.

By thoughtfully orchestrating every phase — from initial onboarding to advanced contributions—you create a sustainable cycle of growth, loyalty, and shared success for your community.

The Commitment Curve

Designing a community engagement model involves defining clear participation and commitment stages and guiding members through an ever-growing and rewarding journey that encourages lasting involvement. David Spink’s Commitment Curve is a great model that helps articulate each level’s commitments and related engagement states to give participants a transparent roadmap for deepening their connection.

As mentioned before though, flexibility is key — your members should feel free to explore and advance at their own pace, reflecting the natural ebb and flow of their interests and schedules.

• Clearly define stages: Outline distinct engagement levels and package them as “roles” or “milestone” so members know what each entails, and get them.

• Set commitments and rewards: Communicate what members gain at every stage, from exclusive content to recognition.

• Ensure flexible movement: Allow individuals to move forward or step back as their priorities shift.

• Incorporate rituals and challenges: Add structured activities that lend meaning, excitement, and purpose to each level of involvement.

• Attach custom roles and badges: Celebrate achievements and give members a sense of status and identity within the community.

• Keep a visible leaderboard: Display roles and progress through a directory, leaderboard or profile indicators, providing recognition and motivating members to learn, advance, and inspire others.

7. Combining the two engagement models

You can interlace the Community Engagement Cycle and the Commitment Curve models together to chart your community programming and define each key component — from identity to reward.

Let’s take Clay creator program as an example.

Bootstrapping engagement — Community Onboarding

Design a clear onboarding sequence that welcomes them, sets expectations for culture and purpose, encourages personal introductions, and points them to valuable resources. This approach is key to bootstrap initial engagement and avoid churn — one of the biggest risks in community-led growth models.

Make sure to check these use cases when onboarding your new members:

• Welcome message: Gree them immediately upon joining.

• Set expectations & resources: Share expected member goals, next steps, and useful links

• Member intros: Encourage people to connect, sharing their stories or goals.

• Engagement incentive & recognition: Recognize supportive individuals and give people a strong incentive to help each other.

• Resources: Guide members to valuable content and prompt participation.

  

Expanding engagement — Community Programming

Once the foundational initiation is laid out, maintain momentum with a well-structured community programming and calendar that keeps members engaged at a steady rhythm.

The goal is to use your community programming to guide members through increasing levels of engagement and complexity. Work back your programming by helping newcomers reach their “community aha moment,” then gradually introduce more frequent interactions and higher-stakes activities.

For example, as we saw above with Clay’s example, a member might progress from reading a weekly newsletter to sending daily messages on Slack, to creating and sharing content, attending a meetup, or eventually organizing an event themselves.

As you chart your community programming, you can use these strategies to expanding your member engagement:

1. Use case transition: use incentives to transition members to progressively more complex use cases. For example, from reading your manifesto, to attending an AMA, or create a LinkedIn post.

2. Increase frequency: expand engagement by transitioning members to progressively more frequent use cases. For example, from reading a weekly newsletter to sending a message in the community on a daily basis.

3. Increase intensity: encourage members to progressively higher effort/rewards use cases, like creating a X post, which is short and sweet, to a LinkedIn post, to a long-form playbook. Or from attending a webinar, to an IRL meetup, to eventually organizing a meetup themselves.

To bring the above strategies to life you can chart a community programming so that your members keep engaged and are always looking forward to the next interaction.

Having a recurring program sets a rhythm, helping members plan their commitment and deepen their relationships. By mixing online and offline touchpoints, you encourage natural conversations, meaningful connections, and a sense of belonging that transcends the screen.

Here are some of the tactics in your kit to consider.

• Stable foundations: Ensure everyone feels safe by establishing clear community rules, setting expectations, and providing examples of good and great behavior. Define specific contribution roles — who plans events, who moderates discussions, who runs weekly sessions — and support these roles with recognition. Ensure moderators engage at least once a day, and consider hiring a community manager if needed to moderate content and guide member interactions. By doing this, you build a strong cultural backbone that encourages trust, motivates learning, and ensures your community thrives over the long term.

• Events and prompts: Consider adding regular highlights like weekly AMAs, recurring Q&As, or recurring prompts that inspire show-and-tell moments. Offer daily technique classes, run monthly masterclasses, or organize annual retreats, and even parties or concerts. A variety of gatherings — whether that’s to gather product feedback or give back value to your members — keeps them curious and engaged.

• Personal connections: Foster introductions and networking by incentivizing members to meet over Zoom or in person at IRL meetups. Maintain a visible community directory with basic info, tags, backgrounds, and goals so members can easily discover shared interests. Promote member spotlights, challenges, and quests, then congratulate and reward positive behaviors and contributions to reinforce community values.

• Automated engagement comms: set up helpful reminders — notifications, emails, digests, and social alerts — to keep people informed and motivated without overwhelming them.

8. Tactical examples of effective community-led growth

When you create transparent communities, you foster a sense of ownership that leads to sustained engagement, loyalty, and advocacy. Here are some of our favourite real-world examples that bring community-led growth to life.

Building trust and safety — Strapi

Strapi’s public roadmap lets users track and influence product direction, while its handbook offers a window into internal operations. By using RFCs on GitHub for community feedback, Strapi builds trust and shows that member input matters. They also foster transparency through forums, AMAs, and regular updates that keep members informed and valued.

Empowering members through autonomy — Airbyte

Airbyte indexes Slack discussions on public forums, boosting its SEO with user-generated content and compounding an additional growth loop on top of their community.

This approach not only amplifies brand discoverability but also harnesses member expertise to enrich the community’s knowledge base. It ultimately allows members to take ownership by enabling them to contribute code, create content, and organize events.

Cultivating a learning ecosystem — GitHub

GitHub connects developers through its Community Exchange, encourages leadership via the Campus Experts program, and supports skill-building with training resources, workshops, and office hours. Badges and prizes reward engagement, further fueling collaborative learning.

With their learning ecosystem, GitHub makes mentorship, training, and continuous learning part of their community’s DNA. Recognize achievements to motivate members and reinforce a growth mindset.

Recognizing and rewarding champions — Docker

Docker turns highly active members into brand ambassadors called Captains by providing tailored rewards and VIP opportunities, organically increasing loyalty and advocacy.

Understanding that a small group often drives most community activity, they offer personalized rewards, exclusive experiences, and unexpected gestures to show appreciation and keep VIP members inspired.

By applying these tactics — establishing trust, granting autonomy, encouraging learning, and celebrating top contributors — you create a thriving community that grows stronger over time. The result is not just user satisfaction but also a sustainable engine for product feedback, brand advocacy, and market differentiation.

9. Measuring Community-led growth

Measuring and validating the success of your community initiatives is about more than just counting members — it’s about understanding their engagement, trust, and willingness to contribute over time.

Focus first on right-sizing your audience, aim to grow it to 25,000 to 100,000 members, then closely monitor growth trends and spikes in interaction.

Assess participation quality through member interviews and community surveys, gauge affinity and engagement levels by tracking the shift from low to high engagement states, and keep a close eye on trust by observing how often members interact and rely on one another for support.

Ultimately, the goal is to create a dynamic environment where members move naturally from initial curiosity to enthusiastic participation, validating their journey through clear metrics like conversion, retention, and growth in both influence and activity.

Closing thoughts

In an era where products are commoditized and acquisition channels are crowded, a vibrant community becomes a powerful differentiator.

It’s not just another marketing channel — it’s a living ecosystem that thrives on member input, feedback loops, and collective learning. By anchoring your growth strategy in community building, you earn lasting loyalty, higher-quality feedback, and a support network that drives sustainable success.

Getting started doesn’t have to be complicated. Here are five steps to recap this post and get you going with confidence.

1. Understand where: Begin by pinpointing where potential members already gather, then immerse yourself in those spaces to understand the culture, motivators, and pain points.

2. Meet leaders: Engage with the most involved individuals to learn the community’s unwritten rules, goals, and preferences.

3. Choose the right fit: With these insights, select the best community type and platform aligned with your business model, industry, and company stage

4. Prioritize initiatives: Focus on programs that align with member interests and help you achieve your business targets — whether product feedback and contributions, brand awareness through content creation, or events, and overall user support and retention

5. Measure success: Track a north star metric to validate the impact of your community-led growth efforts.

By investing in community-led growth and adopting a thoughtful, metrics-driven approach, you not only deepen relationships with your audience but also empower them to play an active role in shaping your brand’s future.

The result is a resilient, trusted, and ever-evolving community that consistently delivers value, boosts your market presence, and sets you apart in a highly competitive landscape.

The Go-To-Market landscape is at an inflection point. Inboxes and social feeds are overwhelmed with low-quality automated outreach and AI-generated content, making it harder than ever to send ‘the right message to the right person at the right time’, or just stand out on any distribution channel. 

Simultaneously, products have never been easier to build thanks to no-code and AI, flooding virtually every niche with like-for-like competitors with much faster time-to-market, making differentiation harder and increasing overall market risk.

In B2B SaaS land, companies like Ramp, Rippling, Gorgias, and Samsara broke new records with their growth trajectories by leveraging innovative growth strategies. They were early adopters of automated outbound, personalization at scale, and the use of signals and intent data — long before these became ‘democratized’ with the likes of Clay and RB2B. 

However, as these tactics become commonplace, the competitive advantage they once offered has diminished. Volume-based outreach has led to crowded inboxes, desensitized prospects, and stricter spam controls, making it increasingly difficult to stand out.

So how can the next generation of startups learn from the likes of Ramp and effectively break through the noise and drive sustainable growth?

This new breed of challenges demands a new breed of talent — with a different mindset, set of skills, and tooling — the Go-To-Market (GTM) engineer. This post is about why, what great ones look like, and how you can become one yourself (if you wish to!).

The case for GTM engineers

For us at Hypergrowth Partners, we’ve been making the case for GTM engineers for years already — we’ve been evangelizing this growth mindset in growth teams of the likes of Ramp, Gorgias, Vercel, Drift, and more.

The case for GTM engineers is clear to us — however, the AI revolution is making it even more important. Let’s unpack some of the key root causes.

ZIRP, tech layoffs, and AI

In today's hyper-competitive market, every product category is saturated. Even groundbreaking innovations like OpenAI face numerous competitors such as Anthropic, LLama, Qwen, DeepSeek, Cohere, and Grok. The post-ZIRP (Zero Interest Rate Policy) era, coupled with widespread tech layoffs and the rise of AI, is intensifying pressures on productivity, efficiency, and revenue per employee. 

Companies are being pushed to "do more with less," necessitating rapid execution and adaptability. In this market, only the teams that embrace this tempo and productivity demanded by the market will stay ahead of the curve. Otherwise, you’ll drown into a sea of undifferentiated, like-for-like average players; or simply go bust.

GTM SaaS-ification

The explosion of GTM SaaS — Marketing, Sales etc. — is productizing entire job roles through vertical and or horizontal SaaS tools.

For example, carrying out lead generation and qualification operations once required teams of SDRs that would manually create lists of potential customers, calling them one by one to verify potential fit, and then pass them onto the sales team to start the sales cycle. Now, you can use various intelligence platforms to collect signals within and outside your properties, and predictably score and qualify them — and automatically reach out to them on the right channels with a hyper-tailored messaging. All without lifting a finger!

The same is slowly happening in content marketing, SEO, performance marketing, sales, PLG, and more, with specialized tools and AIs that are enabling better outcomes with much less resources. Examples include:

• Content: byword.ai, AirOps, EasyGen, Writesonic, Engyne, Synthesia, Ideogram, HeyGen

• Outbound: Clay, Instantly, Smartlead, Unify, Lemlist, LaGrowthMachine, Sendspark, Apollo, Koala, Salesloft, Letterdrop

• TAM/List building: Keyplay, Ocean, Common Room

• Enrichment: Fullenrich, Common Room, Waterfall, BuiltWith, Zoominfo, Clearbit, Cognism

• Signals: Common Room, Waterfall, Clay, RB2B, Madkudu, Pocus

• Research: Tome, Poggio, Endgame

• Product Marketing: Octave, Humata, Relume, Gamma

• Website: Mutiny, Userled

• SEO: Ahrefs, SERanking

• Paid marketing: DemandBase, NRich, Smartly, Metadata, Primer, RollWorks

• Sales: Orum, Gong, Nooks, Attention

• Training: Hyperbound, Blue

• Revenue Orchestration: Cargo, Warmly

• Lifecycle: Userflow, Customer.io, Crisp, Intercom, Refiner

In the most extreme scenarios, we’re seeing solopreneurs who can setup, maintain, and even scale multiple go-to-market workstreams — content, outbound, and SEO — by just leveraging a set of AI-powered tools.

This is putting further pressure on building knowledge of the latest tools on the market to carry out complex outcomes with small, lean teams.

Operationalization

Operationalization has become another byproduct of this shift. Every GTM function now has its own "ops team" — marketing ops, sales ops, dev ops, product ops. These teams are quickly gaining more and more popularity in tech startups, and for good reason. They’re:

• Both technical and business savvy: they combine understanding of the customer journey, ICP, and business model, with technical skills of data and SaaS management.

• Data management: Know how to collect, clean, and mine customer data from large datasets, and use them for business purposes like messaging personalization and distribution — tying them to real business impact.

• SaaS stacking knowledge: Can comfortably jump into, learn, and master multiple SaaS tools — setting up custom workflows that reflect the customer journey — stacking the right tools together based on the company stage and needs, while also imbuing data into each of them to guarantee personalization at scale.

Operationalization means data and tooling knowledge — and the ability to leverage both across the entire customer journey — are becoming critical traits of modern tech talent that yields above average results, faster, and with less resources.

Cross-functionalism and use-case focus

Cross-functionalism is another critical factor that makes the case for the GTM engineer. Since the first Reforge course came out back in 2016, lines between core GTM functions — product, sales, data, and marketing — have been blurring, requiring professionals to build more and more T-shaped skill sets.

As we mentioned above, GTM teams must zoom in and out of the entire customer journey, wearing multiple hats and plugging into different skills to address problems wherever they arise in the funnel.

• Monetization: customers are not upgrading from our free trial. Is it a pricing model problem, a UX/UI problem, or a product one that is preventing customers from monetizing?

• Retention & Engagement: customers are not combing back with the product. Is it a UX/UI, feature parity, messaging, or targeting problem?

• Acquisition: customers are not signing up. Is it an awareness, messaging, UX/UI, targeting, or channel problem?

These problems relate to the key use cases of a tech business — how to acquire, engage, retain, and monetize their users — and transcend the typical problems that relates to the function of the business — marketing, product, sales, etc.

Customers are buying products via a multi-touch and cross-channel experience. To effectively cope with this mindset, tech teams need to transcend their siloed view in terms of a company function — from marketing, product, and sales — and instead embrace a cross-functional use-case view of the business — to acquisition, retention & engagement, and monetization. 

This requires modern teams to be able to understand and manage the end-to-end customer journey, strategize experiments throughout it, and quickly switch gears to solve different types of problems. This adaptability makes modern talent more of generalists with knowledge in 2-3 key areas.

Growth mindset

In this new, fluid and fast-paced landscape, mindset outweighs hard skills. 

While hard abilities remain important, soft skills like risk-taking, adaptability, rapid learning, modular thinking, lean operations, speed, and execution are now more paramount than ever. 

That’s why experience in early- or growth-stage startups — where the speed of growth forces teams to undergo massive change and fast learning curves — become most formative and in-demand. Because it’s what helps forge the growth mindset as we define it at Hypergrowth Partners.

It’s easier now to see how the case for the GTM engineer is clear.

• ZIRP, tech layoffs, and AI are putting pressure on teams to be smaller and more productive

• GTM SaaSificiation is enabling small teams to achieve better outcome with less resources

• Operationalization puts the focus on tech stacks and data knowledge as critical skills

• Cross-functionalism is forcing smaller teams to own the full journey and wear multiple hats

• All of the above make execution and mindset more important than hard specializations

Now that we’ve made the case for the GTM engineer, let’s dive deeper into what great ones look like, and how they differ from other common profiles we’ve been seeing in growth teams in the last few years.

What makes GTM engineers different

GTM Engineers represent a new paradigm in growth teams, distinct from more ‘now traditional’ positions like growth marketers and growth engineers. Before understanding what a GTM engineer is, let’s make some distinctions as to what it’s not.

GTM engineers vs. Growth marketers

Growth marketers focus primarily on customer acquisition. Their objectives revolve around attracting new users through channels like SEO, paid advertising, content marketing, and social. Their focus is on top of the funnel and related topline metrics, like traffic, signups, and activations. To do so, they rely on tools such as web analytics, social platforms, SEO and paid ads software to set up, optimize, and scale campaigns. In some cases, their remit extends to the signup and activation flows, including landing page and UX/UI optimization for the top-of-the-funnel. Their mindset is centered around channel tactics, A/B testing, ICP knowledge, and messaging.

In contrast, GTM Engineers go beyond just acquisition to encompass the entire customer journey. They act as system orchestrators across all stages — from awareness to revenue, retention and churn. They integrate multiple SaaS tools, set up end-to-end custom workflows, and handle large datasets to create a seamless and personalized experience, with or without code. Their holistic approach integrates a solid RevOps and data fluency, while also being knowledgeable in messaging and ICP more typical of growth marketers.

GTM engineers vs. Growth engineers

Growth engineers are software programmers focused on growth product experiments. Their goal is to build or scale experimental features that drive engagement, retention, or revenue. They work closely with growth PMs and growth designers to “build” out these experiments, track their impact via A/B or multivariate tests, and validate them before passing them onto core product and engineering teams. To do so, they use a mix of Python, JavaScript, and SQL too. Their mindset is deeply technical yet scrappy, focused on building aggressive and scalable solutions.

GTM Engineers differ by not being confined to the product itself. Differently from growth engineers, they integrate expertise in the user journey, ICP, messaging, and business acumen (e.g. pricing, forecasting, etc.). Like growth engineers, they can code, but can also quickly jump into managing multiple SaaS, connecting them together, managing data pipelines, and ensuring alignment between GTM teams. 

In addition, GTM Engineers master prompt engineering — and beyond just ChatGPT. Prompt engineering means knowing multiple AI models — Perplexity, Llama, OpenAI, etc. — and knowing how to scope them and leverage different use cases to deploy scraping, scoring, routing, personalization and inject efficiency and scalability to the GTM motion. It’s about how to ensure consistent results at scale.

They make their data fluency actionable by injecting it directly into (AI) tooling to optimize the end-to-end funnel — for use cases like signal-based plays and personalization across the entire funnel — bridging gaps between product, marketing, and sales. 

GTM engineers vs. Growth Product managers

Growth PMs focus on scaling growth by managing a pipeline of product growth experiments. Their objectives include analyzing product data to mine growth opportunities, coming up with hypotheses for experiments, and prioritizing them on a roadmap to drive engagement, retention, or revenue. They work with growth designers, growth engineers, and growth analysts — and tools like product analytics, user feedback platforms, and A/B testing software — to run these experiments. Their mindset balances product, design, and UX/UI with business growth.

While Growth PMs concentrate on the product's evolution to stimulate growth, GTM engineers adopt a more comprehensive scope. Unlike Growth PMs who focus on product experiments, GTM engineers focus on integrating product, marketing, and sales strategies as one, unified growth system. They leverage cross-functional data and insights, intertwine multiple (custom) tools to ensure that every aspect of the customer journey is cohesive and effective.

GTM engineers vs. Growth hackers

The term "growth hacker" often suggests a focus on quick, overnight wins through unconventional tactics. This mindset can set false expectations, positioning the role as one that seeks shortcuts rather than sustainable strategies. We’re not big fans of this term.

GTM Engineers differ significantly in this regard. They prioritize building robust systems and processes over short-term hacks. Their approach is iterative, thoughtful, and scientific, emphasizing scalability through automation and sustainability by working back from business forecasting. By avoiding the "hacking" mentality, GTM Engineers aim for long-term growth and scalability.

GTM Engineers: The Holistic Growth Orchestrators

The challenge now lies in defining and recognizing these professionals. Titles like Chief of Staff, Head of Growth, GTM Ops, RevOps, or Special Projects have been used, but none fully capture the essence of this role. 

Let’s examine even more what their day-to-day looks like, deep diving into their background and real-life examples.

Goals

Concretely, GTM Engineers own pipeline and revenue growth. They’re able to forecast and workback from key business metrics and build workflows and systems — across every customer touchpoint that reliably and directly tie into the business bottom-line.

Ownership

GTM engineers own the entire buyer journey, from identifying the right Ideal Customer Profile to building pipeline and converting it into revenue. They find the most cost-effective ways to turn accounts into dollars, whether through AI, automation, off-the-shelf tooling, product experiences, data vendors, paid programs, or traditional sales. What matters is that it works efficiently.

Their responsibilities include setting up signal-based, cross-channel demand generation programs — such as outbound, paid, content, and SEO — and extending their influence to the bottom of the funnel through lifecycle marketing, expansion, cross- and up-selling, and churn prevention. 

In the early-stages, they set up and execute on setting up these workflows from scratch with a focus on sustainability, repeatability and scrappiness. Then, as the organization matures, they switch into optimization and testing, by coming up with hypothesis-driven experimentation typical of growth teams.

Cross-functional expertise

They embody the cross-functional convergence of marketing, sales, product, engineering — and business! — acting as the link between every functional GTM team. They integrate a unique, T-shaped blend of:

• ICP, user behavior, and messaging knowledge

• End-to-end customer journey

• Business forecasting and modeling

• (Semi)-technical knowledge of coding, SaaS, data, and APIs

Because of that, they don’t think as ‘marketers’, ‘PMs’, or ‘engineers’, but can switch gears to solve system-level problems of ‘acquisition’, ‘retention’, or ‘monetization’. 

Combining technical and creative skills

Standing out requires a blend of technical prowess and creative acumen. Smaller, leaner, cross-functional GTM teams are becoming essential, composed of individuals who can connect the dots across various functions and embrace allbound strategies. These are the "Internet plumbers"—cross-functional generalists who deeply understand each stage of the funnel, are fluent in the latest technology and automation, and can orchestrate large, modular growth stacks. They're more comfortable in tools like Clay than traditional CRMs like Salesforce, and their favorite acronyms are ICP rather than MQL.

Combining large datasets — first-, second-, and third-party signals — with messaging testing, and SaaS tooling, they blend technical and creative skills to build scalable and modular systems tailored to the ICP and the customer journey.

Growth mindset

Because of their cross-functionality, fluency in AI and SaaS tooling, and system thinking, GTM engineers can comfortably operate within lean, small teams, wearing multiple hats and taking full ownership of pipeline metrics. They stay abreast of the latest tools and maintain a growth mindset that prioritizes fast-paced thinking, risk-taking, and execution excellence. Hands-on in building custom tools and integrating systems, they develop high-impact solutions tailored to their organization's needs. Operating. They can work closely with:

• Sales & CS: as their work tie directly to revenue and business impact

• Marketing: as they have deep understanding of the ICP and messaging

• RevOps & Engineering: as they know how to code, use, and interlink multiple SaaS and AIs

• Product: as they operate also on the bottom-of-the-funnel, at churn and expansion

In shorts, GTM Engineers bridge strategy and execution across the entire funnel. Sounds hard or close-to-impossible? And it’s exactly that rarity that makes this type of talent extremely valuable.

Background

This new breed often comes from two, opposite backgrounds: 

• Technical marketers and business operators with a creative flair, such as Hank Taylor, who combine marketing savvy with technical skills and branched out in  more cross-functional, GTM role roles.

• Engineers who have developed a keen interest in revenue generation and GTM strategies, like Rory Conroy and Raman Hundal, leveraging their deep understanding of tech and SaaS to drive revenue initiatives.

Then there are 360° GTM professionals like Sylvain Giuliani, who seamlessly integrate multiple disciplines, embodying a full-spectrum approach to go-to-market strategies. In essence, leading GTM Engineers are versatile problem-solvers who bridge the gap between technical execution and business growth.

Real-world examples of GTM Engineering teams

Gorgias

Consider Gorgias' approach (they have one of the best GTM engineering teams on the planet) — their objective was to understand why Zendesk users were migrating to Gorgias. 

• A traditional growth marketer might listen to Gong calls and manually extract insights—a time-consuming and less scalable method. 

• In contrast, their GTM Engineer developed a script using prompt engineering and natural language processing to analyze these calls automatically. They extracted core reasons for migration, applied these insights strategically, and integrated the findings into GTM systems. When specific signals were detected, they fed personalized, AI-crafted emails at scale.

Gorgias also utilized Cargo to optimize their email outreach. They replaced static segmentation with dynamic, AI-driven content generation, built conditional logic flows for real-time email customization, and integrated with the Instantly API for automated email dispatch. 

This sophisticated orchestration led to a 70% increase in conversion rates compared to previous sequences.

Datadome

Similarly, Datadome exemplifies the GTM Engineer's impact by building a systematic pipeline for lead generation. 

• For each closed-won deal detected in their CRM, they automatically generated lookalike accounts. 

• They retrieved key information from the original deals to craft resonating messages for these similar accounts. 

• The GTM Engineer identified and enriched the right stakeholders within these accounts, allocated them to sales, and pushed them into a sequence where the first email was drafted using deal information combined with AI.

In essence, leading GTM Engineers transcend traditional functional roles by integrating scalable tech, workflows, data — as well as messaging, creatives, and business logic — into complete GTM strategies, resulting in increased efficiency, personalization, and revenue generation.

Here’s a list of top GTM engineers for you to connect with and learn from:

• Trevor Fox

• Hank Taylor, HyperGrowth Partners & Laravel — ex Vercel, GitLab

• Gaurav Vohra, Superhuman

• Sylvain Giuliani, Census

• Rory Conroy, Gorgias

• Jeff Ignacio, Keystone AI

• Diego Martinez, Pigment

• Marcel Santilli, HyperGrowth Partners & GrowthX Labs — ex Deepgram

• Guillaume Aubert, Gorgias

• Raman Hundal, Rhombus

• Vincent Bonjean, Qobra

• Diego Martinez, Pigment

• Aurelien Aubert, Cargo

• Maxence de Villepion, Cargo

• Keerthivasan C, Omni

• William Borges, Veriflow AI

• Nicolas Druelle, The Revenue Architects

• Callum Hamlett, Arquit

How to become a GTM engineer

So, you want to become a GTM engineer? There are multiple pathways to do so. 

• If you come from an engineering background with an interest in revenue and go-to-market strategies, you can leverage your technical skills to build custom data and SaaS architectures that operationalize GTM programs like personalized outbound, lead scoring and routing, or signal-based plays. Developing proficiency in SaaS, AI, customer journeys, personas and messaging can complement your technical and problem-solving abilities and position you to design GTM systems that drive real growth.

• If your background is in marketing or sales, you can acquire the necessary technical skills to transition into a GTM Engineer role by attending coding bootcamps like Ironhack, Le Wagon, or Codesmith.io and get equipped with knowledge in AI, automation, data operations, and programming languages like JavaScript. 

Joining a growth engineering team — perhaps within Hypergrowth Partners' portfolio companies or organizations building GTM engineering tools — can provide the best practical experience and exposure to real-world challenges. Think of companies like:

• Ramp

• Vercel

• Ashby

• Together.ai

• AirOps

• Attention

• Clerk

Also, engaging with GTM engineering communities allows you to network with like-minded professionals and stay updated on cutting-edge developments. Finding a mentor who specializes in growth engineering can also accelerate your journey, offering guidance and insights that refine your skill set.

Regardless of your starting point, the key is to continually expand your T-shaped skillset across both technical and creative domains. By combining technical skills with strategic understanding, you position yourself as a versatile asset capable of orchestrating holistic growth initiatives that span the entire customer journey.

Conclusion

The future of GTM engineering lies in the formation of smaller, tech-enabled teams that leverage AI to drive efficiency and revenue growth. 

• As AI increases revenue per employee, the need for large, specialized teams diminishes, making lean, cross-functional teams of GTM Engineers the new norm. 

• Sales roles will evolve accordingly, with sales representatives becoming key touchpoints within a technology-orchestrated customer journey rather than sole drivers of the sales process. 

This shift paves the way for GTM Engineers to emerge in leadership positions; future CROs and CMOs will need both technical and strategic expertise, and GTM Engineers are natural fits for these evolving roles. Their unique blend of skills positions them to lead organizations in a landscape where tech and strategy are increasingly cross-functional and intertwined.

As traditional growth strategies lose their edge in an increasingly competitive B2B SaaS landscape, the role of the GTM engineer has become crucial. By bridging technical expertise with business strategy, GTM engineers are the new architects of growth.

Implementing GTM engineering in your organization is not just a competitive advantage — it's becoming a necessity. Embracing this shift will position your company at the forefront of industry evolution, ready to outpace competitors and capture new market opportunities.

As we move towards 2025, capturing the attention of B2B buyers in outbound is as challenging as ever — it requires laser-focused targeting, messaging and timely relevance, and multiple touchpoints. Setting up RB2B and a Clay table just won’t cut it.

The explosion of MarTech tools for prospecting, signals, enrichment, and automation is not just creating lots of noise on LinkedIn, but also false promises that signal-based outbound is quick and easy. But it’s not.

With an overabundance of data and tools at our disposal — and email client servers cracking down on outbound as a channel — how can growth operators orchestrate effective signal-based outbound campaigns that generate sustained pipeline?

By strategically leveraging simple tools — LinkedIn Sales Navigator, Clearbit, Apollo.io, GetCargo.io, and Instantly.ai — Attention was able to create highly targeted, data-driven set of campaigns and effectively engage high-potential accounts — efforts that generated a staggering $1.2M in pipeline in just 4 months.

Because of the overabundance of outbound content on LinkedIn these days, most people think they can go from 0 to hero with outbound in a flash, regardless of your stage, budget, and persona. But again, it’s not that simple. So here’s a disclaimer.

This playbook provides a step-by-step guide on how Stewart and the Attention team — with the help of Loic from HyperGrowth Partners — implemented a tactical outbound strategy, maximizing outreach efficiency, and driving sustainable pipeline. It’s ideal for companies that target B2B buyers in a sales-led motion, with ideal customer profiles like Marketing & Sales living on LinkedIn.

To implement this playbook, it’s key to work back with the right resources. You need to have a sales team ready to engage prospects — be it BDRs or full-cycle AEs — and at least one growth marketer in-house to help run the program. Budget-wise, this setup would cost approximately $1,500 per month in SaaS subscription, making it best suited for businesses with an average contract value (ACV) above $6,000.

Ok, now that we’ve got this out of the way, let’s dive in!

Step 1: Define your ICP and messaging

Identify firmographics traits

To build an effective intent-based outbound strategy, the first step is to identify the key traits that define your ideal customers. 

This involves determining:

• Their geographic locations

• Specific industry verticals they operate in

• The size of their companies

• The job titles of potential contacts

• And the pain points they experience that your product can solve

By clearly defining these characteristics, you can focus your efforts on a target audience that is most likely to engage with your offering.

Create 1-3 buyer personas

Next, develop one to three detailed personas for each target role within your Ideal Customer Profile (ICP). These personas should provide in-depth insights into:

• The specific outcomes they’re looking to achieve in the context of your product

• Motivations behind their outcomes

• How they measure such outcomes, with relevant KPIs

• Existing alternatives they’re using to achieve their outcome in absence of your product

• Challenges, problems, and pain points arising from using such alternatives

• Capabilities — what your product does — that help them achieve their outcome

• Benefits of your product/features that come from using your capabilities

• Differentiators that make your product better/different from current alternatives

Creating comprehensive messaging frameworks for each persona allows you to create conviction across marketing and sales, and tailor your outreach messaging and channel strategies to increase resonance and relevance with each segment. 

“I recommend to focus on one persona per campaign — it enables you to be more targeted in your approach, making your tests more effective. Begin by targeting your economic buyer first — e.g. VP Sales/Revenue in the case of Attention — or the persona that is closest to the most significant pain point your product addresses.”. - Loic

By focusing on the key decision-maker who stands to benefit the most from your solution, you increase the likelihood of engagement and expedite the sales cycle. This focus ensures that your outreach is not only targeted but also more likely to convert into economically tangible results — pipeline!

Step 2: Build your TAM, SAM, and SOM lists in Apollo

Start from existing account data

Once you got your personas and messaging locked down, start by leveraging your existing account data. Retrieve current accounts from your CRM.

“Clean up the data to ensure accuracy of your seed list — validate their emails, websites, and phone numbers.” - Stewart, VP Growth

By starting with data you already have, you can build a strong foundation for your campaign and focus on prospects that are more likely to engage.

Alternatively, you can create a new list of target accounts on Apollo by importing your segmentation criteria outlined above.

Expand your initial list with ICP traits filters

Once you have your initial list, expand it by applying Ideal Customer Profile (ICP) trait filters to identify additional high-potential prospects. 

“Attention used prospecting tools like LinkedIn Sales Navigator, Clearbit, or Apollo.io to create highly targeted account and persona searches that matched our ICP.” - Stewart

Use lead scoring to tier your segments

Once you have identified all your traits, come up with segments for each trait, and assign a score to each bucket that will determine how good of a fit each bucket is. For example:

• Geo: US might have a higher score than UK or France, because sales cycles are shorter

• Industry: SaaS might also be higher fit than healthcare as MarTechs are more used

• Company size: mid-market might have a higher fit due to higher ACV and lower churn

• Job titles: as mentioned before, roles that are also budget owners might be score higher

By the end of this exercise, you should have 2-3 key prospect tiers — e.g. tier A, B, and C — each with a corresponding score range. This will inform various aspects of your outbound:

• Align marketing and sales on what great looks like, focusing around a shared pipeline goals

• Which prospects to prioritize with outreach

• How to route various tiers through relevant sequences and sales hand-offs

In the case of Attention:

• Senior buyers like VPs of Sales/Rev Ops receive the highest scores, junior titles receive lower ones

• They prioritize specific industries such as enterprise software and financial services

• Sales headcount is another key factor — prioritizing accounts that have sales teams between 5-100

“We bucket these into three tiers — high quality, normal, do not contact. High quality leads receive both Linkedin messages and SDR phone calls. Normal leads just receive emails, and ‘do not contact’ are not engaged at all.” - Stewart

Get market size data to work back funnel projections

Next, gather data on your Total Addressable Market (TAM), Serviceable Addressable Market (SAM), and Serviceable Obtainable Market (SOM) sizes to understand the scope of your opportunity.

“We employed Apollo.io's Data Health Center to assess TAM/SAM sizes, identifying the best go-to-market opportunities across startups, mid-market, and enterprise segments — and tailor our focus and messaging accordingly” - Loic

You can use this data to decide whether you should focus on persona X or Y, considering factors like competition, your product's capabilities and feature parity, and your brand positioning — all of which impact your sales cycle and pipeline. 

Use these estimates to further narrow down and size your ICP segment, and — based on your Average Contract Value (ACV) — work backward to determine the number of deals, opportunities, meetings or calls, and set pipeline/lead targets across your segments. 

This GTM planning allows you to allocate resources effectively and set realistic goals for your outbound marketing and sales teams.

Step 3: Enrich and score your accounts in Cargo

Import and analyze SAM in Cargo

“Cargo is a next-gen AI growth engineering SaaS that allows you to automate and orchestrate your growth workflows at scale. At Attention, we use it to extract, enrich, prioritize, and trigger outbound campaigns on thousands of contacts.” - Loic

To effectively enrich and prioritize your Serviceable Addressable Market (SAM), begin by importing your SAM account list from Apollo.io into GetCargo.io for enrichment.

Find lookalike accounts with Ocean

Within Cargo, use the Ocean.io integration to find lookalikes of your target account. 

“Creating lookalike audiences based on your current customers allowed us to target prospects with similar characteristics and higher conversion potential.” - Stewart

Enrich accounts with waterfall research

Then, add another Cargo step to enrich your target account list. This step is twofold — account-level and contact-level.

Start with contact-level information such as:

• Where your contacts live

• The college they attended

• Their LinkedIn career — including years of experience and previous roles or companies

• Recent job changes or promotions

• Any personal interests found on their profiles, like skiing or playing a sport in college

Then, layer in recent intent signals to tailor your outreach effectively. Examples include:

• Recent revenue milestones, fundraising, product launches, or new market expansions

• Purchase of specific technographics

• Hiring or layoffs surges in their company

“We monitored a 20% increase in SDR hires in the last six months to identify accounts with a higher fit for Attention’s product. Or, we tracked champion job movements — when new hires joined key accounts who could advocate for us — and gain quick internal recognition from implementing Attention.”. - Loic

These signals can help you estimate a higher propensity to buy and an increased likelihood of expanding their go-to-market motions, in line with the need for Salestech products like Attention. 

Then, use your ICP segments and lead scoring criteria to rank your accounts based on their fit and engagement potential.Score and rank accounts based on their fit and engagement potential to focus your efforts on the most promising opportunities.

Step 4: Set up targeted outreach waves

Group high-score accounts in themed waves

To maximize the effectiveness of your outbound campaigns, group your high-scoring accounts into targeted outreach waves. 

Theme these waves based on your segmentation criteria — like industry, specific use cases or pain points, geography — or personas — such as VP Sales/CRO, Sales Director, or RevOps Lead. 

For example, if you have a champion persona (Account Executive) and two decision-maker personas (Head of Sales and Head of Marketing) operating across three regions (US, EU, Asia), you can structure your outreach as follows:

US Campaign

1. Champion (AE): Test three messaging strategies around three specific signals.

2. Decision Maker (HoS): Test three messaging strategies around three specific signals.

3. Decision Maker (HoM): Test three messaging strategies around three specific signals.

Then, scale this template across all your key theme combinations, and progressively start creating messages from the highest-scoring segments.

Group high-score accounts in themed waves

For each wave, customize outreach messaging around the signals and personas identified in each wave. Assign each group a tailored messaging theme and value proposition that resonates with their unique characteristics and challenges. 

This approach ensures that your outreach is highly relevant and personalized, increasing engagement and the likelihood of conversion.

Step 5: Enroll prioritized accounts into multi-channel outreach with La Growth Machine or Instantly

Enhance deliverability

To enhance email deliverability, use Instantly.ai's warm-up features. These tools build credibility for your sending domains, increasing the likelihood that your emails land in inboxes rather than spam folders.

Email clients provider like Google are cracking down on outbound as a channel, alerting recipients of potential spam from suspicious domains and emails including links or pictures — so deliverability has never been more important. Some providers like Smartlead are even discouraging tracking open rates of emails because of this crackdown.

“We do track open rates. It leads to slightly worse deliverability, but we think the ability to retarget people who open an email multiple times is worth the trade-off.” - Stewart

Automate sequence enrollment

To streamline your outreach process, automate enrollment by pushing your prioritized contacts from GetCargo.io directly into La Growth Machine for cross-channel outreach on both email and LinkedIn, or Instantly for just emails. 

This integration automatically enrolls them into multi-touch email sequences, ensuring timely and consistent engagement without manual effort. 

Messaging is always paired to the most recent signal related to the contact or org. So if someone was recently hired into their role, we will reach out and congratulate them on the role. We're testing the combination of multiple signals in the email copy, like someone was recently hired and recently followed our linkedin page — there’s a ton of opportunities for stacking signals here.” - Stewart

Personalize messaging with AI

Personalization is crucial for capturing your audience's attention. 

Leverage ChatGPT within Cargo to craft compelling, personalized emails that address each account's specific pain points and potential use cases. This level of customization makes your messaging more relevant and increases the chances of eliciting a positive response.

“We personalize copy in Cargo and then send it to Instantly via an API.” - Stewart

Strive for simplicity and relevance in your outreach emails:

• Use first name in salutation

• Lead the opening with a tailored, relevant one-liner based on a recent signal or event

• Transition to a second one liner mentioning an alternative, or your value prop

• Close with a soft CTA, like “Can I show you a 15-min demo?” or “Can I share more info?

Avoid using links or images to hurt deliverability, especially on first touches with cold contacts. 

Set up automated nurturing by establishing follow-ups and touchpoints that guide prospects through the sales cycle. 

“We aim for a minimum of four touchpoints. If we see no engagement from someone in the first few touches, we'll remove them from the sequence. We keep follow-ups short and sweet — we're more trying to ‘bump’ the thread” - Stewart

Integrate notifications via Slack to keep your team informed of key replies or self-serve signups from specific high-score accounts, and update scoring attributes via Cargo right into your CRM to identify higher-potential leads.

Send physical letters or gifts via Sendoso

To stand out from competitors, add a Cargo step to send physical sales letter or gifts via Sendoso to the highest-value prospects.

“A tangible item can make a memorable impression, differentiating your approach and providing meaningful conversation starters.” - Loic

This combination of automated digital engagement and personalized physical outreach creates a multifaceted strategy designed to capture attention and drive sustainable growth.

Step 6: Monitor, analyze, and optimize your campaigns

Track key metrics

To maximize the effectiveness of your outbound campaigns, it's crucial to track key metrics diligently. Monitor:

• Open rates — mostly influenced by your list health, deliverability, and subject line

• Reply rates — mostly impacted by a combination of your list composition and messaging

• Meetings booked — again, influenced by list, messaging, and CTA, but also timing

• Opportunities logged — more impacted by their SDR/BDR meeting experience

• Pipeline value of total opportunities — a combination of all the above factors

Learn signal/messaging/persona fit

This continuous monitoring provides invaluable insights into how your campaigns are performing and where adjustments may be needed.

“Understanding the fit between signals, messaging, and personas is essential for success. Evaluate which messaging strategies, target personas, and engagement signals yield the best results.” - Loic

By analyzing this data, you can determine which combinations resonate most with your audience, allowing you to tailor your approach for maximum impact.

Testing

Iterate on your campaigns by testing new approaches, refining your targeting criteria, and adjusting your messaging based on data-driven insights. This proactive strategy ensures that your outreach remains dynamic and effective, ultimately leading to better engagement and higher conversion rates.

“We operate on two-weeks sprint cycles — we'll re-evaluate messaging after every couple of weeks, or when an experiment hits a stat sig level of volume. However, in the early stages of setting up the outbound motion, net-new list building is more important as volume is lower. The opposite is true for messaging — when you have larger list, you can focus on testing that.” - Stewart

Conclusion

By implementing a signal and intent-based outbound strategy, leveraging simple yet effective tools, and enriching your existing datasets with lookalikes, B2B SaaS companies can significantly enhance their outbound efforts. This step-by-step playbook empowers VPs and growth operators to:

• Execute a tactical, data-driven approach that targets high-potential accounts with precision.

• Personalize outreach and continuously optimize based on real-time feedback to improve engagement.

• Drive sustainable pipeline growth and gain a competitive edge in the market.

By focusing on these strategies, businesses not only improve engagement but also position themselves for long-term success in an increasingly competitive landscape.

In 2024, most people are easily swayed by the content influencers share across their platforms. While the B2C influencer market is hyper-saturated, content creators in the B2B space have adopted a similar playbook that is becoming a clear growth lever — to the extent that some founders are jumping on the bandwagon to take on the  influencer persona themselves.

Particularly in the Developer Tools sector — where community has been the name of the game for years –– “marketing” has their sights set on creating awareness and building trust within the developer community. The tech industry moves quickly and developers are constantly evaluating which languages, frameworks, and tools will help increase their velocity to ship polished user experiences. 

“YouTube has become a trusted platform where developers can go to be informed of the best new tools and strategies with confidence and ease. The platform offers developers on-demand access to a wide spectrum of content to grow their skill set and become more marketable, no matter their role or level of experience.” - Alex Rapp, Clerk

Despite the significant influence creators have on B2B brand building (and the bottom line), most companies are dropping the ball. They drown in cumbersome analytics and operations, and force guidelines and briefs onto creators, undermining the very authenticity that makes these collaborations valuable.

In this post, we'll show how Clerk — with the help of Gonto and the HyperGrowth Partners team — tackled this challenge, sharing roadblocks and learnings that allowed them to transform YouTube into a top performing full-funnel pipeline.

Learnings from pilot partnerships

When kicking off Clerk’s YouTube partnership program, Gonto and Nick Parsons, Director of Marketing at Clerk, zeroed in on YouTubers as the initial core personas. But why? 

The YouTube algorithm is one of the best recommendation engines on the internet, giving marketers confidence they’re reaching an engaged and high-intent audience. YouTube also favors long-form content, such as how-to’s and tutorials, that can have a long shelf life due to viewers continually consuming the content despite the publication date.

Last summer, Nick started by reaching out to creators that were a fit for Clerk's solution and simply asked if they'd be interested in trying Clerk. By giving the creator a test drive, Clerk quickly realized that it became easier to find authentic partnerships.

The emphasis was on maintaining an organic and personal vibe in communications to foster genuine relationships. 

“We wanted to avoid cringey sales pitches and instead give developers the chance to experience Clerk’s 'aha' moment on their own, showing them both the developer and user experience rather than selling them in a 30-second mid-roll ad.” - Nick

The pilot experiment included three long-form videos solely featuring Clerk, which did not move the dial on the metrics they were hoping to influence. However, these learnings began to shape Clerk’s future YouTube partnerships. 

The initial integrations did drive awareness, but accompanied by some negative sentiment. In connecting with some viewers of these early integrations, the Clerk team learned they built the impression of: "These guys are paying people to talk good about them," which is not how Clerk wanted to be perceived. Moreover, these videos didn't impact important metrics like account sign ups and activations.

Faced with additional issues of poor attribution and tracking effectiveness, these learnings showed that Clerk needed to experiment with a new approach more in-line with their core values and one that spoke to developers, rather than selling.

“We wanted the viewing experience to be authentic, so we decided to change direction.” - Nick

From then onwards, Nick and Gonto committed to giving creators full creative freedom to showcase Clerk’s features they were most passionate about and even call out product bugs in their content.

“At the end of the day, we learned that developers don’t want to just learn new frameworks and services in a vacuum, but in the context of learning how to create entire apps, like an Uber or ChatGPT clone. That’s why we prompted creators to use Clerk as one of the tools in the toolbox for creating those apps. And that performed drastically better.” - Gonto

Clerk advocated creators recommend their own content topics but suggested ideas that were popular in the developer community, such as "how to build a ChatGPT clone" or "how to build a project management app," to attract relevant audiences. By positioning themselves as just another part of the tech stack, not the focus of the video, Clerk hoped to achieve the organic integration they sought to test.

After running a few more integrations as part of the pilot, the team validated the above hypothesis by observing that the base of sign ups started to increase. Armed with this new direction and positive results, the team was ready to scale the channel to a proper growth program.

From pilot to partnership program

After the initial pilot, Clerk hired Alex Rapp, who grew YouTube into a full-fledged creator program, rapidly achieving step growth for the channel.

Solving attribution

To enhance tracking effectiveness, Alex introduced branded attribution links in video descriptions to give a more organic appearance and built a reporting suite that eased the process of measuring effectiveness at macro & micro levels.

Despite these efforts, the team noticed a significant proportion of sign ups were still coming from a “halo effect” rather than directly from the attribution links. On days where partner videos went live, Alex observed an uptick in sign ups attributed to direct traffic and organic search. It’s well documented that developers are keen to avoid being tracked, so this was no surprise. The team quickly learned that these ‘hard to measure’ channels followed a similar trend to their YouTube-attributed sign ups. This allowed them to directionally attribute sign ups to specific videos, even when the traffic hadn’t come directly from one of their attribution links.

All of that being said, the attribution links did contribute to their ability to directionally measure impact and opened doors to more avenues of growth and experimentation.  For example:

• Building retargeting audiences from partner channels

• Running in-feed YouTube ads of partner content they’d sponsored to extend reach

• Re-distributing sponsored content to other channels (e.g. newsletters, dev.to, etc.)

Automating creator scouting

With attribution basics out of the way, scaling partnerships became the next priority. The strategy here was two-prong:

1. The team began by manually searching YouTube for mentions of ‘Clerk’ in video titles, descriptions, and transcripts to identify creators who were already using Clerk in their tutorials. This approach indicated positive brand awareness and creator affinity for Clerk.

2. The team expanded their search to identify creators working within frameworks and libraries supported by Clerk, such as Next.js, React, and React Native.

They automated both steps by creating a YouTube scraper integrated into their Hex reporting environment, drastically reducing the manual burden. Developed internally by one of their data engineers in just one day, the script crawled public YouTube data daily for specific keywords like ‘Clerk’ or ‘Clerk Authentication’.

Scouting creators

When evaluating potential partners, Clerk leveraged YouTube’s native metrics and Social Blade, among other tactics, to assess key performance potential signals:

• Monthly channel growth trends, using 10,000 subscribers as a soft minimum threshold

• Views-to-subscriber ratio to gauge audience engagement

• Community interaction through likes, comments, and Q&A participation

• A consistent production schedule to ensure ongoing engagement with their audience

  

Overall, Clerk prioritized growth potential over audience size. The partnerships were split:

• 40% established creators with established audiences, whose growth curve wasn’t as sharp — these creators leaned into production quality and brand equity value, which comes with all the related positive associations.

• 60% up-and-coming creators for cost-effectiveness and future potential — these were not just value-for-money bets, but faster-growing ones they could invest in and “grow together with”.

Granting full creator autonomy 

What set Clerk’s creator program apart was their outreach and collaboration process. It was entirely rooted in granting total creative freedom to foster genuine partnerships with creators.

Keeping outreach casual

Alex built on the personal outreach Nick had created and shared additional partnership benefits, including creative authority, no deadlines, and performance insights to name a few.

This email was followed by a face-to-face meeting to get to know one another and iron out what an ideal partnership looks like for both parties, strengthening the human relationship and ensuring the creator & Clerk were on the same page.

Creative collaboration > Pitch

When discussing partnerships, Alex encouraged creators to speak authentically about the product, even if it involved flagging product bugs. 

Rather than dictating the messaging, the team leveraged their Clerk expertise to help creators identify features that would best resonate with their audiences and the content topic. To foster creative brainstorming, Clerk would occasionally share content topics based on the latest trends in the developer community — for example, how to build an app that uses the latest version of Claude 3.5 Sonet. However, Clerk ultimately left final content decisions up to the creator, in the spirit of giving them full autonomy.

“This approach empowered creators to showcase the product authentically, resonating more deeply with their audiences, because they were able to build applications they were genuinely excited about. In the end, the creator knows their audience best, and we didn’t want to add friction to what they’ve worked so hard to create.” - Alex

Flexible production schedule

To accommodate the creators' workflows, Clerk also gave them full flexibility by not enforcing deadlines for when content would be published. This allowed creators to focus on producing high-quality work they could be proud of, without the pressure of adjusting complex production schedules to meet artificial deadlines.

“We aimed for quality over quantity, fostering a frictionless environment for creators to build.” - Alex

Creator dedicated communication lines

To simplify collaboration, Alex established dedicated Discord or Slack channels for each partner that included technical experts from the Clerk team. Both parties used these communication channels to ideate, troubleshoot, and take care of logistics in a speedy manner.

“Collaborating through email was far too cumbersome for us and our partners. Setting up comms in Discord was a no-brainer since that’s what developers use already. Creating dedicated channels, rather than pointing them to our support channel, gave them a fast lane to our team.” - Alex

Partner compensation and management

When it comes to partner compensation, Clerk offered monetary incentives on a per-video basis.

Clerk allows creators to propose their preferred compensation per video, trusting their insight into what it takes to produce the quality content their subscribers expect. Initially, Alex used scouting signals to negotiate fair compensation. In the present, he’s built a normative database, inclusive of efficiency benchmarks, that more precisely guide negotiations — especially during their ‘experimental phase’. 

Partnership experimental phase

To kick off the partnership, they suggest an initial scope of 3 to 5 video integrations to understand how Clerk resonated among the creators' audiences. 

“The experimental phase gave me enough confidence in the performance we were seeing to either extend the partnership or pause efforts. In some cases, we’d make recommendations to improve engagement and sometimes it’s just not a good fit, but that’s alright. If Clerk wasn’t resonating with a creator’s audience, we didn’t want to force the issue.” - Alex

Sharing insights

Alex and team shared transparent feedback on video performance. By highlighting winning themes alongside key metrics, Clerk provided creators with directional reporting to improve their content, giving them indications on how they performed compared to their benchmarks. In some cases, they also shared learnings from more established creators to help grow their channels. The emphasis was on a mutual partnership, not just driving Clerk’s metrics or funding a creator’s channel.

“This feedback loop empowered creators to refine their work, enhance their content, its impact, and our overall relationship — they wanted to continue partnering with us because we gave them data to be successful. Surprisingly, this was novel to some partners and something they’ve been asking for but weren’t getting from other sponsors.” - Alex

As partnerships grew, Clerk introduced creators to other developer tool teams within its network. This fostered content diversity, expanded reach, and unlocked additional developer tool networks, sometimes reducing Clerk’s partnership costs and enhancing mutual benefits.

Additional incentives tactics

Gonto tested other incentive tactics with HyperGrowth Partners portfolio companies, besides Clerk, including:

• Experimenting with a performance-based CPM model ranging from $20 to $55 for every 1,000 views a video accumulated

• Requesting a ‘make good’ if the first integration didn’t reach a minimum threshold of views

• Capping the viewership amount in the first two months to evaluate performance and payments

Processing learnings and results

Scaling their YouTube program sharpened Alex’s focus on collecting results and insights to assess its effectiveness.

Setting up actionable reporting

Alex unlocked partnership performance by leveraging BI tools like Hex and Notion that were fueled by Clerk’s backend database. This allowed Alex to easily create a normative database, establishing performance benchmarks, efficiency tiers, and easily capture findings to continually optimize performance.

The reporting suite is updated at specific milestones, post-publish, to account for the long-tail effects of partner integration allowing him to understand the different stages of growth, signaling which partners excel at SEO practices. 

Youtube’s full funnel impact

Despite the known challenges of attribution, further data analysis yielded deeper learnings for the Clerk team. They understand that YouTube partnerships aren’t just a direct conversion play, but can also influence every layer of the funnel.

“Once we established our normative database, it became evident that some partners were better at driving sign ups & installations than others. Rather than writing off the lower converting partners, we saw they were able to contribute to our success by filling the top layers of the conversion funnel.” - Alex

• Top Funnel: Increase in brand awareness and engaged site visitors

• Mid-Funnel: An organic increase in the number of creators using Clerk in tutorials; Increased engagement in partner video comment sections

• Bottom Funnel: Step level growth in sign up/installation volume while retaining above industry average conversion rates

“Starting with 3-5 creators, we now boast 20 active partners that have driven a 30% increase in YouTube attributed sign ups as well as the strongest installation rate of any of our channels. This approach fostered genuine engagement and unlocked new avenues for growth.” - Alex

The chart above shows the daily volume of sign ups and installations, which peak when new videos were published. Not every YouTube integration had the same impact on sign ups or installations, something Clerk plans to solve for through more advanced data analysis.

These results underscored the value of the creator program and validated the hypothesis to double down on the investment.

Closing Thoughts

By fostering casual and genuine partnerships with YouTubers, Clerk successfully embedded itself into the developer community. Their approach not only enhanced their reach and adoption, but also unlocked valuable insights into audience interests and content strategies, all while keeping ops lean and casual, ultimately capturing time savings for the Clerk team.

For growth operators in the B2B SaaS space, Clerk's strategy offers a compelling blueprint for engaging creators with genuine, value-driven collaborations, which is ultimately what partnerships are all about.

In the race for scalable organic growth, many B2B SaaS companies find themselves caught in a paradox. Everyone craves the long-term benefits of organic growth, yet few are willing to invest the necessary time and resources upfront. This shortsightedness has led to a troubling trend — the misuse of AI to automate outbound marketing and content creation, resulting in a deluge of subpar messaging flooding the internet.

Consider the all-too-common scenario of a founder lamenting, "We're burning $100k monthly on ads with moderate success, but can't justify $10k for content because it's 'too slow'."

This mindset often stems from past disappointments with overpriced content agencies delivering mediocre results. However, this approach misses a crucial point: to win the organic game, companies must shift from renting audiences to earning them.

The situation is further complicated by the first wave of AI tools, particularly chat-based systems. While they promise to automate and scale organic growth, the reality is often disappointing. Despite a significant productivity boost — estimated at 2-3x per individual contributor — these tools are frequently misused to amplify noise rather than signal. Even more advanced vertical AI content writing platforms, while gaining popularity, often fall short of delivering truly effective, tailored content that resonates with specific audiences and aligns with unique business goals.

The bottom line? Growth and marketing teams are already leveraging AI, but often inefficiently. Many find themselves investing the same, if not more, time in programming and connecting these tools, without achieving the top-tier results they seek. To truly harness the power of AI for organic growth, a more strategic, quality-focused approach is essential.

Why AI workflows actually deliver on the AI promise

AI workflows are ushering in a new era for B2B SaaS go-to-market strategies, representing the third generation of AI and a revolutionary approach to scaling growth. 

These workflows deliver on the AI promise by empowering small teams to amplify their productivity 100-fold, enabling the rapid deployment of complex organic strategies and providing a significant edge in time-to-market and market share expansion. 

The impact is astounding: our experience shows that AI workflows can slash content creation costs from $1-2 per word to as low as $0.10 - an impressive 20X reduction! 

This post delves into how integrating AI into GTM strategies can supercharge growth and efficiency for modern startup teams. We're not just offering a step-by-step guide on implementing AI workflows for B2B SaaS GTM across content and outbound channels; we're providing a strategic framework to identify and scale growth opportunities throughout your business. Our approach leverages AI for research, content generation, and workflow automation, dramatically boosting efficiency and output. 

At HyperGrowth Partners, we're not just giving you the fish, we're giving away our fishing rod, equipping you with the tools to transform your GTM strategy in the AI era.

Benefits of custom AI workflows

AI workflows are revolutionizing B2B SaaS marketing, offering a powerful solution that delivers on the AI promise of scalability and cost savings while maintaining exceptional quality. 

However, it's crucial to note that human oversight remains essential in setup and review processes - this human touch is your competitive advantage, preventing your brand from becoming a "fast-food" content factory in the eyes of both customers and search engines. AI workflows outperform vertical AI apps in several key ways:

• Multi-level customization: Custom AI workflows allow tailoring content to specific needs and use cases, incorporating brand voice, audience preferences, specific constraints, and context.

• Multi-step processing: Breaking down content creation into multiple steps ensures comprehensive and polished output, increasing the quality bar even with complex tasks.

• Multi-model integration: Using different AI models for different tasks like ideation, research, and content generation across text, images, video, and audio — helps leverage their unique strengths while optimizing overall token costs.

• Model training integration: Incorporating industry-specific resources like whitepapers, technical documentation, niche books, and more; ensures content aligns with your brand's expertise, even when there’s no evergreen content to scrape on the Internet.

• Measurement and improvement: Custom AI workflows allow for a much more objective measurement of productivity gains and impact, identify bottlenecks to improve, as well as for refinements for doubling down on scale.

When implementing AI workflows, don’t try to go from zero to 100% AI automated. Instead, adopt a crawl-walk-run approach. Ask yourself:

• What existing tasks could an AI workflow make 10x easier/faster?

• How would complete abstraction from the work look?

• With unlimited time, how would you approach certain tasks?

This strategic implementation ensures you harness the full potential of AI workflows while maintaining your unique brand voice and expertise.

Deepgram’s Story: AI + PLG Flywheel = 24X Traffic Growth in 2 Months!

At Deepgram, Marcel ingeniously combined AI workflows with a Product-Led Growth (PLG) flywheel to create a compounding growth engine, where each component gains momentum to drive efficiency. This innovative approach allowed his team to build entire programs that form the core of this flywheel:

• AI Apps catalog: A comprehensive directory generating significant monthly views, benefiting startups, and attracting newsletter subscribers

• AI Minds newsletter: Engages a large subscriber base, promoting startups and encouraging new signups

• Startup program: Offers free credits and community access, featuring members in newsletters and AI catalogs to fuel growth

• Affiliate program: Provides referral incentives, driving signups from influencers and program members

By leveraging AI workflows, Marcel's team was able to efficiently create and manage these interconnected programs, each feeding into the next to create a powerful, self-sustaining growth ecosystem. This approach not only maximized resource efficiency but also ensured a cohesive, multi-faceted strategy that continually attracts, engages, and converts users in the B2B SaaS space.

Strategic questions to guide AI workflow implementation

When implementing AI workflows for B2B SaaS content marketing, it's crucial to focus on tasks that are time-consuming, repetitive, or require precision. To guide your AI implementation strategy, ask yourself:

• Which tasks consume the most time and could be automated?

• What repetitive tasks are ideal for AI assistance?

• Where are the bottlenecks in current processes that AI could streamline?

• What data-rich sources can AI leverage for insights?

• How are competitors using AI, and where can we differentiate?

• Where can AI provide quick wins with immediate benefits?

Start your AI workflow journey by:

• Using AI for research on your company and competitors

• Conducting in-depth audience analysis with tools like Gong and Humata

• Leveraging keyword research and competitor analysis tools (e.g., Ahrefs, SEMRush) to form hypotheses

• Beginning small and iteratively building AI capabilities

However, be mindful that not everything should be automated. For instance, integrating with a legacy CMS might be more costly and time-consuming than maintaining a human-in-the-loop process. Always weigh the potential benefits against the implementation costs to ensure your AI strategy truly enhances your content creation process.

Key components of an AI workflow

To implement effective AI workflows in B2B SaaS content marketing, three key components are essential:

1. Command Center (Airtable)

This is your coordination hub, offering:

• Database-like structure with flexibility for various use cases

• Robust ecosystem of extensions, integrations, and automations

• Visual interface for easy collaboration

• Cost-effective scaling based on user seats

2. Workflow automation (AirOps) 

Think of this as Zapier with embedded programmable AI, featuring integrations with CMS, docs, Gmail, and Slack for critical notifications.

3. Integration automation (Make vs. Zapier)

Triggers your AirOps workflow when input lands in Airtable.

4. Modular CMS (Webflow or Strapi)

Additionally, a cutting-edge, modular CMS like Webflow or Strapi is crucial to avoid integration bottlenecks. 

5. Human in the loop

The final piece is the human-in-the-loop strategy, involving a senior IC with content marketing and writing skills who can:

• Run daily AI workflows and SEO/website audits

• Conduct keyword research

• Manage content updates and backlink outreach

• Stage content in the CMS and QA AI-generated content

This comprehensive approach ensures a scalable, efficient content program that leverages AI while maintaining human oversight for quality and strategy.

Use case — AI workflow for content creation, SEO, and organic Traffic

AI tools have revolutionized the way B2B SaaS marketers drive traffic and create engaging content. Programmatic SEO, a powerful strategy that combines AI efficiency with human insights, offers a scalable approach to optimized content creation. This process can be broken down into two key phases:

Phase 1: Insight + Validation

• Question: Can AI write better content programmatically?

• Answer: Yes, when fed the right input, AI can generate superior answers

  

Phase 2: Content + Page building

The focus here is to identify the necessary elements for programmatic page creation at scale.

By leveraging this approach, marketers can efficiently produce high-quality, targeted content that resonates with their audience. The key lies in properly structuring AI inputs and validating outputs, ensuring that the content not only meets SEO requirements but also provides genuine value to readers. This strategy allows for rapid scaling of content production while maintaining quality, giving B2B SaaS companies a significant edge in their content marketing efforts.

Use case — Create an app catalog or database

Creating an app catalog using AI workflows can significantly streamline your B2B SaaS content marketing efforts. Here's a step-by-step process to efficiently build a comprehensive app catalog:

1. Seed list generation: Export a list of websites from Ahrefs or SEMRush.

   

    

2. Import your seed list into Airtable as your command center.

   

3. Categorization: Employ an LLM to categorize the list within your command center.

4. Copy asset creation: Utilize AirOps/Claude prompt to generate:

• Meta titles and descriptions

• Taglines

• Articles’ body

5. Image asset creation

• Cover images: Pull using Brand Fetch

• Logos/icons for thumbnails: Create using thum.io or abyssale

6. Quality control: Implement human-in-the-loop QA tasks to check for:

• Missing cover images, logos, or colors

• Quality flags

• Final QA

7. CMS Publication: Use AirOps to publish the finalized content

This AI-driven approach allows for rapid, scalable creation of high-quality app catalogs, ensuring consistency and efficiency throughout the process while maintaining human oversight for quality assurance.

Use case — Generate SEO articles

Revolutionize your SEO article generation process with this AI-powered workflow:

1. Target keyword identification: Leverage AirOps to gather comprehensive data from:

• Live Google SERP results

• Competitor H1-H4 analysis

• "People Also Ask" and "Related Questions" sections

2. AI-driven copy creation: Utilize AirOps/Claude to generate:

• Meta titles and descriptions

• Article introductions, titles, and outlines

• Full article bodies

3. Enhanced relevance: Use AI to fetch related posts and choose internal links

4. Visual appeal: Employ AirOps/Midjourney to create featured and OG images

5. Quality assurance: Implement automated fact-checking with Perplexity and plagiarism detection with Grammarly

6. Human touch: Incorporate a human-in-the-loop for final QA, including:

• Checking for missing visuals

• Verifying links and cross-links

• Conducting a final quality review

7. Seamless publishing: Use AirOps to push content directly to your CMS

This streamlined process combines AI efficiency with human oversight, ensuring high-quality, SEO-optimized articles at scale.

Use case — Automate content strategy and roadmap

Revolutionize your content strategy with this AI-powered workflow:

1. Identify competitor keyword gaps using Ahrefs data

2. Filter out brand and negative keywords with AirOps/Claude

3. Cluster keywords intelligently using AI prompts

4. Prioritize topics based on search volume, ranking difficulty, purchase intent, and topical authority

5. Implement human-in-the-loop quality control for keyword strategy and final QA

6. Funnel prioritized topics into diverse content types:

• Editorial articles

• Listicles

• Directory pages

• Glossary pages

This approach leads to increased traffic, conversion rates, backlinks, and brand awareness. Expand your AI-driven content strategy to include:

• Directory pages and knowledge-base articles

• Information-rich catalogs and databases

• Content repurposing across podcasts, videos, and social media

• Competitor comparisons and integration landing pages

• On-page technical SEO fixes

By leveraging AI throughout your content lifecycle, from keyword research to distribution, you'll create a scalable, data-driven content machine that consistently delivers value to your audience while boosting your SEO performance.

Use case — Turn Signals into opportunities, and scale pipeline

AI is revolutionizing the way B2B SaaS companies operationalize metrics and user behaviors to generate leads and opportunities. To harness this power, follow these key steps:

1. Define your sales cycle thoroughly

1. Establish your Ideal Customer Profile (ICP) and lead-scoring model

2. Utilize tools like CommonRoom or Koala to capture intent signals

3. Prioritize accounts based on ICP and intent scores

4. Send personalized emails triggered by specific actions (e.g., demo bookings, pricing page views, lead magnet downloads)

5. Automate sales team alerts via Slack and CRM

6. Automate sales team alerts via Slack and CRM

   

This AI-driven approach extends beyond lead generation, offering transformative applications across demand generation:

• Enhancing sales outreach and enablement

• Scaling Account-Based Marketing (ABM) and personalization

• Optimizing paid social and SEM ad-to-landing page experiences

• Enriching e-commerce product pages

• Personalizing lifecycle marketing and churn discount strategies

By leveraging AI in these areas, B2B SaaS marketers can dramatically improve efficiency, personalization, and overall campaign effectiveness, driving growth and customer retention like never before.

Conclusion

In today's rapidly evolving B2B SaaS landscape, integrating AI into workflows is no longer optional—it's essential for modern go-to-market (GTM) strategies. By creating self-reinforcing cycles around products, companies can ensure sustained growth and market dominance. 

The key lies in rethinking AI not as a standalone tool, but as a series of workflows that operationalize important signals, building competitive muscle in terms of revenue per employee, productivity, time to market, and scale. 

These AI workflows offer a transformative approach to GTM strategies, enabling B2B SaaS founders and marketing teams to effectively scale growth and revenue across every organic and paid channel. While this journey requires careful planning and incremental implementation, the benefits are undeniable:

• Significantly increased speed and efficiency

• Enhanced market impact and penetration

• Improved ability to adapt to market changes

By embracing AI workflows, B2B SaaS companies can position themselves at the forefront of their industries, driving innovation and growth in ways previously unimaginable.

The landscape of DevTools is undergoing a seismic shift — B2B DevTools marketers, grab pen and paper and take notes.

Traditional Developer Relations (DevRel) has long been the cornerstone of developer engagement. Focused on the 3Cs — conferences, community, and content — DevRel teams have excelled at building relationships and fostering developer ecosystems. These efforts have been crucial in creating brand loyalty and establishing thought leadership within the developer community.

However, a persistent challenge has plagued DevRel initiatives: the difficulty in measuring their direct impact on the bottom line. While DevRel activities undoubtedly add value, quantifying this value in terms of revenue or user acquisition has often been elusive. This lack of measurable outcomes has sometimes led to questioning the ROI of DevRel efforts, especially in more fiscally conservative business environments.

Enter Developer Marketing — the next evolution in developer engagement. This data-driven approach bridges the gap between community-building and measurable business outcomes. Developer Marketing takes the best aspects of DevRel — the deep understanding of developer needs and the ability to create meaningful connections — and combines them with measurable growth tactics that drive quantifiable results.

It's not about abandoning the community-centric approach of DevRel, but rather enhancing it with bottom-line-focused methodologies that resonate with both developers and business executives.

As we delve deeper into this topic, we'll explore how this shift is reshaping strategies, driving ROI, and creating new opportunities for meaningful developer engagement that directly impacts business growth.

In this post, we’ll specifically delve into the most largely untapped and hugely controversial opportunity for DevMarketing — Developer-Led Outbound. We’ll be sharing creative tactics on how you can effectively employ personalized outbound to bypass developers’ “BS sniffer”, add value, and drive meaningful conversations.

Let’s go!

From closed builders to key buyers

Last decades’ evolution of the developer's role in B2B SaaS has been nothing short of revolutionary. While developers were once viewed as siloed actors building products with internal tools, the landscape has dramatically shifted since the mid-2000s. 

The rise of cloud computing, SaaS, and now AI technologies has propelled developers into the spotlight as key influencers in the buying process.

Here's how developers have become pivotal in B2B decision-making:

• Growing influence: Though directors of engineering still hold the final say, developers wield significant power in shaping purchasing decisions.

• Technical expertise: Senior engineers can effectively argue for specific tools that impact project timelines and outcomes.

• Budget allocation: Engineering teams now have dedicated budgets for external SaaS tools.

• Early adoption: Developers are often the first to embrace and champion new technologies within their organizations.

While they may not hold the final decision-making power, their influence has grown exponentially. Today's developers wield significant sway in the procurement process, acting as key influencers who can make or break a deal. 

Consider this scenario: two senior engineers approach their director with a compelling case — "If we buy Product X, we'll ship Feature Y on time. Without it, we're looking at delays." 

In most cases, the director will heed their advice, recognizing the technical expertise and on-the-ground insights these developers bring to the table. Companies like Twilio, MongoDB, and Stripe recognized this shift early on, pioneering developer-centric approaches through:

•  Modular tech stacks

•  Engineering-focused content marketing

•  Robust Developer Relations (DevRel) programs

However, these trailblazers primarily focused on inbound and Product-Led Growth (PLG) strategies. The next frontier? Engaging developers through targeted, developer-led outbound. Controversial? Yes, but as the developer's influence continues to grow within the org, savvy B2B marketers must tap into this channel to effectively reach and persuade this technically-savvy audience across all channels.

Let’s show you how to do this properly.

The decline of DevRel and the rise of DevMarketing

Before we get into the weeds, it’s key to mention that the landscape of developer marketing is evolving rapidly.

Traditional Developer Relations (DevRel) strategies are giving way to more sophisticated, measurable approaches. While DevRel has long focused on the 3Cs — conferences, community, and content — its impact on the bottom line often remained elusive. 

And that’s when Developer Marketing emerged — a more data-driven evolution that bridge the gap between community-building and measurable business outcomes.

The evolution of DevRel into the more robust Developer Marketing approach is intrinsically linked to broader economic shifts. The era of zero interest rates fostered a climate where DevRel teams could operate without strict revenue-related KPIs, focusing primarily on community building and engagement. However, as economic realities have shifted, new times called for a more revenue-focused approach. 

Companies like PlanetScale and MongoDB recently restructuring their DevRel teams to re-align their focus on measurable growth goals are other tangible signs that reveal the decline of DevRel in favor of a more attributable approach to developer GTM.

Also, the latest tech advancements enabled DevMarketing strategies to leverage data created by developers interacting with products and websites, combining them with AI-driven personalization. This combination allows for outbound marketing efforts that are tailored and value-additive. Unlike the generic outreach of the past, modern DevMarketing can offer developers insights, solutions, and opportunities that are specifically relevant to their work and challenges — bypassing their BS sniffer altogether.

The result? Outbound marketing to developers is no longer a taboo practice but a powerful tool for delivering genuine, targeted value. 

By harnessing data and AI, companies can now create outbound campaigns that resonate with developers' specific needs and interests, transforming what was once seen as intrusive into a welcome source of personalized support and information. 

This shift marks a new era in developer engagement – one where marketing efforts are not just tolerated, but potentially eagerly anticipated by the developer community.

Developer-Led Outbound, Done Right

Developer outreach has entered a new era, demanding a delicate balance between attributable strategies and respect for developers' preferences.

While DevTools companies need measurable results, developers still possess a finely-tuned "bullshit sniffer" and favor touchless experiences. The key to successful outbound marketing lies in delivering genuine value. 

Below are few creative plays we’ve tested with our portfolio companies and proved to be successful.

1. Programmatically identify and flag bugs in GitHub open-source projects

One innovative approach is programmatically identifying and flagging bugs in GitHub open-source projects. This method is centered in showcasing technical expertise while offering practical assistance. It’s an hyper-targeted strategy provides immediate value, potentially opening the door for further engagement. 

This play could work specifically well for companies like Snyk that focus on finding vulnerabilities on projects and granting security.

1. Create a list of relevant GitHub repos with tools like Clay or Apollo

2. Write an AI prompt to scrape the code within these repos and identify “common” bugs

3. Extract emails of developers working on these repos

4. Craft an AI-generated message to flag the bug and offer a solution to developers

Bonus: tailor the prompt/flag based on the dev activity/contribution to the repo, or on their role within the repo (IC vs. manager)

Then, if developers respond positively, a follow-up message can introduce your product as a long-term solution to prevent similar issues. 

The crucial factor? Ensuring the identified bugs align closely with the problems your product solves. By focusing on relevant issues, you demonstrate not just technical acumen, but a deep understanding of the developers' specific challenges and how your solution addresses them.

2. Scrape Fortune500 company's T&Cs to identify back-end stack and suggest 10X better alternatives

If you’re targeting enterprise customers, some of their GitHub might not be open source, making the play above more challenging to implement at scale.

If that sounds like you, then another play to elevate your developer outreach is by tapping into an often-overlooked goldmine: a company's terms and conditions. This creative approach allows you to uncover valuable insights about the backend technologies used by the company (they have to display it by law!) and position your product as a superior alternative. Here's how to execute this strategy:

1. Compile a list of target companies from LinkedIn

2. Use AI prompts to analyze their T&Cs, returning their current backend stack

3. Write another AI prompt to identify competitors' tools within the identified stack

4. Source developer contact information from these companies

5. Based on the above, program the AI to extract how the target prospect is using their current alternatives product so that we can position and suggest specific use cases where our product is better

Bonus: Inject a programmatic case study from a similar company to showcase your industry expertise and credibility. 

This method not only demonstrates your technical research and acumen, but also your ability to provide tangible value from the very first interaction. By offering actionable insights based on their specific tech stack, you're not just selling a product – you're presenting a solution to challenges they may not even realize they have. 

This targeted, data-driven approach transforms cold outreach into a compelling conversation starter, setting the stage for meaningful engagement and potential sale.

3. Compare site performance with public tools like PageSpeedInsights

The last play we’re sharing is leveraging PageSpeedInsights to deliver competitive edge insights. This strategy transforms cold emails into valuable, action-oriented communications. This play was inspired by Vercel's successful tactic of measuring page load times and offering performance-boosting solutions, providing clear, tangible value from the first interaction. 

1. Depending on your target segment, you can build a target company list from:

   • Global 1000 API or LinkedIn, if you’re selling to mid-market and enterprise with an established social presence

   • Crunchbase API, if you’re selling to startups who’s recently raised a specific stage or amount

2. Use an AI prompt to identify each company's key competitors

3. Craft a specific API call that can deliver you PageSpeedInsights for both your target company and their competitor, returning both scores and areas of opportunities

4. Source developer contact information from target companies

5. Craft AI-generated messages presenting both sites' results and specific, actionable insights for the developer to action

By presenting a side-by-side comparison with competitors and outlining concrete steps for improvement, you position your outreach as a strategic asset rather than a sales pitch — something developers tend to appreciate!

Again, this personalized method not only demonstrates you’ve done your (technical) research) but also your commitment to helping developers achieve measurable improvements in their day to day. It's a powerful way to initiate meaningful conversations.

Conclusion

The future of developer GTM lies in the strategic integration of hyper-personalized data within developer-led communications.

This powerful combination creates a comprehensive approach that not only fosters community but also drives measurable business outcomes. By adopting value-additive outbound strategies, companies can effectively engage and convert developers, transforming traditional marketing efforts into welcomed, personalized interactions. 

Here's why this matters:

• Demonstrable ROI: Measurable, attributable marketing efforts provide clear insights into campaign effectiveness and contribution to business growth.

• Trust-Building: Value-driven outbound strategies, tailored to developers' specific needs and challenges, build credibility and foster long-term relationships.

• Conversion Power: Personalized, insightful outreach not only captures attention but also increases the likelihood of converting developers into loyal customers or advocates.

By embracing this integrated approach, B2B marketers can navigate the delicate balance between providing value and driving business results, positioning their products as essential solutions in the developer's toolkit while respecting the unique preferences of this discerning audience.

Delivering the right message to the right person at the right place is the holy grail for today’s GTM teams. The alignment between your audience and offer, which hinges on the demand and supply dynamics, is the core of modern marketers’ strategy.

Given that 75% of today’s B2B buyers prefer a touchless sales experience — without talking to reps — and one that is personalized to them, signal-based interactions are becoming increasingly important for reaching audience/offer fit, and building converting pipeline.

In this post, we’ll articulate an end-to-end playbook to use intent signals in your go-to-market, so you can maximize audience/offer fit, and drive pipeline with higher conversion rates.

1. Understanding signals — what and how they make your GTM motion “intelligent”

First off, let’s start with basic definitions.

• Signals: events that a user takes (eg. visited your pricing page, performed an action on your product, interacting with a social post, etc.). These are helpful for surfacing who is in the market for your product and their propensity to buy.

• Attributes: traits that distinguish high-intent users regardless of their signals (eg. their role, seniority, the funding or n. of employees of their company, their industry, etc.). Attributes tell us if they would be a fit to buy, even if they performed a high-intent signal (eg. can they pay us or are they an intern?).

Ultimately, the combinations of both signals and attributes make up the audience circle in the above Venn diagram. Gathering signals and attributes helps you align your offer with your audience's needs and expectations — that’s what makes your GTM “intelligent”. You sell only to people in the market, driving more pipeline, and increasing your focus and conversions.

To get the best results, you have to maximize the overlap between your audience (the signals generated by high-fit buyers and customers) and your offer (the relevance of your response to those signals). Capturing buying signals allows you to do three things:

• Capture: collect signals that are highly correlated with buying intent for your offer

• Identify: identify people who perform those signals across multiple channels

• Enrich: check whether these people match your ICP, revealing who’s in the market for your offer at this moment in time (or who’s about to be)

• Engage: depending on the signal — which corresponds to a specific intent stage and buying maturity — personalized your offer, the message you’re going to engage them with, to graduate them to the next buying stage.

2. Select Signals — Identify data source and related signals

Choosing the right sources for gathering signals is the first step to an intelligent GTM. 

There are three main signal categories:

1. First-party signals (contact level, owned) — data collected and owned directly by your product and SaaS. Examples include:

• Website (eg. visited case study or pricing page)

• Product (eg. signed up for a trial or activated) — especially relevant for PLG/PLS

• CRM (eg. booked a meeting, closed a deal)

• Email automation SaaS (eg. attended a webinar, subscribed to the newsletter)

• Support SaaS (eg. created a support ticket, filed a complaint)

• Chat SaaS (eg. interacted with a chatbot, left 10 messages in chat)

2. Second-party signals (contact level, unowned) — data collected by core platforms where your audience spends time on. Examples include:

• Employment data (eg. champion changed job and posted about it on LinkedIn)

• Social media activity (eg. reacted to a company or competitor's post)

• Community activity (eg. joined a Reddit community or Slack channel)

• Developer activity (eg. submitted a PR on a repo)

As you might have noticed, first- and second-party signals are almost always at contact level. That is, we can reveal the person — their email, name, company, or some unique ID — by enriching the signal.

3. Third-party signals (company level) — specific data identified by third platforms that, when combined with first- and second-party signals, can be highly indicative of audience/offer fit. Examples include:

• Job listings (eg. target role opened on job listing platform)

• Technographics (eg. new integration added, new SaaS acquired)

• News & Events (eg. new product launched, new key hire or fundraise announced)

• Keyword consumption (eg. content on related keywords consumed)

• Reviews site activity (eg. visited competitor page/pricing on G2)

The main difference between first-, second-, and third-party is that the first two categories happen at a contact level, whether third-party ones relate to miscellaneous news about their organization.

Knowing which signals are important and why they determine a high audience/offer fit goes hand in hand with your targeting strategy. Make sure you have that strategy intentionally laid out instead of just “brainstorming” a spray-and-paint approach to signals.

If your targeting strategy determines your ICP attributes, then your T-shaped funnel will inform which behavioral signals you need to capture.

3. Map Signals — The T-shaped funnel and divergence point

Now that we understand which signals are out there for us to collect, the next step is to map them out on the customer journey and prioritize how we collect them. Let’s do that by rebuilding our buyer’s funnel.

Modern SaaS buyers’ funnels are T-shaped — they’re multi-faceted when you’re in the nurturing stage, however they then become more linear.

• T-base layer: when a buyer is in the base layer of the T-shaped funnel, they perform events across multiple channels — examples include getting a new job, hitting a feature gate, visiting a pricing page, signing up for a product, attending webinars, engaging with competitors on social media, and more.

• T-bottom layer: when they request a demo, the journey starts to become more linear, with consequential steps like committing to an opportunity, a proof of concept, a procurement review, and then a signed agreement.

Divergence typically occurs when a buyer's interest is high enough to agree to a conversation, and they book a demo with the sales team. This is the key signal and inflection point — it causes the action to switch the funnel to fluid and horizontal to linear.

From that moment onwards, uncovering signals more signals becomes critical to converting pipeline into revenue. Before that inflection point, signals-driven GTM must prioritize the identification of who’s in market (audience), and nurture them to book a demo.

Here are key steps to get you started on this exercise.

1. Map out the stages from signal to close.

2. Identify your conversion point depending on your growth motion (eg. signup for PLG or demo for sales-led). 

This is the divergence point when the funnel goes from multi-faceted to linear. It's usually when a signal is acted on and the next step is a discovery meeting booked.

3. List all signals leading to this divergence point.

4. Note the volume, cost, and conversion rate of each signal.

5. Identify the top-performing signals.

4. Score Signals — Prioritize signals correlated to pipeline

Not all signals are created equal, and not all of them are indicative of the same audience/offer fit. Once you have identified the right signals across your T-shaped funnel that will help you determine your audience/offer fit, you need to analyze your historical data to pick which signals are more correlated to pipeline.

The goal of this step is to build a scoring system that weighs your signals based on:

• Volume/Frequency: how often a signal happens. The higher the frequency, the more likely that a specific signal is associated with a top-of-the-funnel buying stage.

• Impact on conversions: how predictable the signal is to getting someone to the next step of the journey, especially given that the linear stages of the funnel convert at similar rates. For example, pricing page visit or a job change might convert to a demo at different rates, but once they do, bottom-layer funnel conversion rates are more predictable.

• Signal difficulty: how difficult is it for you to execute this signal to personalize your outreach?

This scoring system helps you prioritize which signals are determining a higher audience/offer fit. Start collecting/analyzing high-conversion, low-frequency/volume signals. Then, work your way up on the higher-frequency/volume signals depending on your ICP targeting.

Then, group signals in the same category or data source together as they might be easier to collect and cross-check insights. For example:

• If you know that “workspace with enterprise integrations” and “workspace with 3 seats” are indicative of high audience/offer fit, then you might want to prioritize collecting these signals.

• Or, if your historical data shows that prospects that “followed a competitor” and also “reacted on your LinkedIn post” are more likely to book a meeting, then it becomes key to prioritize collecting these signals together.

By the end of this phase, you should have a model that informs your team which signals are linked with a higher audience/offer fit.

Use this framework to build your model and identify your magic quadrant

5. Execute Signals — Use signals to personalize your outreach strategy

Now that you’ve collected and scored the signals that have a higher correlation to conversion, you can use them to personalize your demand gen programs and send the most relevant messages to the right people at the right time.

Again, you need to prioritize creating these personalized plays depending on your targeting strategy and the signals that have a higher correlation to audience/offer fit.

For example:

• If your top signal is someone visiting an integration page on your website, your email may include a customer story that details how that integration is used.

• If the signal is product users hitting a usage threshold on their current plan, your outreach may include an offer to walk users through different plans before they lose access.

• If the signal is an economic buyer engaging with one of your competitors online, your outreach may include a comparison of your two solutions or user reviews that highlight the differences between your products.

6. Automate Signals — Progressively scale your signal-based outreach

Once you lock down your initial signal-based outreach plays, you can use them to progressively automate your outreach personalization.

A helpful framework to scale is the Paul Graham’s Crawl, Walk, Run

• Crawl: start manually

• Walk: progressively automate

• Run: automate the end-to-end process

This doesn’t just ensure that the messaging remains targeted, but also that you don’t over-invest. Let’s look at examples for each stage:

Crawl — manually capture, enrich, and engage

The goal is to first start scrappy and validate a hypothesis before investing resources for scale. Starting with just a spreadsheet, an internet connection, and an email client is often all you need at this stage. It's tedious, and it should be, because you need to go slow before going fast.

1. Find a LinkedIn influencer who covers content about problems your product can solve. Use tools like Taplio, Kleo, or just a good old LinkedIn search.

2. Manually review commenters that fit your ICP, and copy-paste them into your CRM.

3. Send them a connection request with a note, DM, or email leading your message with reciprocity about the influencer’s posts; then, layer your value prop as a segway

Walk — automatically capture and enrich, then manually engage

Once you’ve learned which signals are starting to work for you and you’ve defined 3-4 successful GTM plays, you can introduce automation to capture signals. You can still keep humans in the loop to review the personalized outreach before sending.

1. Taking the use case from above, we can the CommonRoom Chrome extension to automatically identify people who engage with a list of relevant LinkedIn influencers.

2. Automatically filter commenters that match your ICP, then enroll them in a specific segment that your reps can engage with.

3. Start from a custom template, then manually personalize your outreach message depending on their comment, topic, or specific influencer they’ve interacted with.

Run — automatically capture, enrichment, and engagement

When you achieve confidence and repeatability at the walk phase, your signal-based engagement can also be fully automated.

1. Use CommonRoom to automatically capture signals of people who engage with a list of relevant LinkedIn influencers

2. Automatically filter and enrich commenters that match your ICP, then enroll them in a specific segment that your reps can engage with.

3. Automatically push your tailored email messages to your email sequencer like Outreach or Apollo.

7. Stack Signals — Layer use cases for signal-based outreach

When you reach the Run stage across multiple signal-based GTM plays, you’ll eventually reach a plateau of pipeline growth. It’s not a bad problem to have, but when that does happen, you have two options:

1. Start running tests to optimize your messaging for existing plays

2. Creatively combine/stack multiple signals to uncover new plays

Example signal stacking A: LinkedIn post engagement + website visits

• LinkedIn post engagement and web visits — when stacked together — can signal a prospect's interest in your product or category.

• A LinkedIn post engagement, whether from an employee, advocate, or competitor, indicates that the prospect was compelled to check out your website.

• Stacking these signals suggests the prospect is beyond the awareness stage, either feeling a pain point acutely or actively looking for solutions.

Example stacking B: 10-k earnings initiative + job change

• A job change often signals upcoming changes in technology, team, or strategy within a company.

• An earnings report (10-K) provides insights into a business's financials, risks, priorities, initiatives, and investments.

• Stacking job changes with 10-K initiatives identifies individuals as change agents likely to make an impact on company objectives listed in the earnings report.

• Combining these signals in an outbound message shows that you have done your homework, understand the situation, and can help achieve company objectives.

Closing Thoughts

Signal selection, execution, scaling, and stacking are essential components of an intelligent GTM that drives a qualified pipeline while optimizing the focus of your resources. 

Aligning the right message with the right audience is the underlying principle of an intelligent GTM that every modern marketing and sales team should pay attention to.

A signal-based approach is the secret to enabling this alignment, leading not just to increased meetings booked, but also a more efficient pipeline and a better relationship across marketing and sales teams.

However, many startups struggle with designing intuitive navigation structures. Common issues include overly complex menus, poorly organized content, and a lack of clear pathways for different user personas. These problems can frustrate users, causing them to abandon the site and, ultimately, the product.

So, how can startups create a navigation structure that is both simple and effective, catering to the diverse needs of their users?

Gonto provides principles for crafting effective website navigation. Drawing on insights from top DevTools websites and practical examples, Martin outlines strategies and tactics that can help startups design navigation systems that enhance user experience and drive engagement.

1. Design the Homepage for All Users

Your homepage hero should cater to a wide audience, including developers, and decision-makers from potential customers. The navigation is critical, as it is the first thing people see and interact with. Therefore, when designing the homepage, it should be structured to ensure that it addresses the needs of everyone who visits. This means providing a comprehensive overview of your company's work while directing users to specific sections tailored to their unique roles.

The idea is that the hero section targets everyone broadly  —  both developers and directors of engineering, or any of your target roles within the organization  —  but each subsequent section should focus on ONE specific persona. 

For example, If you target developers, there must be a section that includes code. The higher up, the more you show you care about them; because they’ll see content that includes code snippets and API calls, demonstrating that their needs are understood and prioritized. Then, the following sections might focus on high-level benefits and more relevant features for decision-makers.

Stripe’s homepage exemplifies this approach beautifully. It is designed to speak to a broad audience by showcasing how the app looks and works, which appeals to a general business audience. It also includes sections highlighting specific functionalities and code examples, catering directly to developers. 

The inclusion of interactive elements and animations engages users, while different sections focus on varying user needs — from detailed product information to high-level overviews suitable for directors and managers. By ensuring that every part of the homepage is tailored to a different user group, Stripe effectively communicates its value to all visitors.

2. Show Programming Language and Framework Fit

Displaying which programming languages and frameworks your tools support is crucial for developers. From my experience, 50% of a developer tool’s Product Market Fit is Programming Language and Framework fit; so you better show it, high on your homepage!

Developers buy products so they don't have to implement them themselves and do it faster. Having a good Programming Language and Framework fit makes them feel a lot more confident that they'll be able to accelerate using this product. Whether featured in the navigation or prominently on the homepage, this detail can significantly enhance your conversions.

Developers primarily care about two things once they understand the cool features of your product: compatibility and ease of implementation. They want to know if your product works well with the specific programming languages and frameworks they use. Examples of companies that do this well include Algolia, Vercel, and Strapi.

For instance, if you visit Vercel's website and scroll through its pages, you'll find templates for technologies like Svelte, Next.js, and others, clearly showcasing the supported frameworks. On their rendering page, Vercel uses logos of the technologies, which developers instantly recognize, to indicate compatibility.

Similarly, Strapi's website prominently displays the technologies it supports as you navigate through the homepage. This visibility ensures developers understand which frameworks and languages are compatible with Strapi from the get-go.

Algolia also excels in this area. Despite having a more enterprise-focused site, Algolia lists the main technologies they support, showing how their product integrates with various frameworks. This approach reassures developers about the ease of implementation with provided SDKs.

Including this information either on the homepage or in the navigation helps clarify your product's focus and compatibility. It's also beneficial to demonstrate how easy it is to implement your product with SDKs. Algolia, for example, effectively shows implementation details, helping developers visualize how they can use the product within their tech stack.

3. Product or Platform Sections Target Developers

When designing your menu navigation, you must always include a section specifically labeled "Product" or "Platform." While the terminology may vary, the focus should remain the same — targeting developers and showcasing your product. This section should never be aimed at decision-makers but should instead cater to the quick-scanning nature of developers. They need to understand at a glance what your product offers.

As we mentioned before, developers often "hover" over website sections, looking for specific technical details rather than reading through extensive text. I’ve learned about this behavior from watching hours of FullStory and Hotjar web recordings. 

It’s also because devs are productivity- and time-obsessed, and they hate to switch between mouse and keyboard. That’s why developer-first tools like Raycast or Linear have built-in keyboard shortcuts

This behavior underscores the importance of designing product sections that are clear, concise, animated; and provide quick links to essential docs and technical specs.

First, developers hover around the whole page to grasp the whole of the product and then dive deeper if they feel it's a fit for them. That’s why having a product or feature name with explanations hidden behind a click is not "hover" friendly, as you’ll force developers to click to learn more.

Strapi’s navigation is an excellent example of this principle in action. Their product section is meticulously organized to address developers' needs efficiently. Each product offering is accompanied by a brief description, allowing developers to quickly grasp what each component entails.

For instance, a glance at Strapi’s site reveals that they offer a community edition that is open source, an enterprise solution, numerous integrations through a marketplace, and a hosted solution. Developers can easily identify key features such as roles and permissions, content translation, and a UI builder.

4. Solutions Pages Target Decision Makers

Solutions sections on your website should be designed primarily for decision makers such as directors of engineering, VPs of engineering, and other similar roles. The key focus here is on relatability rather than discoverability. Instead of crafting content with the sole intent of making it discoverable in Google, or having some specific SEO Keywords, aim to make it highly relatable to the specific needs and challenges faced by these decision-makers.

When a developer sends a link to a director of engineering or a VP, the recipient should immediately see how your product addresses real-world problems relevant to their industry and use case. Again, this is not about having the right keywords for search engines, but about showcasing relatable examples and success stories that resonate with developers. Because developers want to feel that your product is tailored SPECIFICALLY for them.

Vercel provides a great example of this approach. Their solutions pages emphasize different business challenges tailored to their target industries, making it easy for decision-makers to see how the platform can benefit their specific operations. For instance, when you navigate to Vercel's solutions section, you can find examples such as support for Web Apps, platform engineering teams, composable commerce for e-commerce, building AI applications, and providing multi-site insights. This clear categorization helps potential clients quickly identify the value Vercel can bring to their specific context.

By highlighting how your product helps companies similar to theirs, whether through industry-specific solutions or use-case examples, you make it easy for decision-makers to envision your product as a fitting solution to their problems. Additionally, including a variety of specific industries and use cases can further enhance relatability and make your solutions pages even more compelling.

5. The Need for a Developer-Specific Section

When designing your navigation, an important question to consider is whether developers are your main persona. Based on this determination, you can decide whether to include a dedicated developer section in your navigation. 

If developers are your primary audience, particularly in the early stages of your company, it’s better not to have a dedicated developer section. This approach signals that your entire site is tailored to developers, ensuring they don't feel sidelined by having to navigate to a specific section just for them. Instead, content relevant to developers should be seamlessly integrated throughout your site, making it clear that developers are your main focus.

However, if your company has grown and moved upmarket, targeting enterprise clients alongside developers, including a developer section can be beneficial. This dedicated section provides a clear and convenient place for developers to find all the technical resources they need, such as documentation, APIs, SDKs, and other developer tools. It also indicates to developers that, while your site caters to a broader audience, their needs are still a priority.

Examples illustrate this approach well. 

LaunchDarkly effectively targets developers on its homepage, focusing on feature flags and continuous delivery without isolating developer-specific content into a separate section. 

On the other hand, companies like Storyblok initially did not have a dedicated developer section reflecting their primary focus on developers as their main audience.  Since then, it has evolved to include a developer section as it expanded its target market to include more enterprise clients.

In another case, even when the developer remains your main persona, you might "forget" about them as you grow and target enterprise customers. NewRelic is a good example of how they unconsciously forgot developers are the main persona and their entire website changed based on that.

For companies where the developer is not the main persona or where the focus has shifted, having a developer section helps organize the content. When a separate developer section is absent, it's common to have navigation items like "Docs" or "Resources," which developers can use to access the necessary documentation, APIs, and SDKs.

6. Maintain Transparent Pricing

Transparent pricing is a cornerstone of building trust with potential customers, particularly developers. When developers visit your site, they want to see clear, straightforward pricing information to quickly understand the cost and value of your product. Without transparent pricing, developers may become skeptical about your product's credibility and hesitate to adopt it.

It’s important to note that while you don’t need to display your enterprise pricing, you should at least provide clear details on your self-service or credit card pricing. This transparency reassures developers that your product is accessible and within reach, making them more likely to consider it for their projects. Pricing should be easily accessible from the main navigation, allowing developers to find this crucial information without hassle.

For example, Vercel’s pricing page is an excellent model of transparency. It clearly outlines the costs associated with different tiers of service, from free plans to premium options. This straightforward approach allows users to quickly grasp the value proposition and decide which plan suits their needs.

By placing pricing information prominently in the navigation, you ensure that developers can quickly access and evaluate it. This practice not only builds trust but also facilitates quicker decision-making, as potential customers can immediately understand what they are committing to financially.

7. Design as a Feature

The design of your homepage and website should be a standout feature in itself. A visually appealing, modern design can significantly enhance user engagement and perception, making your product more attractive to potential users. In recent years, the trend has leaned towards cool and futuristic designs, exemplified by websites like Upstash and ScaleAI, which use striking colors and sleek layouts to create a high-tech feel.

However, as this trend becomes ubiquitous, it may no longer differentiate your site as effectively. Instead, consider adopting a unique design approach that sets your site apart. For example, MotherDuck has successfully bucked the trend by opting for a vintage aesthetic. This distinctive design evokes a sense of nostalgia and uniqueness, making the website memorable and engaging for developers. It’s reminiscent of a spaceship or retro sci-fi theme, providing a refreshing contrast to the typical futuristic designs.

Moreover, it's crucial to embrace the principle of "show, don't tell" in both your navigation and homepage design. This means demonstrating how things work through visuals and examples rather than just describing them. For instance, use interactive elements, animations, and real-time demonstrations to show your product's capabilities and features in action. This approach is far more effective in engaging users and helping them understand your product's value.

Snyk’s homepage is a prime example of leveraging sleek, modern design to capture attention and convey a sense of innovation and reliability. By incorporating these design principles, you can create a homepage that not only looks good but also provides a compelling user experience.

Closing Thoughts

In conclusion, Gonto's advice on structuring website navigation for startups focuses on simplicity, product-focused organization, prominent use cases, clear visuals, skimmable content, interactive case studies, strong visual hooks, adaptability to different user needs, and consistency. These strategies help create a user-friendly and effective website navigation structure.

Guillaume Cabane on AI-Assisted Outbound & Targeting

One of the most impactful areas where AI is making its mark is in outbound and targeting. The tooling for these use cases has evolved significantly, and it’s becoming all the rage on our LinkedIn feeds!

1. Automate lead list building and scoring with LLM-assisted intent signals

SaaS such as Waterfall, Clay, and Common Room, are rapidly emerging to streamline the aggregation, cleansing, and enrichment of data before it's used in outbound campaigns. These tools leverage Large Language Models (LLMs) to scrape data from multiple sources and enable the use of AI with custom datasets to inform critical strategic aspects like TAM definition and lead scoring.

For example, with Clay, you can scrape LinkedIn or Google Maps to define your TAM, according to targeting criteria like Industry, company size, type, and location. Clay will give you a potential TAM figure straight off the bat, helping you define and prioritize your go-to-market strategy.

On the other hand, Waterfall helps you enrich your lead lists from multiple data providers at once without you needing to sign up for long-term, individual contracts to each data provider like Apollo, Lusha, or Zoominfo, allowing you to reduce your CPL and overall CAC.

These tools are becoming more and more critical to engage the right customers and improve conversion rates, especially in the current market landscape where capital efficiency is key.

2. Scale personalized outreach and growth ops with AI

Clay — together with Cargo and Unify — play also a crucial role in lead scoring, routing, and outreach personalization your outreach with the help of the data gathered from across multiple sources.

Clay, for instance, is invaluable for — including LinkedIn, Google Maps, and CRM systems, or by adding additional data vendors. By using an “Excel-on-steroids”-like UI, GTM teams can easily enrich these lists with additional data points such as LinkedIn engagements, Technographics, or competitors' actions.

Once you’ve built these highly relevant and segmented customer lists, Clay helps you use ChatGPT to create personalized copy snippets that you can add directly to your email template in Unify, Smartleads, Instantly, Apollo, or AmpleMarket, and drive reply and demo conversion rates.

Cargo helps GTM teams automate pipeline operations like adding custom Salesforce fields, pushing to CRM stages, or scoring leads with signals from across various sources. Whereas this might not be relevant for early-stage growth ops, this automation is particularly beneficial for scaling from 1 to 10 in growth and late-stage orgs (eg. Series B-E), where data collection on Ideal Customer Profiles (ICP), lead scoring, pipeline, and intent signals is readily available.

Players like Unify also show how players are increasingly verticalizing, closing the gap between accessing prospecting contacts, enriching them with signals, orchestrating them, and activating them by sending personalized emails at scale.

3. Increase sales efficiency

Switching gears from outbound and growth ops to sales, that’s when the role of AI in enhancing sales efficiency — namely the SDR team’s ability to book demos — cannot be overstated. 

At Ramp, SDRs are increasingly using AI tools like ChatGPT to craft personalized email drafts by identifying common traits and patterns among prospects, such as shared educational backgrounds or industry experience. This personalized approach has led to a 3x increase in response rates compared to fully automated outbound strategies, which adds credit to the fact that AI-powered teams are still better than standalone AI. 

Another example of AI empowering human performance lies in audio-based solutions like Orum. Orum is revolutionizing salesfloors that reach customers via phone, by rethinking the dialing process from the ground up. Teams using Orum can use AI to boost sales efficiency up to 6x, scaling the number of daily customer conversations. With Orum, your team could:

• Automate the dialing process, by calling your entire CRM without teams having to touch a dialpad.

• Detect voicemails vs. human voices, increasing call coverage and SDR efficiency dramatically.

• Productize objection handling from calls, feeding back into CRM and pipeline generation, and product roadmap, ultimately distributing more insights across the organization.

• Integrate Orum with CRM and sales intelligence tools like Gong also helps make strategic decisions around rep performance visibility, coaching, and hiring.

• Save many hours on logistics that can be allocated to prospecting, customizing emails, and collaborating with Marketing and Product to refine the GTM.

On another note, what doesn’t work yet is fully AI-based SDR voice agents like air.ai — we’ve tried it, and the AI is not there yet.

Rex Gelb on AI-Assisted Paid Marketing

Paid marketing is another domain where AI is driving significant advancements.

1. AI targeting and bid optimization is finally thriving

For example, Google Ads Performance Max utilizes LLMs to optimize bids across channels like Search, YouTube, and Display, enhancing spending efficiency and targeting precision. The evolution of LLM-driven bidding optimization — which now ingests billions of data points every minute — is a testament to the advancements in AI technology, which now allow for more sophisticated and effective campaign management than was possible with earlier iterations of the tool.

Before LLMs, teams used to segment ad campaigns by various customer attributes like gender, interests, geolocation, product, etc. With LLM, targeting is getting so much better at showing the right creative to the right customer segment that you’re better off setting the budget at a campaign level using features like Advantage+ on Meta instead of manual ad-set-level targeting.

Many could ask — what’s the minimum conversion data needed to leverage these AI capabilities effectively? According to Rex:

• For Meta, the best practice is around 50 optimization events within a 7-day period. 

• For Google, you should have a minimum of 15 conversions over a 30 day period. 

If your budget can support generating such conversion volume, then you’re all set. Teams should approach minimum conversion volumes as a curve rather than a step function; the more data the better. 

Furthermore, the incorporation of intent signals into broad match queries by LLMs has increased relevance and conversion rates by up to 40% in comparison to traditional exact or phrase match types. For example, if you hire a celebrity called “Naomi Campbell” to promote your product, your product will never show up for board match queries like “Naomi Campbell”. However, the latest LLM improvements to broad match terms will scout for relevant intent signals and show your product ad with Naomi Campbell when those signals apply.

To Rex’s surprise, the AI promise made by these tools was not delivered just 6 months ago, while it is very much exceeding expectations now. This shows how fast the AI space moves, and how much incremental improvements compound. That’s why teams experimenting with AI tools must incorporate frequent re-testing to re-validate their potential. Ultimately, teams that won’t are the ones who’ll miss out!

2. AI Chat Ads are Coming

Innovations in ad placement continue to evolve, with Bing testing new AI chat placements that allow advertisers to show ads in Bing Chat results. As AI chat is eating into traditional search behavior at a fast rate, ad platforms are racing to roll out these new experiences.

As advertisers, the competitive advantage is on a first come first served basis, but it gives you the ability to streamline the discovery and comparison process greatly, simplifying complex purchases like B2B SaaS into a more natural and linear process that is done into one, chat UI. These interfaces drive accessibility and potential for PLG motions, where the buyer can self-educate and self-select the best software to buy based on their situation.

3. AI-powered creative creation and optimization

Additionally, both Meta and Google Ads enhance creative outputs with features such as automatic video creation, cropping, and cross-placement optimizations. This can be super useful for small and/or busy teams that don't have or have limited creative resources in-house.

“Major AI innovations in performance are happening at an ad network level, because these platforms own user attention, the data sets that inform LLMs, and the funding to significantly improve the machine learning process. By fall 2024, AI-powered advertising will be wild.”

Closing Thoughts

In conclusion, integrating AI into growth strategies not only enhances operational efficiencies but also fosters innovation and competitive advantage. 

By understanding and applying these advanced AI use cases, teams can effectively harness the power of AI to scale growth and reduce overheads, ensuring they remain competitive in an increasingly AI-powered market.

Back in 2022, Gong used LinkedIn to make the company look ahead of where it really was, leading it to dominate the Revenue Intelligence category, which at the time was still in the making. 

Gong needed their audience to be comfortable with recording sales calls — and doing so with Gong over other alternatives — so LinkedIn helped make sales call recording and analysis desirable for sales leaders and positioning Gong as the leader in that space.

During the COVID years, Gong created the playbook of LinkedIn takeovers, with their CMO Udi Ledergor and CEO Amit Bendov becoming the archetype of founder-led marketing on LinkedIn. They did so also out of the realization that Gong’s audience (sales and marketing teams) — ”live” on LinkedIn. That’s why it’s key to go all in on the platform and build a native LinkedIn brand.

In this post, we’ll pull the curtain on Gong’s playbook and speak directly with Jonathan Costet, who built and executed Gong’s LinkedIn playbook back in 2020, growing the company page to over 200,000 followers in just over 2 years.

Let’s dive in!

1. Encourage Employees to Takeover LinkedIn

Gong's prime social strategy stood out for its LinkedIn takeovers. They managed to mobilize employees across every level and team to consistently post on LinkedIn, literally taking over the platform and creating mindshare. This was a great tactic to scale the brand’s organic mentions while decentralizing content creation efforts beyond just the “executive thought leaders”.

Jonathan and the team were smart to root these takeovers into employees’ peak engagement moments. For example, they encouraged new hires to share a post about their first week at Gong. By coordinating tens of these posts to go live on the same day, they gained momentum around the LinkedIn algo while ensuring the content was genuine and resonated with the audience.

This strategy wasn't limited to new hires, though. Gong capitalized on every pivotal moment of their story — company retreats, events, new content, product launches, end-of-financial years, and fundraising. The more moments they could leverage and get the entire org to create content, and tell their story and unique POV, the bigger their mindshare became.

Given the higher reach that the LinkedIn platform would give to personal pages over company pages, Gong was able to drive awareness beyond anything that could have been achieved from the corporate page alone.

Folks seeing these takeovers asked Gong’s CMO: “How do you do this? All I can see on my feed is purple”. He openly stated that without these takeovers, they’d have never built this million-eyeball reach.

Another key aspect was focusing solely on LinkedIn instead of following a cross-platform approach. This focus not only streamlined the takeover process and aligned the team’s efforts but also recognized LinkedIn's unmatched value in connecting with Gong’s target audience — B2B sales and RevOps leaders.

So how did they manage to create such a muscle and encourage people to post? First, they made it clear in their hiring process that LinkedIn content and personal branding are key company values and activities of the day-to-day. That:

It’s not just Marketing’s job to post on LinkedIn, but their core responsibility is to train and empower everyone to do so. Because when everyone spreads the word about Gong’s mission, everyone gains.

Things like drafting prompts, adding reminders on everyone’s calendar, hosting content enablement sessions, and sending 1-to-1 and 1-to-many Slack messages all drove bottom-up participation across the org. From the top down, managers encouraged their teams to engage consistently, reinforcing a culture of personal branding; and also leading by example by creating their content.

Executing this strategy early on in Gong’s journey paid off as they scaled. On the one hand, it trained the first cohorts of employees to become ambassadors who eventually turned into several LinkedIn micro-influencers with tens of thousands of followers each. On the other hand, it created a virtuous cycle that attracted talent who understood the benefits of content creation and personal branding, scaling this muscle into a proper internal UGC loop.

According to Jonathan: “This didn't happen by design, but it was a by-product of the strategy being so successful”.

2. Embrace Thought Leadership from Multiple Layers

To effectively encourage the whole company to regularly create content, you must have your entire executive team lead by example. 

Thought leadership shouldn't rest solely on the CEO's shoulders. Each thought leader can reach and influence different layers of the target audience in their unique ways — be it the CMO, CEO, Head of Content, BDRs, and Sales leaders — by articulating nuanced insights and POVs from their day-to-day work. Every one of these perspectives helped expose Gong to a broad audience and showcase the value of different use cases/personas

Realizing the potential of a brand with cross-layer thought leadership, Jonathan deeply involved the whole executive team in their content strategy and trained them to generate and formulate opinions around their areas of expertise, passions, and hobbies, to create content and make it an integral layer of the overall strategy.

This is a great tactic to maintain the momentum of the LinkedIn takeovers, leading by example with the wider organization. But also, it’s a strategy to shape more than one conversation across different levels of seniority within the target function — C-level, head of department, and ICs — to reach more segments of your target customer and influence buying decisions from different angles.

When you’re considering purchasing Gong, and you need to get buy-in across champions and economic buyers, it helps that each of these actors already knows who their Gong’s counterparts are, because they see their marketing and sales leaders sharing insightful stuff on LinkedIn. It builds affinity and trust, which influences purchasing decisions.

3. Build a Company Page with a Unique Brand Voice

LinkedIn takeovers and thought leaders accounts were complemented by a clear strategy for the Gong’s Company Page, which boiled down to one keyword — edutainment, or a combination of entertainment and education.

If you’re around LinkedIn today, it’s common to see creators posting insightful flowcharts on one day, and a funny meme the following one. That’s edutainment or zero-click content. And Gong was nothing short of a pioneer in executing this.

How do you build a brand voice? You think of it as a real person. What are they interested in? What would they talk about? What would they sound like? Which keywords would they use? Based on these answers, Jonathan and the team devised specific topics and copywriting styles to create Gong’s unique brand voice, including things like opinions, catchphrases, use of caps, and line breaks.

The team did a great job at optimizing the company’s page content balancing familiarity and consistency on one hand — so that even if you take your company’s logo out, people will still know whose post it is — while testing new topics and content that drives novelty and shapes new conversations.

To appeal to the LinkedIn algo, every post across all account types is optimized for short-form, and aimed to keep conversations on LinkedIn, keeping the engagement within the platform rather than redirecting traffic elsewhere, like we see these days with the typical “link in the comments”. This small tactic compounded the brand's reach and engagement organically.

4. Complement with Conversion-Focused Ads

The final component of the strategy was amplifying organic reach with conversion-focused paid campaigns, which can more effectively target elusive decision-makers like CROs and VPs of Sales.  

With organic providing millions of dollars worth of reach and awareness, the Gong team was able to focus the budget on demand gen, engaging decision makers at top accounts with a mix of content/product offers.

They set up their creative strategy to address the most painful pain points of the more senior target, and then deliver a compelling call to action to close the loop. These include whitepaper downloads, recorded demos, and product tours via Navattic, as well as free trials and demo signups.

Additionally, the campaign ensures that the creative visualizes the customer's pain points and hints at how the product UI could solve them. The Gong’s team was also one of the first beta testers of a killer LinkedIn Ads feature that is becoming popular these days — thought leader ads. This unique feature boosts organic posts with LinkedIn-level granular targeting. Another great tactic to reach the right people, compound social proof, and scale influence.

Unlike other channels, LinkedIn is uniquely positioned to drive high-value leads at hard-to-break-into accounts. By driving awareness through organic posts, the paid team was able to focus exclusively on prospecting into key decision-makers in top accounts.

Closing thoughts

To dominate on LinkedIn, companies must craft a unique brand voice, engage authentically with their audience at scale and from different perspectives, and seamlessly integrate brand awareness with conversion-focused demand gen.

By focusing on quality content that educates and entertains, businesses can scale organic reach and build a brand that stands out from the crowd. And then use paid marketing to drive highly targeted MQLs from decision-makers in key accounts.

As we all know, LinkedIn can be a powerful growth engine much more than a social platform. Gong’s unique approach shows in detail how your company and team can do the same, fostering lasting connections with key decision-makers, and driving pipeline.

Once just an abstract concept, electricity soon lit up our cities, extending our time, powering our machines, and revolutionizing how we live and work. This transformation didn't happen overnight; it took visionaries like Franklin, Volta, and Faraday who saw beyond the immediate thought experiment to its vast potential across industries. 

As we stand on the brink of the generative AI era, we're at a similar crossroads. Just as electricity found its way into every corner of our lives, AI will permeate every industry, offering myriads of applications that are already redefining our world before our eyes.

Founders and CEOs are daringly exploring how AI can not just automate but reinvent their operations, with startups emerging at every layer of the stack — from silicon to compute and cloud providers, foundational models, infrastructure, LLM Ops, and horizontal and vertical application platforms.

The AI rally shows in the investment landscape, with $21 billion funneled into 400+ Gen AI deals in 2023. A staggering 75%+ of these ventures are early-stage or bootstrapped; another signal of the vibrant AI ecosystem. This momentum is expected to not only continue but accelerate into 2024, as both established tech giants and startups vie to capitalize on Gen AI's vast potential.

The Hyper-Competitive AI Market Calls for Differentiation

This rapid emergence of companies and products across the Gen AI stack presents a significant challenge to emerge in a hyper-crowded and hyper-fast-moving market. The urgency to differentiate and communicate value becomes more paramount than ever. To put additional pressure on founders, the timeline to achieve product-market fit and demonstrate customer traction has significantly compressed, forcing companies to prove their role and secure a foothold in the landscape, as waiting for full product functionality is a luxury no one can afford

In the last few weeks, several LLMs have launched both open- and closed-source from existing or new players, each outperforming others. Similarly, vector databases emerged as a new category last year and now there are several new entrants and existing database players entering the market making it even more competitive.

As the stack continues to evolve rapidly, the market gets more crowded, and as you move up the stack you start facing both new entrants building those capabilities and big tech incumbents expanding across the stack.

These challenges make it critical to be a top-of-mind brand in a market that changes daily. Unless you are a top-three in your category and are growing your mindshare with your audience, you won’t survive long. With the pressure to demonstrate unique value before reaching PMF, and continue to scale revenue after initial traction — or a new funding round — the question arises. 

How can early-stage AI startups emerge amidst the noise and become category-defining leaders? What strategies should they invest in to conquer their market share, ensuring they drive recognition and adoption amidst a sea of competitors?

It's Time for a Paradigm Shift

Traditional B2B SaaS products follow a sequential path: defining features, building the product, testing with users, and refining based on feedback. Marketing often plays a catch-up role, promoting a finished product once it has found early traction, and there’s already a sales team craving for leads.

This just won't fly with Generative AI, and here's why; the entire landscape — made of the product, the technology, the target market, and user problems — is in constant flux. 

Every week brings breakthrough capabilities and new applications that reshape what’s been on the market until yesterday. Products become obsolete in a matter of weeks, or if you are lucky, a few months. 

In this dynamic landscape, the role of marketing must fundamentally shift. 

• Marketers can no longer wait for the finished product — they must be at the forefront, articulating the company's bold vision for the future, positioning the organization as a category leader, and educating the market on the transformative potential of this technology.

• It's also about crafting a crisp narrative, establishing differentiation, and building the brand of the people behind this vision and the company driving it. In the generative AI space, marketing isn't just about what's on the truck today, but about defining the curve, convincing the market why your company is best positioned to lead the way, and guiding the product strategy to achieve that.

• Marketing needs to build a moat bigger than the product itself. To emerge in a hyper-competitive Gen AI market, founders must embrace a marketing-led approach.

Let’s dive into each aspect in more detail.

1. The Importance of Category Creation and Positioning in the AI Landscape

New AI categories are being formed and existing ones are being disrupted at a dizzying pace. The window of opportunity is narrow, but the potential upside is immense. Successful AI companies will be those that can clearly define the market category they aim to enter and lead, articulating a compelling, differentiated positioning.

This is where marketing takes center stage, spearheading the category creation process in lockstep with the executive team, product, and technology. Drawing on deep market and competitive intelligence, as well as direct customer insights, marketing must lead the charge in identifying the right category to target, crafting a compelling differentiated positioning, and articulating a solid case for long-term leadership.

Crucially, this positioning can't be static — it must evolve constantly, adapting to the market's changing needs and new technological breakthroughs. By shaping the product roadmap and ensuring the positioning is maintained, marketing plays a vital role in propelling the AI company to the forefront as an industry leader.

For example, Cohere has been successful in defining and owning the "Enterprise AI" category for LLMs. When I was leading marketing at Cohere, we did so by carving out a clear and differentiated positioning compared to behemoths like Open AI and Anthropic, establishing ourselves as the go-to provider for sophisticated enterprises. We’ve been consistent in our category messaging and leveraged all our announcements across funding, product launches, events, website, and partners to carve out the category and own it.

In this high-stakes arena, category creation has become marketing's driving mission. 

2. Simultaneous GTM Strategies

Historically, successful startups go to market with one motion — either PLG bottom-up or Enterprise top-down — and then layer the other one on top. However, for Gen AI companies adopting a singular go-to-market strategy won’t be enough. 

Effective market penetration now demands a new level of complexity — the simultaneous execution of not two but three concurrent go-to-market motions — bottom-up product-led growth (PLG), top-down enterprise sales, and partnerships — serving multiple segments at once. Each serves a specific purpose:

• PLG will foster brand recognition and technical credibility, with innovative products and research that will conquer the hearts and minds of the (non-) technical community, driving word of mouth and scaled feedback to influence the product roadmap and build the brand.

• Top-down enterprise sales and partnerships are critical for driving sustainable revenue and cementing credibility at the executive level. By securing large enterprise customers and forging strategic alliances, these high-touch channels ensure speedy market penetration that reinforces the brand at the top of the funnel while earning sustainable and scaled revenue. Specifically:

  • Top-down Enterprise. Every board is asking their CEO about their Gen AI strategy. Your AI company must be top of mind for the C-suite because AI is top of mind for them! They are the executive sponsor and will involve their AI/ML or engineering teams in the conversation because it’s among their top priority.

  • Partnerships. Every enterprise, system integrator, and cloud providers want to offer generative AI solutions to their customers. They are looking to partner with AI players either above or below their (level of the) stack of their core competency. This creates a unique moat opportunity for startups to get co-marketing and distribution muscle via these partners to cross-share customers with a joint GTM motion.

In this ever-complex GTM scenario, the challenge for marketing is clear: they must develop dedicated, coordinated strategies for each motion from the get-go. The stakes are high, but the potential rewards are transformative. By striking the right balance, AI companies can foster rapid adoption, and drive exponential revenue stacked across all the GTM motions.

3. Building Credibility Beyond the Product

In this accelerated generative AI market, the true trailblazers will be those who can shape — and own — the industry conversation way before their full product is ready on the market. That's because it's not only the funding cycle that's at a peak but the news cycle as well. 

That’s why marketing's role must transcend the traditional focus on creating positioning, awareness, and demand for your product among core buyer personas. To truly establish your company as an industry luminary and harvest credibility beyond your product, you need to cultivate relationships with a diverse array of influential stakeholders — AI policymakers, the press, research and developer communities, government leaders, and other key players shaping the generative AI conversation in your category.

It's no longer enough to simply promote "what's on the truck today", a.k.a. your product. Instead, marketing must take a bold, forward-looking stance, articulating your long-term roadmap and showcasing how your company's innovation will transform the future trajectory of AI. This is about shaping the industry narrative, rather than just reacting to it.

At Cohere, we did so by building a brand vision around the executive team. We’ve invested resources so that the CEO and the founding team could share their provocative take on the future of AI targeting specifically decision-makers of the sales- and partnership-led motions — C-suites, executives, and policymakers.

Specifically, we’ve secured speaking slots at the Davos World Economic Forum Panel and published in related publications to ensure that we got on the map of every single world leader and positioned ourselves as a cutting-edge company at the forefront of AI. We’ve completed this presence also by speaking at major events like Collision, National TV broadcasts, and major publications; every channel reinforcing the ever-evolving yet bold vision of the founders.

As mentioned before, Cohere’s corporate and brand communications were integrated by sharing upcoming products and partnership announcements ahead of time as “coming soon”, generating hype and solid brand awareness.

4. Every Marketing Functions Must Have a Growth Mindset

To stay ahead of this market, you need to find new creative levers all the time. You can’t just go through the B2B SaaS playbook. And you can’t just do this in growth marketing, but it becomes an integral part of all marketing functions, especially across Product Marketing, PR & Communications, but also Revenue marketing, and Developer Relations.

Traditional SaaS growth is applied to either bottom-up adoption or top-down demand generation. However, Marketing teams in AI-first companies will need to share a growth mindset across all their functions to meaningfully shape and influence their areas and contribute to category creation and leadership.

It won’t be enough to have the latest tools to create SEO content, demand through paid marketing or automate lead scoring. Growth Marketing and Growth Product teams won’t be the only ones to run AB tests and experiments. In this new game, even PR & Comms, Brand, Product Marketing, Partner Marketing, and Research must embrace the high-velocity, risk-taking attitude of growth teams.

Successful marketing-led motions will focus on extremely lean operations, velocity, tight feedback loops, and focus on concrete outcomes. These cultural defaults require teams with not just functional expertise, but think-on-your-feet, highly resilient, persistent, and adaptable talent. 

While the right talent mix will depend on your category, it could involve hustling your way to the White House AI forums, arranging a roadshow for your leadership and tech team to speak at important events, partnering with your research team to serve the community through open source products or building an entire educational program to create a Generative AI economy for developers. Or educating every reporter of top publications on your narrative to get press mentions every time your competitors are mentioned.

An effective tactic to go against the grain is crafting and owning controversial and memorable opinions around hot topics. Remember the “AI Doomsday” narrative? Leaders from several big AI companies signed a letter warning humanity about the “risks of AI”. However, Cohere’s founders didn’t share that narrative and worked on an opinion piece to counter the mainstream narrative and drive instead their ‘value additive’ vision — educating the market on what the industry should focus on instead. Now, that’s an innovative way to get your company on the map, own the narrative, and rapidly gain mindshare. 

Regardless of the tactics you embrace, everything you do should be focused on penetrating your target market, not following the herd on those topics. The best part is that these initiatives are completely independent of product readiness and new features; when the growth mindset is distributed across all your Marketing functions, you will even move faster, as there won’t be any execution dependencies on product or other teams. This is how you can make things happen.

It's Time for a Paradigm Shift

This requires the marketing team to place a few long-term bets on how the market will evolve and what will drive a sustainable long-term advantage. At Cohere, we’ve made an early investment in building the LLM University — an initiative to upskill developers on the technology, even before the ChatGPT hype stemmed, from the conviction about the skilled resource gap in the AI market. 

This not only boosted Cohere's technical credibility and drove huge traffic, but also helped accelerate the pipeline of potential customers. At some point, this investment alone generated 40% of all Cohere traffic!

Closing thoughts

To successfully stand out in the hyper-competitive Gen AI landscape, startups must embrace a new marketing-led approach, investing early not just in simultaneous GTM strategies, but in shifting marketing from a support and distribution function to a core strategy, where all its teams share a high-velocity, risk-taking growth mindset. This shift involves forecasting future industry directions, redefining product roadmaps through visionary leadership, and executing both bottom-up and top-down marketing strategies concurrently. 

Emphasizing marketing's strategic role ensures products remain relevant and competitive, driving rapid penetration and establishing a solid, beloved brand. This approach not only differentiates companies in a crowded field but also paves the way for sustainable growth and industry leadership.

This heightened complexity brings a burdening question. How do you execute multiple and concurrent GTM motions? And what resources do you need to pull this off? How do you hire a team that builds a category-defining AI startup?

Stick around to find out in the next posts of this series.

B2B growth strategies often differ significantly between Europe and the US, influencing multiple facets of the related markets, such as the number of growth stories, IPOs, but also talent.

It’s renowned that the European landscape of B2B companies is perceived to generally lag in the above metrics, as reflected by the lower numbers of European B2B SaaS going public or expanding to the US, compared to the larger American counterparts crushing the public markets and the European expansion. The same comparison is true when looking at the talent market enabling such innovations — the EU’s top talent market is much smaller, more inexperienced, and not rewarded for the long term.

Despite exceptions like Alan, Personio, Pigment, and a few more unicorns, these aspects make it challenging for EU startups to scale sustainably and across the entire market geography.

What is this lag attributed to? Does it depend on a more conservative, perfectionist approach that prioritizes “more than good enough” products vs. an MVP mindset? Does it come down to a different risk-reward profile of founders and the VC ecosystem funding them? Or is it a regulatory and cultural fragmentation — across languages and customs — that prevents founders from making swift changes in their staff due to labor laws, or rewarding their talent properly with long-term equity incentives?

And most importantly, how can European B2B companies get inspired by their American counterparts and adjust their growth and hiring strategies to be competitive on a global scale?

In this post, we’ve talked to Loic Jeanjean, our latest addition to the HyperGrowth Partner’s roster and fractional CMO who previously headed up growth functions at European unicorns like Ledger and Pleo, but also in numerous US-based B2B SaaS companies, to find out!

As a European based in Lisbon and advising both EU and US companies, Loic gives us his unique take on the key reasons why European startups fall behind, and key tactical insights about how they could get back on track and aim for a successful scale, including US expansion. 

In a nutshell, European businesses need to embrace swifter operations in line with the fast-paced markets of the roaring 2020s, and a much more customer-centric approach if they want to aspire for global scale. Let’s dive in.

1. Embrace Customer-Centricity, Good Enough & Risk Taking!

In Loic’s experience, European companies tend to be more risk-averse and slower than their American counterparts. This attitude shows in things like over-documenting their operations, micromanaging teams’ decisions, and ultimately seeking perfection in product, marketing, and sales. 

We could speculate that to be a symptom of an idealistic culture, but it most likely comes down to the fact that many European teams don’t know what good and great products look like, or even what good enough is!

Speculations aside, this attitude toward product building leads to slower execution and responsiveness to market changes, which is reflected in delayed launches and scale.

When it comes to product releases, the focus should be on velocity — ship more times, faster. You should aim for consistent, weekly releases. As a good forcing function for your team to build such velocity, aim for shipping 80% of your plan. If you have 10 improvements, commit to shipping 8 instead, as long as you hit the mark of doing it every week.

When European teams embrace the ruthlessness of the growth mindset, made of continuous experiments at a highly tactical level, they’ll be able to quickly kill something that no longer works, and revisit it at a later stage; or swiftly double down on what does work, and push their limits to incrementally improve it. 

“I love the LVR metric — Lead Velocity Rate — and advise founders to aim for a minimum 10% MoM. This pushes Marketing & Sales teams to take more risks and come up with more tests to iterate on their work every month.”

Another key difference between American and European product teams is that the latter are generally less customer-centric; rather, they tend to develop products over-indexing on the founders’ internal conviction or personal experience, which ultimately silo their team and product from their target market.

“If you’re in product or GTM, this might sound like a broken record, but it’s impressive how many teams we see that still don’t just pick up the phone and talk to customers.”

If you’re heading up a customer-facing team in Europe, you should set a goal to speak to at least one prospect or customer per week; that includes not just sales and product, but also marketers. Surveys are not enough to grasp the nuance needed to compete in today’s market; you need to pick up the phone and invest the time to build a system to have in-depth, regular conversations about their:

• Business: who they are, their goals, and how they operate

• Pain points: what keeps them up at night

• Tooling: what they use to run their business, or their fave app on their phone

• Channels: where they learn and hang out with others, on- and off-line

• Decision-making: who they follow and respect, and influence their decisions

Build these habits and you’ll not just quickly emerge in the European market, but you’ll soon graduate to the World’s League and be able to compete on a global scale.

2. Broaden your Horizons of what Good and Great GTMs Look Like

Founders, CXOs & VPs, but also team leads, in Europe often lack experience in what successful GTM looks like, as they are often first-timers in their roles. 

Additionally, they tend to benchmark their TAM ambitions and set goals against local customers and competitors, drastically reducing their vision of what’s possible, and in general what good and great look like. In the worst cases, this leads to complete tunnel vision and even arrogance, which discards valid tools in your GTM’s arsenal.

For example, many European companies we’ve worked with question tactics like offline events and webinars because they “have already tried this in the past”, or “won’t scale”; when in reality the issue is a lack of expertise in executing them properly.

In turn, this attitude leads to an inability to execute the right strategies, accumulating strategy and tactical debt across the board. This debt is very hard to identify and course correct, because it compounds in the medium term, and impacts negatively virtually all functions — product, marketing, sales, data, brand, and hiring. 

Founders and C-levels who want to avoid this need to have the maturity and self-awareness to identify what’s under the hood, and typically this comes down to having seen the movie already a couple of times.

Set Global-scale References of What Great Looks Like

Even if you don’t have direct experience, you can learn from others. Expanding your horizons and learning from global-scale success stories can provide valuable insights and motivation for European founders and teams. This is especially true across GTM and hiring which are quickly becoming the more challenging aspects of sustainable scale. 

Start with building the right mindset and attitude towards learning. In the US, it’s common sense that you have to invest resources upfront to make a return. In other words, you have to break an egg to make an omelet. That’s not always the case in Europe.

You need to be humble enough, willing to acknowledge that and invest your resources into painting a picture of what good and great look like on a global scale. 

In the Internet era, you don’t need millions to get started. Just join cutting-edge e-learning platforms like Reforge & CXL to access the top thinking and tactical frameworks to empower your team with the right tools.

And please read the classics of Go-to-Market and Leadership — books like Good to Great, E-Myth, Obviously Awesome, The Ultimate Sales Machine, From Impossible to Inevitable, Crossing the Chasm, Predictable Revenue, The Sales Acceleration Formula, The Cold Start Problem, Play Bigger, Managing Oneself, Measure What Matters, Radical Candor, High Output Management, and more.

Bring in External Advisors to Set World-Class Examples

It’s a no-brainer that investing in your team is one of the best things you could do; not just for your individual contributors, but also, most importantly, your leaders.

Get your leaders an advisor to mentor them. Engage advisors with repeated, real-life experience in the “wild wild US market”, ideally those who have worked in companies that are already past your current and next stage of growth. Task them to help you reshape your team and operating culture, and transform your GTM.

Focus advisors on what we have discussed so far —  helping you build velocity and take more, bolder, shots at the net, tight feedback loops with customers, and a higher risk-reward attitude. But also uncovering blind spots!

External advisors or fractional executives who have experience in leading hypergrowth companies have a much better compass for identifying real problems and course corrections. Just be open to listening to them, and implement their advice, or you’ll be fooling yourself twice! We’ve seen many examples of leaders investing tens of thousands in advisory, ignoring their advice, and ultimately going bust.

3. Leverage a Distributed Remote Network of Talent and Investors

Engage VCs with the Right Operating Team

Last but not least, there’s another key difference between Europe and the US — how funding is released, hiring is made, and equity compensations are structured, with the European VC and talent market being smaller and slower. 

According to , that’s exactly where European sluggishness comes from:

"The European ecosystem as a whole is more risk averse, and that starts at the VC level. EU VCs expect more revenue, and more maturity at an earlier stage than their US counterparts. That culture permeates into the companies through the founders and creates more frequent positive outcomes but smaller ones. Unfortunately, the venture business is not about getting ‘1 more X’, it's about getting the ‘100X’"

According to , it comes down to culture: 

“I think there’s a lack of culture of ‘good advisory’. Most VCs in Europe have never seen it, never done it, never tasted the good stuff. They’re also reluctant to pair founders with advisors who can push them to do better and give them the blueprint. Overall, Europe plays risk-averse in an asset class that's based on huge risks”.

The European venture capital ecosystem also tends to have fewer experienced operators and successful founders compared to the US, which can impact the quality of advice and support provided to portfolio companies. This creates a "vicious circle" where the lack of success stories makes it harder to build a strong VC ecosystem, and founders rely on average advice to approach hiring and GTM.

Loic’s advice is to inspect carefully former operators in VC teams to determine what good looks like. Bonus point if they have members who successfully led the US expansion of European companies, or worked in a US SaaS before in a high-impact position like CRO, CMO, or VP of Sales & Marketing.

Hire and Reward Risk Takers in Your Team

When it comes to hiring, the nature of the European market is even grimmer. According to Loic:

“I've always been a huge fan of hiring for potential, but at some point in your startup journey, you'll need to bring people who have seen the movie once or twice. Once you pass 5-10M in ARR, it's hard to wing it.”

Hiring and nurturing talent can be more challenging in Europe — causes are both cultural and regulatory. A tendency to keep employees that “have been with us since the beginning” results in hiring and keeping inexperienced talent, and builds reluctance to make changes when needed. Managers conflate “being nice” with “making the right business decision”. In parallel, strict labor laws prevent letting people go when they’re not the right match. These hurdles can make it more difficult to adapt to swift market changes and proactively evolve company structure and culture as required.

But there are strategies to mitigate these problems. Using EOR solutions like Deel or Remote to get globally distributed talent can be a good starting point, as it increases your talent TAM and your experience range. Especially after COVID, lots of Europeans who worked in the US are slowly coming back to Europe bringing their SaaS experience to the continent — that’s an amazing opportunity to get these unique profiles — with exposure to both cultures —  into your organization. 

And once you hire the right people, adapt compensation practices to reward risk-taking and scale operations more aggressively. Don't punish risk-takers, but get them to share their learnings — the good, the bad, and the ugly — with the rest of the org. This will transform your culture, boasting transparency, accountability, learning, and curiosity.

Also, uncap your commission plans — make it really exciting for people to crush their goals. And make these commissions accessible to your entire GTM team, not just sales. For example, a marketer who gets a €100,000 base salary could have a variable plan with 50% of their bonus based on overall company performance — uncapped, to encourage team collaboration — and 50% pegged to their impact — tied to pipeline, marketing-generated revenue or a blend of several metrics, also uncapped. 

Remember, the culture we build in our organization is reflected in your incentive scheme. To build a risk-taking, competitive culture to compete on a global scale, set the carrot and the stick accordingly.

Closing Thoughts

By transitioning from a perfectionist approach to one that values agility and customer centricity, European B2B companies can enhance their competitiveness and achieve greater success in global markets.

This strategic shift requires embracing risk, focusing on rapid iteration, and putting the customer at the heart of all business operations. These changes not only align with global business practices but also pave the way for sustained growth and market penetration, especially in challenging markets like the US.