The Next Generation of B2B Growth
How AI is Shaping Growth Strategies, Teams, and Stacks
Every startup wants to drive hypergrowth while maintaining healthy unit economics. And to deliver that, you need the right strategy, team, and growth stack. Forward-thinking growth marketers know that the best strategy for building a competitive moat is personalization at scale — marketing campaigns that mimic true human-like, one-to-one interactions across multiple touchpoints.
But why is that? And now that AI has pervasively entered our lives, how will this affect growth strategies like personalization and the stacks and teams used to execute them? To make it worse, Martech is exploding. Thousands of alternatives and continuous bundling and unbundling of products make selecting, building, and maintaining a growth stack impossible.
So where is the market headed next? What strategies, stacks, and team setups will make the winners of the next cycle led by AI? At HyperGrowth Partners, we’ve operated at the epicenter of B2B SaaS, growth, and MarTech for the last two decades.
This two-post series will unpack which growth strategies, teams, and stacks will make the next generation of hyper-growth companies.
The Cyclical Evolution of Growth Marketing
To understand how the next market cycle affects growth strategies, teams, and stacks, let’s walk down memory lane and explore the last four growth marketing cycles. Each lasts approximately 7-8 years and is driven by disruptive technologies that define the period's strategies, teams, and stacks. Please note the cycles identified here reflect our take on the market and might not be exhaustive (chart inspired by chiefmartech.com).
2000-2007: Cloud Computing, Single-use-case SaaS. Cloud computing revolutionized how software was delivered to businesses, dismantling on-premise (I still remember selling business software in…boxes 🥲). Most companies were providing single-use-case solutions, with Salesforce and Hubspot leading the way. Back then, there were mostly top-down sales teams focused on SDR-led outreach and digital marketers operating the newly launched Adwords (I was one of them at Apple!). There were no such things as automation or personalization, as each integration needed to go through the infamous IT.
2007-2015: Mobile, Social & SaaS Bundling. The following cycle saw the rise of smartphones, mobile apps, and social. Single-use-cases SaaS slowly evolved into platforms that bundled multiple products ‘all-in-one’ — Hubspot did it for marketing automation, Zendesk for customer service, and Salesforce for sales. Inbound marketing, developed by Hubspot, and digital advertising, led by Google and Facebook, became new popular growth strategies on top of outbound and SEO, growing the roster of go-to-market channels. Also, despite seeing the first cross-functional growth teams at Facebook, Hubspot, and Uber, marketing technologists were still the standard in this period.
2016-2022: Data Analytics & SaaS Unbundling. Mobile and social enabled businesses to track various data points across devices and apps, paving the way for data analytics disruption. Segment, Amplitude, and DataBricks emerged to help businesses manage and analyze large datasets. Especially CDPs like Segment enabled data to flow across a myriad of micro-SaaS — each handling a specific use case — igniting the explosion of MarTech unbundling (which grew 429,000% over the previous cycle!!!) and modern growth stacks as we know them today. According to Gartner, software spending was up to $4,800 per employee per year in 2020! These modular data-powered growth stacks enabled advanced and automated growth strategies like personalized outbound at scale. And with the birth of Reforge, growth marketing became a popularized practice and an in-demand mindset in any organization.
Why data? We need to know our customers!
Before stepping into the future and seeing what growth marketing will look like in the age of AI, someone might ask: Why all of this in the first place? Why would every hypergrowth startup want sophisticated growth stacks and tons of customer data points to deliver personalization at scale?
As marketers, we aim to engage buyers, capture attention, and drive action. But with the growing volume of messages generated by growth automation, our poor buyers became increasingly desensitized, eventually disregarding ~96% of their ads.
I often propose this thought experiment. Think of your physical mailbox. You get mail SO frequently that out of ten letters, nine are likely ads you will trash without reading them. Now imagine the tenth envelope. It has a handwritten return address and a clumsily stuck stamp. These features utterly convince you it’s from another person. How likely are you to trash that one without thinking of opening it? Take a second to think about it.
Zero. If you are convinced it is from a human and not an ad, you will open it every single time. People buy from people, not from businesses, even in B2B.
Why is that? It’s because of the powerful combination of curiosity — ”Who sent me this letter?” — and reciprocity — ”If someone went through the effort of handwriting me a letter, I should probably at least return the favor and read it”.
So the real question as marketers become — how can you be that one letter that gets picked, opened, and read instead of being trashed? Or even better, that inspires action.
Before being buyers, we are all people. And as a deeply social species, we crave empathy, reciprocity, and social liking. And that’s what you feel when you find a hand-written email that stands out.
As marketers, we’ve been using technology all along — cloud, mobile, social, data, and now AI — to cut through the noise and deliver personalized reciprocity at scale because that’s what drives your conversion rates. The data analytics market cycle made it clear — the SaaS stacks and tools we use are all in service of creating a one-to-one connection with our buyers. Otherwise, why collect customer data at all?
However, what goes up must come down. Like every cycle, successful growth tactics become obsolete because they get overused, and buyers get annoyed and ignored. It happened with retargeting, and now it’s happening with enriched emails. AI is adding a leg up in this cycle.
And that’s where the true top 1% of growth teams stand out. They’re at the bleeding edge of the growth strategy/stack lifecycle. They create the bleeding edge of growth strategies by creatively experimenting with new combinations of tactics and technologies to unlock new levels of personalization and reciprocity. The goal is to build a competitive moat before these tactics get saturated.
2023-2030: The AI-powered cycle
Growth strategies: AI-powered, hyper-realistic personalization
So what will happen in the next cycle? AI makes one-to-one relationship-building not only hyper-personalized but also hyper-realistic. In the previous cycle, customer data and CDPs powered tactics like enriched emails, custom retargeting, or Loom videos with buyer’s names written on a board. These worked for that time, but the most tuned-in buyers could discern they were not ‘real’. Thus the desired reciprocity effect quickly disappeared.
AI will make these messages hyper-realistic so that even the most aware buyers will feel they’re interacting with another person one-to-one.
ChatGPT and Bard will be able to gather many more data points than current CDPs, scouring the web to find contextual information and deliver messaging that matches people's demographics, psychographics, firmographics, and more — all in real-time and at scale. This is where the true 10x growth strategy opportunity is and where the top 1% of growth marketers should invest.
In the near future, we might see buyers getting so many AI-generated messages that ’push marketing channels’ might decay in favor of ‘pull’ ones, where buyers discover and evaluate products via centralized chat UIs à la ChatGPT. But we’re not there yet, and growth teams should invest in the next big opportunity before thinking too ahead of the curve!
If you’re ready to drive hyper-realistic personalization, contact us at HyperGrowth Partners. We have decades of experience implementing growth strategies at the bleeding edge of the growth tactic lifecycle.
Growth stacks: AI-generated, Ambient Stacks
Which stacks will enable these bleeding-edge levels of hyper-realistic personalization? The ones that will:
Centralize and orchestrate data upstream. The explosion of data and touchpoints is forcing the bundling of customer data upstream in the value chain. Data governance and orchestration companies like Census, Segment, and HighTouch drive this trend via their CPDs and (reverse) ETL products. Successful stacks must be able to track and analyze tons of data points from disparate sources and enrich them with third-party data by default. Also, observability must ensure data is clean and reliable at all times. Because clean and proprietary data will make the biggest impact in training AI, it’ll become a marketing channel per se and one of the most important in building a competitive moat.
Unbundle tooling downstream with AI. The MarTech unbundling will likely continue as generative AI will give birth to a long tail of AI-powered micro-SaaS automating downstream use cases like ads, emails, chats, content, SEO, and more! CDPs and APIs will feed data to AI, which, thanks to composability, will progressively take control of connecting the dots among these tools at the point that new campaigns and tactics will be first suggested, then created automatically in the background, without teams even realizing it. But we’re not there yet. In the short term, we’ll still be using CDPs and Zapier to drive custom downstream personalization — this is where growth teams should invest now.
Growth Teams: AI-powered Growth Ops Generalists
Since the rise of the first cross-functional growth teams at Facebook, Hubspot, and Uber, the lines between core company functions — marketing, sales, product, and engineering — have been increasingly blurring. No code has democratized engineering to non-technical users, and buyers' complex demands have forced teams to collaborate closely and at faster rates.
Also, the explosion of data, APIs, and automated tooling has been driving progressive operationalization of each function to the point that each one has an ‘ops’ team — marketing ops, sales ops, rev ops, dev ops, and so on and so forth.
These two forces, operationalization, and cross-collaboration, coupled with AI, call for a more holistic approach to lead the growth of the business. One where a small, nimble team has the big picture of the whole funnel, strategy, and stack.
Because AI-powered stacks will be full funnel — closing the loop from acquisition to won deal, feeding back to the top of the funnel, marketers in the medium term will become more and more system operators. We’ll respectively see:
Fewer specialists. We’re already seeing large specialist teams shrinking due to the recent tech layoffs. AI will likely push this trend forward, reducing the number of full-time marketing specialists in each area and relying more on ad-hoc expert advisors to drive channel-specific strategies. Organizations are also moving away from channel-specific marketers like paid and SEO because, as a result of AI, they’re becoming more and more algorithmic and automated. This makes it easier to justify investing in an AI-powered SaaS platform to do the job instead of a full-time team. If anything, it’s worth investing in specialists with no SaaS-based algorithmic solutions yet, like in PLG or outbound.
More cross-functional generalist. As a result, there will be a convergence of small teams of growth operators. These are not mere generalists but “Internet plumbers” that deeply understand each stage of the entire funnel, know the latest technology that can be plugged into each of these and can stack and connect each block together. Like plumbers, they understand how to pipe data into different SaaS tools to unlock personalization and reciprocity each step of the way. These people will have a specific mindset, strong business sense, notions of product, engineering, marketing, sales, and deep-rooted culture of data and ops. Their focus? Set up the strategy to deliver hyper-realistic personalization through AI and orchestrate large, modular growth stacks.
However, we’re not there yet! In the short term, winning growth teams will hire talent to use data to creatively feed AI to power the hyper-realistic personalization that makes buyers tick. These growth operators are empowered by AI to craft & test infinite variations of personalized experiments. As a practitioner, I’ve been working very hard to get there, doing things like:
Consistently check the MarTech section of Product Hunt
Talk with founders and work with MarTech companies
Scour and contributing to MarTech Slack communities
Constantly ask mentees about new growth stacks and tactics they’ve been using and their results of course!
And then recycle what works in early-stage into later-stage organizations.
Zooming out, it’s clear that AI is not the first technology that brought disruption and significant change. However, the order of magnitude of this cycle will be unprecedented, and the changes affecting VC-backed startups will be much more abrupt.
You don’t have to face all of these challenges on your own. At HyperGrowth Partners, we’re experts in designing growth strategies, stacks, and teams. Hit us up if you want to join the next generation of hypergrowth B2B SaaS.
In the next post of this series, we’ll deep dive into the next-gen B2B SaaS growth stack. Subscribe to stay in the loop.
Editor’s Note: This article was written in collaboration with
, who assisted in bringing to life ’s experience and insights through his writing.
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I'm looking forward to part 2!