Mastering Signal-Based Marketing

Generate more pipeline by selecting, executing, and scaling intent signals

The holy grail of Marketing — Audience/Market Fit

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.

Need help to architect and implement your signal-based GTM? Our team has been using intent signals before they were cool, and we have an in into top-tier signal collection SaaS in the market.

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.

Is your signal-based marketing ready to run? Let us help you automate the end-to-end process to scale pipeline.

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.