Signal-based GTM: what it is and how to get started
The business case for signal based GTM - precision over volume
Hi Friends - A few weeks ago I broke down the four sources of revenue every company has to work with: inbound, outbound, nearbound, and existing customers. Yep - everything you do falls into one of those four buckets.
If you’re an early stage startup, you’re probably leaning heavily into outbound. And that makes sense. It’s the fastest motion to stand up. You don’t need to wait for SEO or other digital marketing to compound, or for a partnership ecosystem to mature. You can start right now.
But here’s the problem: most startups run outbound like they are playing darts at a bar - drunk. Build a big list, blast a sequence (with terrible copy), and hope for the best. And at a ~1% average conversion rate on cold outbound, you need a LOT of volume to make that math work — volume that will kill your domain reputation real freaking fast.
The fix isn’t to abandon outbound. It’s to get smarter about which accounts you spend time on.
There are two great ways to do this:
Account-based marketing (ABM) — a coordinated, multi-channel approach where marketing and sales align around a defined set of named accounts with personalized campaigns and content.
Signal-based outbound — equipping your sales team with real-time buying signals so they know which accounts to prioritize and when to reach out.
If you can combine the two, even better.
ABM builds awareness and air cover with your target accounts while signal-based outbound ensures your reps are spending their time on the accounts most likely to convert - right now. Together they create a continuous loop — marketing warms the account, signals tell you when it’s ready, and sales moves on it with context.
We’ll go deeper on ABM later this month. For now, let’s start with the foundation both approaches depend on: understanding which signals actually matter for your business.
Signal-based GTM: what it is and how to get started
Start with your own signal profile
Every business has a different set of signals that predict buying behavior for their product. The mistake I see founders make is jumping straight to tools and data sources before spending the time to understand what signals actually matter for their product and ICP.
Here’s a simple exercise: look at your last 10 closed-won deals and ask yourself (or them) what happened right before those accounts entered your pipeline.
Did they just go through an acquisition? Hire a new leader in the function you sell into? Have a cyber incident? Recent revenue growth or decline? Kick off a relevant initiative?
There’s almost always a pattern. That pattern is your signal profile, and it should drive every decision about what you track and where you invest.
If you skip this step, you’ll end up monitoring signals that look smart on paper but don’t actually correlate with buying behavior for your specific business. A Series A announcement might be a strong signal if you sell SOC2 compliance software. It might mean nothing if you sell accounting software.
Do this exercise before you do anything else.
First-party signals: start here if you can
First-party signals are the buying indicators happening inside your own ecosystem — website visits, content downloads, email engagement, product usage, CRM activity. They’re the highest quality signals you have because they reflect direct interaction with your brand.
If someone visits your pricing page three times in a week, that’s a better signal than almost anything a third-party tool can tell you.
The challenge for most early-stage startups is volume. If you’re getting 500 website visitors a month and you have 200 contacts in your CRM, your first-party data alone isn’t going to fill a pipeline. It should absolutely be tracked and acted on — set up alerts in HubSpot for pricing page visits, track email engagement, pay attention to who’s opening your proposals more than once. But it probably can’t be your only signal source yet.
Think of first-party signals as your highest-fidelity layer. You want them running in the background at all times. But keep in mind — by the time someone lands on your pricing page, something already changed inside their business that sent them looking.
Maybe they hired a new leader who’s reevaluating the stack. Maybe they just closed a funding round and now have the budget. The first-party signal you’re seeing is often the result of a third-party signal you missed.
Which means even if you have strong first-party data, supplementing with external signals lets you get in front of accounts earlier in the buying cycle — before they’re on your pricing page and your competitor’s too.
Third-party signals: filling the gap
This is where most early-stage teams need to focus their energy — buying signals happening outside your ecosystem that suggest an account might be entering a buying cycle.
The good news is there’s a growing landscape of tools that make these signals accessible without a massive budget:
Job changes and org moves. When a company hires a new VP of Sales or a new Head of Marketing, there’s often a 90-day window where that new leader is evaluating their stack and making changes. Apollo, LinkedIn Sales Navigator, and plenty of other tools make it easy to track these moves across your target accounts.
Funding events. A company that just raised a Series A or B often has budget earmarked for building out the exact functions you sell into. Crunchbase or Pitchbook are the most well-known sources here — you can set up alerts for funding rounds in your target segments.
Tech stack changes. If you sell a product that integrates with or replaces specific tools, knowing when a target account adds or drops a technology is a strong signal. Apollo, ZoomInfo and others allow you to track technographic data.
Category or competitor research. When a company starts actively researching your category or a competitor — reading G2 reviews, comparing vendors, visiting competitor profiles — that’s about as close to a hand-raise as you’ll get for outbound. G2 Buyer Intent data surfaces this activity.
Each of these gives you a lens into a different type of buying behavior. And most offer free tiers or affordable plans that work at startup scale.
Where individual signals fall short
Here’s the catch with everything I just described: each signal in isolation can get noisy.
A company raising a Series B doesn’t mean they’re buying your product. A VP hire doesn’t mean they’re evaluating your category. A G2 visit could be a bored analyst doing a competitive landscape for their boss.
The real power of signal-based GTM isn’t tracking individual data points. It’s layering and correlating them.
When you see a target account that just raised funding AND hired a new leader in your function AND started researching your category on G2 AND visited your website twice this month — that’s a fundamentally different signal trail than any single one of those events alone. That example account is almost certainly a buyer – right now.
The problem is that manually cross-referencing all of these sources is brutal. You’re logging into five different platforms, exporting CSVs, trying to match company names across inconsistent data sets. It’s not sustainable or scalable.
This is the exact problem we built ABM Intel to solve — pulling these signal layers together into a single view so you can see which accounts are actually heating up right now, not just which ones match your firmographic criteria. If you’re running an outbound-heavy motion and want to skip the manual correlation work, it’s worth a look.
But even without tooling for the correlation layer, the principle holds: stacking multiple signals is exponentially more predictive than any single one. And not all signals are created equal – figure out which ones matter the most for your sales cycle.
Signals are a GTM multivitamin
One thing I want to close with — and this is a framework I come back to constantly with my clients — you need to focus your investments on GTM multivitamins.
Signal-based GTM shouldn’t just be an outbound play.
The same signals that tell your SDR or AE which accounts to prioritize this week also tell your marketing team where to focus ad spend. They also inform your CS team which existing customers might be ready for an expansion conversation.
One investment in understanding and tracking buying signals can pay off across every function in your GTM motion. For a resource-constrained startup, that kind of leverage is everything.
Start with your signal profile. Layer in what you can afford. Act on what the data tells you. Watch your conversion rates soar.
P.S. We just started offering GTM Engineering services, too. This is the exact kind of infrastructure and automation a GTM engineer can help you build to scale.
With love and gratitude -
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In this newsletter I share the exact tips, playbooks, and GTM multi-vitamins I’ve used to help 30+ B2B startups scale their revenue 150-590% YoY.