For most of my career in B2B marketing, the targeting conversation went something like this: pick a firmographic and demographic profile, build a list, run the campaign. It felt like science. It mostly wasn't.
The problem is that a company's size or industry doesn't tell you much about whether they're ready to buy. It just tells you they theoretically could. That's a wide gap, and a lot of marketing budget has fallen into it over the years.
Over the last few years, marketing has modernized, and signal-based marketing is something every modern marketer should be using. Instead of asking "does this company look like a customer," you ask "is this company and its employees doing something right now that suggests they have a problem we solve?" Or better yet: "was there a specific trigger or event that impacted this company that we can jump in and help with?" The distinction sounds subtle. In practice, it changes almost everything about how you run demand generation.
How We Got Here
The earliest version of intent data was pretty basic though actionable. Third-party providers would tell you that a company had been researching keywords related to your category. It was useful, but noisy. You still didn't know who was searching, what stage they were at, or whether the signal was from a real buyer or an analyst doing competitive research. Then came the next version where we could filter out by titles like analyst and student.
Over time, the systems became more sophisticated and the signal layer got richer. First-party behavioral data from your own product and website became more accessible. Tools like 6sense and Demandbase got better at aggregating anonymous research activity across the web and mapping it back to accounts. Community platforms like Common Room started letting you surface signals from Slack communities, GitHub activity, and social conversations. And product usage data, for companies with a freemium or PLG motion, became one of the most reliable signals available. On top of these you can layer on other signals like funding status, security breaches, competitive usage, GitHub activity, competitor pricing changes, and hiring patterns around specific role types.
Something happens, a campaign goes live. These programs should be automated, evergreen, and not tied to a quarterly campaign calendar. The goal is to build enough trigger-to-play connections that you're always reaching accounts at the right moment, with the right message, not just the right segment.
What This Looks Like in Practice
One of the examples I have is from my time at Cloudflare. If a customer's website traffic patterns suggested they were serving a lot of images and video streams, that was a trigger to introduce Image Resizing and Stream. If we saw bot-like traffic hitting their properties, that was a signal to surface Bot Management. We weren't waiting for customers to raise their hand or for a renewal conversation to create urgency. The signal created the moment. These programs were tied to a company-level metric: DNR (Dollar Net Retention).
At DigitalOcean, I used both product-based and external signals to grow the business. When DigitalOcean launched its AI business in 2024, we needed to find companies actively looking for GPU infrastructure. We started reaching out to AI-native companies that had recently closed seed funding, the moment when teams start thinking seriously about building infrastructure. Another example combined product and external signals for a migration play. We used product usage data to identify customers who were also running workloads on AWS, then filtered to industries where DigitalOcean had demonstrated success, specifically digital advertising. We identified companies in that space that could meaningfully reduce costs by moving to DigitalOcean.
I currently lead marketing for a DevOps company that builds specialized agents for code review and validation. The relevant triggers are things like: developers complaining about their current code review tool on Reddit, repositories with high PR volume indicating active teams who'd benefit most from AI-assisted review, or job postings suggesting a team is scaling engineering headcount.
Why It Matters More Now
The technology has evolved multifold in the last couple of years. AI-powered tools can now process signals at a scale that wasn't possible even two years ago across web behavior, product usage, community activity, and external data sources simultaneously. The barrier to building a signal-based program is lower than it's ever been.
The marketers who will win are the ones who show up with a sharp target list and a message that reflects what's actually happening at that company right now. The tools are here. The data is available. The question is whether you're willing to evolve.
Questions or thoughts? Reach out: amit@curioinsight.com