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Most teams measure demo engagement to answer one question: did this person convert? goHappy asked a different question. Who should we be spending ad dollars on right now, based on what they're actually doing with our demo?
That shift helped goHappy pull off a real turnaround. After a rough year of rising ad costs and a shrinking audience on their one paid channel, they rebuilt their targeting around real engagement data, including how people used their Storylane demos, and grew pipeline 3.5x their average month.
By the end of this piece, you'll understand how they treated demo engagement as an ABM signal, and you'll have a simple starting point for doing the same with your own demo data.
Why this matters
Most companies collect demo engagement data and let it sit inside the demo platform, disconnected from anything else. Meanwhile, ABM programs run on account lists that get built once and rarely touched again. Neither one talks to the other. goHappy connected them: demo behavior became one of the inputs that decided who counted as a target account and who got more marketing spend.
What goHappy was up against
goHappy sells to a genuinely tough audience: managers of multi-location, frontline workforces, a small, hard-to-reach buyer group. For most of 2024, they leaned almost entirely on LinkedIn ads. Then LinkedIn got expensive. CPMs rose 91% over the year, audience penetration dropped to an average of 23% per month, and booking rates fell 36%. A single-channel strategy that used to work stopped working.
Their full ICP list ran to about 18,000 accounts, far too many to meaningfully target with a limited budget. They needed a way to narrow that list down to the accounts most worth spending on, and to keep adjusting that list as new information came in.
Treating demo engagement as a signal, not just an outcome
goHappy scored accounts on a 0-200 scale: 100 points for ICP fit, 100 points for intent. ICP fit covered firmographic details like number of locations and workforce size. Intent covered what an account was actually doing, and Storylane demo activity was one of the clearest signals in that half of the score.
Not all demo engagement counted the same. A single accidental click on a demo counted for far less than someone who reached a high percentage of completion, and clicking a CTA inside the demo counted for more still. The score also decayed over time, so a demo interaction from two weeks ago mattered less than one from yesterday, and different types of activity decayed at different rates.
This is the part any Storylane customer can copy without building goHappy's full 12-signal system. You don't need a dozen data sources to start. You need to decide, for your own demos, which actions actually indicate real interest (completion percentage, CTA clicks, return visits) and give those more weight than passive or accidental engagement.
Letting engagement move accounts, not just score them
The more interesting shift wasn't the scoring itself, it was what goHappy did with a rising score. In a traditional ABM setup, the target account list gets built once, often annually, and stays fixed. goHappy treated their list as something that should move.
An account that fit their ICP but showed no engagement wasn't a priority. The moment that same account started engaging with a demo, visiting pricing pages, or showing other real signals, it got promoted into an active target account and assigned to a rep. Richard Meyer, goHappy's Director of GTM and Growth, described the shift directly: the next phase of ABM is reacting to what's actually happening in real time, rather than treating the account list as an annual exercise done once and left alone.
That real-time movement also worked in reverse. Accounts that stopped engaging could lose priority, freeing up sales attention for accounts that were actually showing up.
How to start using demo engagement as an ABM signal
You don't need Clay, Vector, or a 12-signal pipeline to begin. A simplified version of what goHappy did looks like this:
Pick two or three engagement tiers that reflect real interest, not just any interaction. A good starting set: viewed a demo, completed a meaningful percentage of it, and clicked a CTA inside it.
Weight those tiers differently. A completed demo with a CTA click should count for more than a partial view. Don't treat all engagement as equal.
Add a time factor. Recent engagement should count for more than engagement from a month ago. You don't need a complex decay formula, even a simple cutoff (only count activity from the last 14 or 30 days) gets you most of the value.
Let the score move accounts, not just describe them. Decide in advance what a rising score should trigger, whether that's a rep getting notified, an account getting added to a target list, or an account getting included in an ad audience.
That's the core of what made goHappy's approach work, minus the full data infrastructure they eventually built around it.
The results
By expanding beyond LinkedIn and rebuilding targeting around real engagement, goHappy saw ad impressions double in February compared to their entire previous quarter, CPMs drop 75%, and pipeline grow 3.5x their average month, with paid-specific pipeline growing 5.2x.
The takeaway
Demo engagement data is often treated as a lagging indicator, something you check after the fact to see if a demo worked. goHappy used it as a leading indicator instead, a live signal that helped decide where to spend ad dollars and sales time right now. You don't need a dozen data sources to start. You need to stop treating demo engagement as a number that only matters inside your demo platform, and start treating it as information your broader go-to-market strategy can act on.