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Paid Search · Jul 2026

A Dev Tools Platform Cut CAC Payback From 18 Months to 9

A developer-tools SaaS company had a paid search program generating signups, but almost none converting to paid plans. Restructuring campaigns around the free-to-paid conversion signal, not signup volume, fixed the payback math.

Challenge
The team optimized paid search entirely around free-trial signups, which looked great on a cost-per-signup basis but hid a much worse problem: the vast majority of signups from paid search never converted to a paid plan. CAC payback period had stretched to 18 months, well past what the board considered sustainable.
Solution
We rebuilt the campaign's optimization target around free-to-paid conversion rate rather than raw signup volume, using the company's product analytics to identify which search terms correlated with signups that actually activated and converted, then concentrated spend there.
Result
CAC payback period dropped from 18 months to 9, even though total signup volume from paid search fell by roughly a third

A healthy-looking cost-per-signup number was masking a much worse problem underneath it: most of those signups were never going to pay.

The metric everyone was watching was the wrong one

Cost per free-trial signup looked good every month, and the team kept optimizing toward it. But CAC payback, the metric the board actually cared about, kept getting worse, because a large share of those signups were curious developers who never activated the product, let alone paid for it.

Connecting ad spend to product usage, not just signups

We worked with the product team to trace signups from paid search all the way through to activation and paid conversion, using the product’s own analytics rather than relying on the ad platform’s shallow definition of a “conversion.” That let us see which search terms brought in developers who actually became paying customers.

Results

Concentrating spend on the search terms that correlated with real product activation cut CAC payback from 18 months to 9, even as total signup volume dropped by about a third. Fewer, better signups turned out to be worth significantly more than a larger number of low-intent ones.

FAQ
Why would optimizing for a smaller number of signups improve the business?

Not all signups are worth the same. A third fewer signups that convert to paid at a meaningfully higher rate produces more actual revenue, and does it faster, than a larger number of signups that mostly churn out during the free trial without ever paying.

How did you identify which search terms correlated with real conversion?

We connected the ad platform's search term data to the product's own analytics, tracing signups back to search query and then forward to activation and paid conversion. That let us see which terms brought in people who actually used the product, not just people who signed up out of curiosity.

Did this require changes beyond the ad campaigns themselves?

Mainly better data plumbing between the ad platform and the product analytics tool, which the engineering team had to set up once. After that, the ongoing optimization work was entirely within the campaign structure itself.

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