The problem: scale grew, revenue didn't
A subscription AI app came to us with a situation familiar to almost every founder at this stage: marketing was scaling hard, acquisition costs kept coming down, and MRR sat still.
At the same time the team was running dozens of product hypotheses at once. And the key thing: no one knew for sure which changes brought profit and which dragged P&L down. Because of the nature of an AI product, high user activity without monetization simply burned budget: thousands of dollars went to acquiring active users who didn't pay, due to technical quirks that seemed almost unfixable at the time.
Behind all the individual snags stood one root pain: the team had no system to see where money was leaking and to decide what to do next. Not a shortage of ideas, but a shortage of clarity.
Our approach: not guessing which hypothesis fits, but suggesting ones that have been already tested by the market or in our own experience
We step into the core node, which is the monetization funnel, and we diagnose it methodically. Where exactly it leaks money, on which screen and why. Where specifically between the ad and the renewal the users dismiss the product. And then we fix those screens by applying targeted changes that will influence revenue.
At the core is our method, drawn from our own launches:
'Copy' is too crude a word. The value isn't in taking someone else's work, it's in understanding: whom to take from, what exactly, and how to test it. We use a strong player in the niche as a donor for onboarding or paywall, the market has already paid for that validation for us, and we finish off the rest with our own hypotheses and A/B tests. Starting from a blank page where the answer is already known is simply expensive.
On top of that runs a documented experiment process, where every step is tied to a financial metric, not to intuition:
picking references: donors for a specific screen;
prioritizing hypotheses;
end-to-end analytics of the experiment;
scaling to audiences.
We stopped the chaotic tests, audited the acquisition channels and the app's verticals to isolate the specific points where monetization was dropping, and built a backlog of experiments around them, tied directly to money.
What changed
The system restarted four processes, and all of them are about where and how the app earns:
Prioritizing hypotheses. The team stopped relying on intuition. Hard rules appeared: which metrics take priority, how to segment the audience by the ROI of different channels, how to benchmark against the top-10 strong competitors, how to price flexibly for purchasing power.
End-to-end funnel analytics. Instead of one final goal, we started measuring metrics at every step: ads → store pages → onboarding → paywalls → upsells → renewals. The analytics instantly flagged critical bugs: revenue leaks were fixed before they reached P&L.
Cost optimization. We rebuilt the funnel for a region that used to eat up marketing budgets and increase AI-token costs, which cut infrastructure costs significantly and brought in extra revenue.
Audience migration. When the usual monetization channels for the core audience stopped working, we quickly redirected it to alternative platforms and held MRR at a moment when competitors with the same audience were losing revenue.
Results
MRR grew 58% without a multiple increase in spend.
Funnel conversion to subscription more than doubled: from 2.1% to 4.47% on average.
New audiences delivered 25% of current revenue.
The team got rid of the uncertainty. A backlog of hypotheses that worked appeared, with clear reasons for success, and a framework for the next tests.
The main shift wasn't in a single metric, it was that the founder finally saw the whole money machine as one: where it leaks, what to fix first, and which experiment to run next. From there, growth stops resting on luck.
// We flag retention and product bugs along the way, but we don't fix them ourselves: that's not our zone. Our core responsibility is the marketing funnel, although we will alert you if we find something in the product that's influencing it negatively.