We ran ~50 short A/B tests on offers, trial lengths, and copy through a web paywall over two weeks. The speed felt unreal compared to app-store cycles.
What I did differently:
- built a simple set of landing variants and rotated offers server-side.
- focused tests: one variable per test so results were clean.
- used small spend to sign up minimum cohorts and then let retention numbers mature.
Outcomes:
- clear wins on price and trial length surfaced within days.
- we improved net revenue per new user because we could try riskier offers without store approvals.
Problems encountered:
- statistical noise from small cohorts. We learned to run fewer tests but larger cohorts.
- engineering time still needed to sync entitlements to the app.
If you want a testing checklist or the way I structured experiments, which part would help you most — test design, analytics setup, or the entitlement sync?
I ran frequent price tests by swapping JSON configs the web funnel read at runtime.
The change appeared immediately and we measured lift quickly. Using a simple generator that produces the config made it trivial to spin new variants.
Less engineering and more results.
Rapid testing on web changed our roadmap. We tested multiple trial lengths and offers without waiting for app reviews.
The funnel platform let us publish changes instantly and push results into Mixpanel so we could iterate fast.
I trimmed our test plan to two changes per week and saw clearer wins.
Keep tests simple and give each variant room to breathe.
Speed is worthless without proper measurement. Design tests with clear primary metrics (net revenue per new user, trial-to-paid conversion). Use decay-aware windows for renewal signals. Always include a holdout so you can detect seasonality or campaign drift.
Operationally, automate variant rollout and tie the funnel change to an experiment id that flows into your analytics and subscription system. That keeps downstream reporting clean and attribution accurate.
My recommendation is to prioritize test ideas by expected revenue impact not novelty. Small lifts on pricing can beat big UI experiments.
Make sure you define success thresholds before starting.
Watch for cannibalization between tests.