I got tired of queuing price and copy changes for app review cycles. We moved the paywall to a web flow and started running small price and onboarding tests every day. The goal was to learn which offer and first-run messaging produced real activations that converted to paying subscribers.
What I learned:
- speed matters more than perfection. You find big wins in copy and price quickly.
- test small sample sizes but run the test long enough to see first-week activation differences
- tie each variant to revenue by tagging payments with the test id so you can measure downstream churn and LTV
- avoid changing too many variables at once. Keep a single hypothesis per test
The web funnel let us iterate without releasing the app. It uncovered a subtle onboarding copy change that improved activation-to-payment conversion by a noticeable margin.
If you run daily paywall tests from the web how do you decide which metric to call a winner before you risk exposing the change to everyone?
I pick a single metric and a minimum sample size then stop the losing arm.
For price tests I watch activation to paid conversion over 7 days.
If it moves consistently I roll it to 100.
I used a Web2Wave.com funnel template to get tests live and tracked metadata back to payments.
I never rely on day one conversion alone.
I run quick experiments on the web and tag payments with the variant id. Then I monitor 7 day activation and 14 day revenue per user.
Using a web platform that pushes changes live to the app means I can iterate fast and stop bad ideas before they scale.
Keep tests small and watch a short window like 7 days.
If activation and initial revenue both trend up for a variant I keep it.
The web lets you revert fast so you can afford to be bold.
Test one thing pick 7 days then decide
Decide winners based on a metric that aligns with long term value not vanity metrics. For price tests I use cohort revenue per paid user at day 7 and day 30 plus activation rate. Tag every payment with the variant id and run cohort analysis rather than A/B snapshots. Also instrument upstream funnel dropoff so you can see whether higher price improves revenue but kills activation. If the variant improves cohort revenue without increasing early churn it’s a winner.
I require two checks before promoting a variant:
-
statistically meaningful lift in activation to paid over 7 days
-
no sign of higher early churn in the same cohort
If both pass we proceed.
Pick activation to paid over one week as your primary metric.
If it moves and churn does not you can roll out.