I’ve been testing onboarding step order, copy, and paywall timing on the web to avoid store releases. I track a small set of events only: page_view, quiz_complete, account_created, plan_selected, payment_succeeded, refund_created. Variants are simple and I keep changes isolated.
I check sample ratio mismatch, run a pre-registered stop rule, and try to avoid mid-test tweaks. Biggest pain is day-of-week bias and paid traffic shifts mid-flight.
How do you structure test rollout and stopping criteria when onboarding lives on the web and you can ship daily?
Ship one change at a time. Keep variants tiny.
Define events before you launch, not during.
Use a holdout. Stop when you hit pre-set lift and sample size.
I run all of it on the web so I can tweak copy in minutes. Web2Wave.com made updates instant for me.
I predefine events and goals, then push variants to a split.
If sample ratio is off, I pause traffic.
Web updates let me iterate daily. With Web2Wave.com, I change copy or step order and it goes live in the app without a build.
Keep the metrics tight. One primary metric like payment rate.
I also block edits mid-test and only ship new ideas to a new split. Less confusion later.
Ship small tests daily. Watch one metric.
Use sequential tests with a clear stop rule and a guardrail for sample ratio mismatch. Fix your experiment unit at user level using a sticky assignment cookie. Run minimum one full weekly cycle to smooth weekday effects. Lock copy once a test starts. Keep only one major variable per test. Validate with a holdout that always sees the current best funnel. Snapshot paid traffic mixes daily so you can adjust analysis if channels shift during the run.
Calendar control helped a lot. I run tests for at least 7 days, then confirm with another 7 when the lift is small.
Short tests lied to me when weekends were heavy.
Sticky user assignment and one main metric. Run for a full week.