Conversion · ANALYTICS
Raise checkout completion under an A/B framework
+18%
Most checkout changes ship on taste and are never read back, so the team learns nothing from either the wins or the losses. This engagement puts a hypothesis queue behind the checkout and a significance gate in front of every release.
The process
Instrument the checkout
Events on cart, address, shipping, payment and confirmation in GA4 or PostHog, with error and validation states captured as their own events.
Build the hypothesis queue
Drop-off by step and by segment turns into a ranked list of hypotheses, each written as one change and one expected direction.
Size the test before running it
Baseline conversion and weekly order volume set the minimum detectable effect and the runtime — tests too small to read are cut, not run.
Ship one variant at a time
Each test runs as a clean A/B split with a fixed stopping rule; traffic allocation and exposure are logged in the warehouse alongside orders.
Read it and retire it
Results read at the planned stopping point, winners rolled to 100 percent, losers documented so the same idea is not re-litigated next quarter.
What we need from you
- Analytics access and the checkout page structure
- Order volume history for sizing
- An owner who can approve variant releases
What you get
- Instrumented checkout funnel
- Ranked hypothesis queue with sizing per test
- Test log with decisions and shipped winners
Timeline
Four to eight weeks for the first read cycle.