Healthcare · Growth
Insurance step A/B test
Testing an insurance card scan against manual entry in the booking flow, with confirmed bookings as the guardrail.
Card scan vs. manual entry
+11.9%
Significant winInsurance step completion
095% interval +7.4% to +16.4%
Card scan
Manual entry
control
Who it's for
Growth and product teams who test changes to the booking flow and need results they can trust before shipping.
What it solves
Analyze the booking-flow test on the exposure data in your warehouse, with the same booking definitions as your dashboards, and know when the result is safe to ship.
Questions it answers
Did the card scan lift insurance step completion?
Completion rose from 72.0% to 80.6%, an 11.9% lift (95% interval +7.4% to +16.4%).
Did confirmed bookings or support calls move against us?
Confirmed bookings rose with it, and support calls per booking stayed within their guardrail.
- Is the lift significant, and how wide is the interval?
- Did the effect differ by device or specialty?
The impact
- Ship booking changes on evidence, not on a dashboard glance
- One definition of a completed booking, in tests and in reporting
- Guardrails catch a win that costs bookings elsewhere
The data it needs
- Exposure events from your experimentation or feature-flag tool
- Booking step events
- Appointment confirmation events
Read in place in your warehouse. Nothing is copied out.
Insurance step A/B test: frequently asked questions
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