Checkout experiment
Experiment: checkout friction test
Checkout friction tests usually win in the friction-removal direction — removing optional fields, adding one-tap payment methods, simplifying the form. The framework below names the specific changes worth testing and the sample-size discipline.
Min sample size: 300+ checkout starts per variant for a 20%+ lift detection.
Duration: 14-30 days minimum at adequate traffic.
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Hypothesis structure
Removing/adding [SPECIFIC FRICTION ELEMENT] from the checkout will increase checkout-completion rate by at least [EXPECTED LIFT] because [SPECIFIC REASON].
If you cannot complete this template, you do not have an experiment — you have a guess.
Variant design
ONE change per test: remove a field, add a one-tap pay option, change a button label. Multi-variable changes muddle attribution.
Primary metric
Checkout completion rate (paid orders / checkout starts).
Secondary metrics (watch but do not decide on)
- Time-to-completion (faster = less friction).
- Field-level abandonment (which step drives the loss?).
Procedure
Step 1
Audit the current checkout for friction candidates
Optional fields, mandatory fields with low downstream value (phone number?), missing one-tap pay options. Make the list before testing.
Step 2
Pick the highest-suspected-friction element
Test the one most likely to move the needle first. Phone number removal often produces lift; payment method addition often produces lift.
Step 3
Split new checkout sessions, not pageviews
Session-level splits ensure each visitor sees one variant consistently across the flow.
Step 4
Track abandonment by step
Which step lost the visitor? The variant should reduce abandonment at the specific step you changed.
Step 5
Decide based on completion rate
Higher completion = ship. Equal = keep simpler variant. Lower = unexpected; investigate before reverting.
Self-deceptions to avoid
- Removing a field that produced useful downstream data without measuring the downstream loss.
- Testing 3 changes at once. Indie SaaS volume rarely supports multi-variant attribution.
- Treating cart-recovery email lift as part of the checkout test result. Cart recovery is a separate funnel step.
What success looks like
Variant lifts checkout completion 15%+ AND does not measurably hurt any downstream metric (support load, refund rate, average order value).
Related benchmark
See the directional range for checkout completion rate to calibrate the expected lift in your hypothesis.
Frequently asked
- Should I remove the email field?
- No. Email is the channel that produces post-purchase value (receipt, support, follow-up). Removing it costs more than it saves.
Other experiments
Test on a page that is already pointed in the right direction
A/B tests on a misaligned page produce two losing variants. The diagnostic labels the alignment problem first; the test optimizes within the right alignment.