Checkout: Auto Suggest
Hypothesis
If we implement 'Auto Suggest' on checkout pages (In this experiment (1) the zip code field position was moved up, right below the last name), then key conversion metrics will improve.
Test Results
Key Learning
Context: Friction during the checkout process causes users to abandon right when they're closest to converting.
What was tested: REAL-WORLD TEST: 'Auto Suggest' was tested on a live checkout page. The test involved 4,735 real visitors. Full statistical results require paid access. Test methodology: In this experiment (1) the zip code field position was moved up, right below the last name. And (2) entering the zip code would populate the state and...
Result: No statistically significant difference was detected. This null result is still valuable — it narrows the search space and helps calibrate your minimum detectable effect for future tests.
How to Apply This to Your Site
This experiment tested checkout: auto suggest but produced no statistically significant change. The test was run on a checkout page in the cross-industry industry. Inconclusive results suggest this particular change may not be a priority — focus testing effort on higher-impact areas.
Before you test: Consider that personalization tests typically require adequate traffic to reach statistical significance. Run your test for at least 2 full business cycles to account for weekly traffic patterns.
What Was Tested
In this experiment (1) the zip code field position was moved up, right below the last name. And (2) entering the zip code would populate the state and city using an autofill API call. Impact on orders completed was measured.
Methodology
Build On These Learnings
Save your own experiments, spot winning patterns across your test history, and stop repeating what's already been tried.
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