Checkout: Customer Star Ratings
Hypothesis
If we A/B test Customer Star Ratings on checkout pages, then we can measure its impact and determine if it suits our context
Test Results
Key Learning
Context: Users on the checkout need validation from others before committing — without visible proof of success, they hesitate.
What was tested: has been validated across multiple real A/B tests. Use this as a high-priority test hypothesis backed by industry meta-analysis.
Result: No statistically significant difference was detected. Inconclusive social proof tests often mean the proof type or placement didn't match what users need at that moment. Try a different format or position.
How to Apply This to Your Site
This experiment tested checkout: customer star ratings 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 social proof 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
Testing whether Customer Star Ratings improves conversion performance. This is a meta-pattern derived from multiple A/B tests across different companies. Applicable to checkout, home-landing, listing, product, signup page types.
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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Checkout: Testimonials
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