Listing: Persistent Filters
Hypothesis
If we implement 'Persistent Filters' on listing pages (The experiment goal was automatically applying filters the users have already done in our result page, during their navigation to the site), then key conversion metrics will improve.
Test Results
Key Learning
Context: Users arriving at the listing can't efficiently find what they're looking for, increasing bounce rates.
What was tested: REAL-WORLD TEST: 'Persistent Filters' was tested on a live listing page. The test involved 441,738 real visitors. Full statistical results require paid access. Test methodology: The experiment goal was automatically applying filters the users have already done in our result page, during their navigation to the site. The variat...
Result: No statistically significant difference was detected. No significant difference suggests users adapted to the change quickly, or the variation didn't address the actual friction point. Try testing more targeted elements.
How to Apply This to Your Site
This experiment tested listing: persistent filters but produced no statistically significant change. The test was run on a category page 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 layout tests typically require large sample sizes to detect small effects. Run your test for at least 2 full business cycles to account for weekly traffic patterns.
What Was Tested
The experiment goal was automatically applying filters the users have already done in our result page, during their navigation to the site. The variation always applied the filters in the same session and asked users on new sessions.
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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