Listing: Conversational Filters
Hypothesis
If we implement 'Conversational Filters' on listing pages (In this experiment, conversational filters were tested at the top of some listing pages), then key conversion metrics will improve.
Test Results
Key Learning
Context: Users can't quickly find relevant products or content on the listing, leading to frustration and early exits.
What was tested: REAL-WORLD TEST: 'Conversational Filters' was tested on a live listing page. The test involved 189,872 real visitors. Full statistical results require paid access. Test methodology: In this experiment, conversational filters were tested at the top of some listing pages. Instead of showing one set of product filters, customers were...
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: conversational 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
In this experiment, conversational filters were tested at the top of some listing pages. Instead of showing one set of product filters, customers were shown three sets of product questions. After selecting each answer, product results would narrow and update further down on the page. Impact on adds to cart and sales were 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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