Listing: Visible Filters
Hypothesis
If we A/B test Visible Filters on listing pages, then we can measure its impact and determine if it suits our context
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: 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. Navigation tests that don't show a difference may indicate the issue is content findability, not menu structure. Consider search and filtering improvements.
How to Apply This to Your Site
This experiment tested listing: visible 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 navigation 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 Visible Filters improves conversion performance. This is a meta-pattern derived from multiple A/B tests across different companies. Applicable to listing 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.
Related Experiments
Listing: Instant Filter Results
Context: Users can't quickly find relevant products or content on the listing, leading to frustration and early exits.
Product: Exposed Menu Options
Problem: How prices are displayed on the product directly influences perceived value and willingness to buy.
Listing: Multiple Steps
Problem: Multi-step processes on the listing can overwhelm users if they can't see how far along they are or how much is left.
Product: Open In A New Tab
Context: Users arriving at the product can't efficiently find what they're looking for, increasing bounce rates.