General: Navigation Optimization
Hypothesis
If we test a similar change on our any pages as tested, then our conversion metric will likely improve based on their implementation decision.
Test Results
Key Learning
Problem: Users arriving at the general can't efficiently find what they're looking for, increasing bounce rates.
What worked: implemented this UI change (Dec 27, 2019). Implementation suggests positive internal results
Takeaway: Even small lifts compound — across thousands of sessions, this adds up. Navigation improvements affect every page — measure downstream engagement and conversion to understand the full impact.
How to Apply This to Your Site
This experiment demonstrated that general: navigation optimization can improve conversions. The test was run on a landing page page in the e-commerce industry.
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
At first glance some A/B tests may seem like they are beautifully simple with a single controlled variable. Yet as we look at the experiment in more detail, more and more subtle differences start coming into view. This I think is the case with 's most recent experiment where it seems that only a dropdown menu was shifted into a hamburger one - which ended up winning.
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
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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.
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Context: Users arriving at the product can't efficiently find what they're looking for, increasing bounce rates.