Product: Social Counts
Hypothesis
If we implement Social Counts on product pages, then conversion rate will improve because this is a repeatedly validated UX pattern.
Test Results
Key Learning
Problem: The registration experience on the product asks too much too soon, causing potential users to drop off.
What worked: has been validated across multiple real A/B tests. The evidence (1.0) suggests it is Likely better. Use this as a high-priority test hypothesis backed by industry meta-analysis. (+2.2% lift)
Takeaway: Even small lifts compound — across thousands of sessions, this adds up. Now test the placement of this social proof — positioning near CTAs, in pricing sections, and in checkout flows often amplifies the effect.
How to Apply This to Your Site
This experiment demonstrated that product: social counts can produce a +2.1% improvement in conversions. The test was run on a product page page in the cross-industry industry.
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 Social Counts improves conversion performance. Based on 1.0 evidence points, version B is Likely better. Applicable to home-landing, listing, product, signup, thank-you 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
Product: Benefit Testimonials
Context: Users on the product need validation from others before committing — without visible proof of success, they hesitate.
Checkout: Customer Star Ratings
Context: Users on the checkout need validation from others before committing — without visible proof of success, they hesitate.
Checkout: Testimonials
Context: Users on the checkout need validation from others before committing — without visible proof of success, they hesitate.
Checkout: Countdown Timer
Context: Without clear urgency signals, users delay their decision on the checkout, leading to drop-offs and abandoned sessions.