Shopping cart: What It's Worth
Hypothesis
If we A/B test What It's Worth on shopping cart pages, then we can measure its impact and determine if it suits our context
Test Results
Key Learning
Context: How prices are displayed on the shopping cart directly influences perceived value and willingness to buy.
What was tested: What It's Worth — 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. Pricing tests that are inconclusive may indicate the price itself isn't the issue — the perceived value or the framing might matter more.
How to Apply This to Your Site
This experiment tested shopping cart: what it's worth but produced no statistically significant change. The test was run on a cart 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 pricing 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 What It's Worth improves conversion performance. This is a meta-pattern derived from multiple A/B tests across different companies. Applicable to home-landing, pricing, product, shopping-cart 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
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Problem: How prices are displayed on the pricing page directly influences perceived value and willingness to buy.
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Problem: How prices are displayed on the pricing page directly influences perceived value and willingness to buy.
Product: More Or Fewer Plans on Product Page
Context: How prices are displayed on the product directly influences perceived value and willingness to buy.
Signup: Payment First
Context: Friction during the signup process causes users to abandon right when they're closest to converting.