Product: Unit Prices
Hypothesis
If we A/B test Unit Prices on product 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 product directly influences perceived value and willingness to buy.
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. 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 product: unit prices but produced no statistically significant change. The test was run on a product 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 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 Unit Prices improves conversion performance. This is a meta-pattern derived from multiple A/B tests across different companies. Applicable to product 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.
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