Listing A/B Test Plan
Test one change at a time and learn from it.
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Design a listing test plan. Listing(s) to improve and baseline stats (views, click-through rate, favorites, add-to-cart, orders, conversion rate) for the last 30 to 90 days: [BASELINE_STATS]. Elements I could change (main photo, title, price, description, tags, shipping price, first line): [CANDIDATE_CHANGES]. Platform features (built-in experiments or manual changes): [PLATFORM_FEATURES]. Return: 1. Hypotheses in the form "If we change X to Y, then metric Z will improve because ...", prioritized by expected impact and ease. 2. A test calendar: one change per listing at a time; test duration (at least two full weekly cycles and enough orders); keep other factors steady; avoid testing during holiday spikes unless comparing the same period. 3. For manual tests: how to record the date, the exact change, and the screenshots; how to compare equivalent periods; how to account for seasonality (compare with a control listing that you do not change). 4. Sample size guidance in plain terms: small shops rarely get statistical certainty; use directional evidence and repeat; a rough rule to wait for at least 100 views and several conversions per variant, labeled as a rule of thumb. 5. A results log template and decision rules (keep, revert, test again). 6. Ethical guardrails: no misleading photos or false scarcity. Include three example tests with predicted outcomes written as hypotheses, not facts.
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