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Run A/B tests per collection to find out whether a merchandising change actually performs better — different content blocks, imagery or ordering.

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How a test works

  1. Pick a collection and set up variant A (control) and variant B.
  2. Give the experiment a name, a start and end date, and optionally an email for notifications.
  3. While the test runs, traffic is split between the variants.

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Reading the results

For each variant you get daily time series of page views, clicks, add-to-carts and purchases, plus the per-view rates (click-through rate, add-to-cart rate, purchase rate). Add-to-carts and purchases only count products that are actually in the collection. Statistical significance is computed with a two-tailed z-test for each rate metric, so you can tell a real effect from noise. When the test concludes, an AI-written summary states which variant won, with what confidence, and suggests next steps.
Give tests enough time and traffic. Low-traffic collections need longer test windows before the p-values become meaningful.