The sample size needed for a Shopify A/B test depends on baseline performance, the smallest effect worth detecting, traffic allocation, outcome variability, and the decision rule. No single visitor count fits every store.
Baseline rate sets the scale
A store with a one percent conversion rate observes fewer purchases than a store with a five percent rate at the same traffic level. Revenue metrics also vary more when order values are uneven.
Define a useful effect
A tiny change may be measurable but not worth design, engineering, or operational cost. Set the minimum improvement that would change the rollout decision and estimate traffic around that threshold.
Plan for eligible traffic
Use only visitors who can enter the experiment, then divide that traffic across variants. Country, device, returning-status, or URL rules can make the usable sample much smaller than total sessions.
Reassess when reality differs
Traffic mix, conversion rate, or variance may differ from the planning estimate. Review whether the experiment remains capable of producing a decision, but do not change the success rule after seeing which variant leads.
Run the test in Shopify
Sofcy supports URL, template, theme, and custom JavaScript API experiments.
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