How do I A/B test virtual try-on?
Looksy does not list a built-in A/B testing feature, so the practical approach is a structured comparison: measure before and after enabling try-on, or split comparable products into cohorts, using Looksy analytics alongside Shopify reports.
Check what Looksy’s analytics dashboard supports
Looksy’s App Store listing describes Analytics — seeing what shoppers try on, add to cart, and buy — but it does not advertise split testing, traffic allocation, or holdout groups. Before designing your own test, check inside the app whether any comparison or reporting view fits what you need, and review the linked resources (FAQ, Tutorial, App Documentation) or ask Looksy support directly. Knowing exactly what the dashboard reports determines which of the two methods below is easier to run: a before/after window comparison or a product-cohort split.
Method one: a before/after comparison
Pick a clean baseline window before enabling try-on — long enough to smooth out day-to-day noise, and free of sales, launches, or major ad changes. Record conversion rate, add-to-cart rate, and average order value from Shopify’s own reports for that window. Then enable try-on and measure an equally long window under similar conditions. The comparison is only fair if nothing else moved: same traffic sources, same pricing, same season. If a promotion lands mid-test, extend the window or restart it rather than explaining the spike away.
Method two: a product-cohort split
If Looksy lets you scope try-on to specific products — confirm how placement is controlled in the app and theme editor before planning this — you can enable it on one group of similar products and leave a comparable group without it, then run both over the same period. Because both cohorts see the same traffic, season, and promotions at the same time, this controls for the timing problems that weaken before/after tests. Match cohorts on price range, category, and traffic volume. Compare add-to-cart and conversion rates between the groups in Shopify, and use Looksy analytics to check that the try-on cohort actually used the feature.
Reading the results without fooling yourself
Run the test long enough to cover at least one full buying cycle for your products — an impulse-priced accessory converts faster than a considered purchase. Look at the gap between try-on usage and outcomes: if few shoppers used try-on, a flat result says more about placement and promotion than about the feature itself. Judge revenue per visitor, not just conversion rate, since Looksy’s Bundles & Upsells can move order value as well as order count. Treat one test as one data point, and rerun it before making a final decision.
Sources: the public Shopify App Store listing for Looksy. Last checked 27 July 2026.
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