Size recommendation for fashion e-commerce
Size recommendation helps online shoppers pick a size suited to their body by cross-referencing their declared or estimated inputs with your size chart. This page explains how it differs from a static chart, how it pairs with virtual try-on, and how to scope a pilot.
Book a demoWhat size recommendation is
Size recommendation is a service that takes shopper inputs (usual size, declared or estimated measurements, photos depending on the enabled feature) and returns a size suited to a given garment, based on the brand’s size chart and the cut of the product.
For a fashion brand or retailer, the commercial goal is to reduce size uncertainty before the add-to-cart click. That supports the measurement of conversion, returns, and revenue per visitor — without promising guaranteed outcomes.
Size recommendation vs size guide vs size chart
Three terms overlap.
- Size chart: static table listing measurements per grade (XS, S, M, L) for a brand or cut. It’s a reference, not advice.
- Size guide: page or popin presenting the chart and explaining how to measure. The shopper still owns the decision.
- Size recommendation: dynamic service combining shopper inputs with the chart to propose a size with a fit score. The shopper still decides, but the uncertainty is reduced.
Size recommendation builds on the existing chart — it does not replace it. The pilot is scoped around the product data, cuts, and collections you already maintain.
Why a static chart alone often leaves uncertainty
A static size chart works when the brand grid is clear and stable and the shopper knows their measurements. In practice, several frictions persist.
- Shoppers don’t always know their measurements, or don’t have a tape measure to hand.
- Cuts vary within the same brand — fitted, relaxed, oversize — and don’t fit the same size identically.
- Charts may be fragmented by collection, category, or supplier, which complicates reading.
- Shoppers often default to ordering two sizes to compare, increasing the return rate.
Size recommendation addresses those frictions by combining available shopper data with your existing chart to propose a size with a fit score. Uncertainty drops, without a guarantee of perfect fit.
How size recommendation complements virtual try-on
Virtual try-on addresses “what does it look like on me”. Size recommendation addresses “which size should I order”. Both questions coexist at the purchase moment.
The FittingMe.ai widget pairs both in the same component. The shopper sees a preview of the drape on their silhouette and receives a recommended size, without changing environment and without reading a separate guide. For the brand, that limits the integration surface and the number of consent paths to scope.
Merchant data needed
To scope a pilot, the merchant team usually prepares:
- Size charts and guides in the format you currently maintain (per brand, per collection, per cut depending on your organization).
- Product variants with their grade and cut — fitted, relaxed, oversize.
- Collections and categories where the cut varies significantly.
- Product metadata useful to recommendation — composition, stretch, reference fit model size.
The pilot is scoped around existing data. It does not require rebuilding your catalogue or centralizing a new master.
Measurement and pilot design
A size-recommendation pilot is measured as a product test: a cohort of equipped product pages, a non-equipped control group, and a measurement plan for agreed indicators (for example conversion, returns by reason, revenue per visitor). Public benchmark ranges stay indicative; the pilot validates outcomes against your catalogue.
In parallel, procurement and compliance review covers processing roles, consent flow, retention and deletion, subprocessors, and transfers. FittingMe.ai shares the material needed during qualified commercial review.
What to keep out of scope for the first pilot
Keeping the first pilot tight helps isolate the impact of size recommendation. A few common scope cuts that work well: pilot one product family before extending to the full catalogue; pick categories where return rate is high enough to detect a delta with available traffic; agree the measurement window before launch rather than mid-flight. Vanity expansions — extra languages, extra cohorts, extra reporting layers — can wait until the core delta is confirmed.