Identifies and fixes the products that drive repeat fit issues and margin leakage.
Improves confidence at the point of purchase
Gives shoppers clear, product-specific sizing guidance when they’re choosing a size.
Turns fragmented data into actionable fit insight
Unifies orders, returns, reviews and exchanges into a single fit intelligence layer.
Delivers size guidance directly on product pages
Puts real sizing insight exactly where the decision happens. On the PDP.
The Problem
Fashion e-commerce has a structural returns problem. 30–40% of apparel orders are returned. Size and fit is the number one reason.
Merchants are caught between a rising trend to charge for returns (potentially based on behaviour - see ASOS) or to continue accepting the costs.
For a typical £3m fashion brand, returns cost hundreds of thousands per year once you account for:
logistics and handling
margin leakage from discounted resale
write-offs
operational drag
Most of this loss is predictable. And a lot of it is preventable.
Most returns solutions focus on the end of the customer journey once the return is already in progress. The majority aren’t acting on the data available at the moment the customer chooses a size.
The FitRight Solution
We analyse:
order and return behaviour
fit-related language in customer reviews
size exchange patterns
And turn that into high-confidence size recommendations on product pages.
If the data isn’t strong enough, we’ll then turn this into a first party data capture opportunity.
No guessing. No forced recommendations. No false certainty.
How it works
Multi-Source Fit Intelligence
Connects Shopify orders and returns, customer reviews, and exchange data into a single fit signal. No single proxy. No guessing.
Product-Level Returns Diagnostics
See exactly which SKUs and Variants are driving returns, why they’re being sent back, and whether they run large, small, or have construction issues.
Size Recommendations on PDPs
Delivers size guidance directly on product pages - where the customer actually makes the decision.
Confidence Scoring
Only shows recommendations when the data is statistically meaningful. If the signal isn’t strong enough, nothing is shown.
Deterministic & Explainable Logic
No black-box AI. Every recommendation is traceable back to real customer behaviour.
Fast Shopify Integration
Connect your store, returns platform and reviews. FitRight starts analysing your data in hours, not months.
FAQ
Size guides are static and generic. Fit quizzes rely on self-reported data. FitRight uses real customer behaviour — orders, returns, reviews and exchanges — to generate product-level size recommendations that update as your data grows.
FitRight only shows recommendations when the signal is statistically meaningful. If there isn’t enough data for a product or variant, nothing is shown. No guessing. No filler. This protects customer trust and brand credibility.
FitRight uses deterministic, explainable logic for fit recommendations. There’s no black-box model making assumptions. Every recommendation can be traced back to real customer behaviour.
Most Shopify stores are connected in a few hours. Once your data is ingested, FitRight starts analysing immediately. There’s no lengthy implementation or consultancy phase.
FitRight is built for fashion e-commerce brands where returns are a material business problem. Typically: