FitRight helps fashion ecommerce teams use the data they already have to improve size decisions before checkout.
Timing is key
FitRight shifts fit insight from post-purchase reports to the product page, intervening before the customer makes a decision.
Leveraging owned data
It analyzes the order, return, and exchange history you already possess to identify patterns without requiring new customer inputs.
Selective Precision
Guidance is only displayed when the data signal is strong enough to be useful; if the evidence is weak, the system remains silent.
Risk-Managed Rollout
Teams can validate value through a controlled deployment on a defined set of products before scaling across the full catalogue.
The problem
Most fashion brands already know returns are hurting them.
They can see which products cause issues.
— They know size and fit are a primary driver.
— They review the data regularly.
— The problem is when that understanding becomes usable.
In most organisations, fit insight lives in reports, dashboards, or in the P&L reviewed after orders have shipped and margin has already gone.
By the time confidence builds, the customer has already decided.
Returns don’t usually come from missing information.
They come from the gap between knowing something and being able to use it at the moment it matters.
They can see which products cause issues.
— They know size and fit are a primary driver.
— They review the data regularly.
— The problem is when that understanding becomes usable.
In most organisations, fit insight lives in reports, dashboards, or in the P&L reviewed after orders have shipped and margin has already gone.
By the time confidence builds, the customer has already decided.
Returns don’t usually come from missing information.
They come from the gap between knowing something and being able to use it at the moment it matters.
Why this is hard
Reducing returns isn’t just a sizing problem.
It’s a timing problem.
Fit insight typically accumulates after checkout through returns, exchanges, reviews, and customer service interactions.
But returns are decided before that insight is visible.
Until fit information can reach the point where size choices are made, most returns initiatives are forced to explain the past rather than change the next outcome.
It’s a timing problem.
Fit insight typically accumulates after checkout through returns, exchanges, reviews, and customer service interactions.
But returns are decided before that insight is visible.
Until fit information can reach the point where size choices are made, most returns initiatives are forced to explain the past rather than change the next outcome.
What FitRight does
FitRight offers sizing and fit decision guidance for fashion ecommerce teams and customers.
It analyses customer behaviour across orders, returns, exchanges and reviews to identify where fit insight is strong enough to give a recommendation.
That insight is surfaced selectively on product pages, When the data isn’t strong enough, nothing is shown.
FitRight doesn’t replace judgement or force coverage across a catalogue using AI assumptions.
It supports better decisions where the data already justifies it.
It analyses customer behaviour across orders, returns, exchanges and reviews to identify where fit insight is strong enough to give a recommendation.
That insight is surfaced selectively on product pages, When the data isn’t strong enough, nothing is shown.
FitRight doesn’t replace judgement or force coverage across a catalogue using AI assumptions.
It supports better decisions where the data already justifies it.
How teams start
Data audit
We assess order, return, exchange and customer service data to see where patterns are already stable enough to support fit decisions.
Agree success criteria
Teams define what matters upfront which products, which sizes and which metrics will determine whether FitRight is doing useful work.
Onboarding
FitRight runs alongside existing size guidance on an agreed set of products. Nothing is removed or overridden.
Measurement
Impact is tracked against agreed metrics, including returns behaviour and conversion, over a defined period.
Expand where it earns its place
FitRight is rolled out further only where the evidence supports it. Products without sufficient data are left untouched.
Insights
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:
Typically:
- £2m–£20m online revenue
- Shopify stores
- 30–40% return rates
- Size and fit is a major driver of margin leakage
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