1. The returns problem
Fashion has the highest return rate in ecommerce. Widely reported industry figures put apparel returns at roughly 24–40% of orders, rising further in categories like occasionwear and where shoppers "bracket" — buying multiple sizes intending to send most back. Each return is expensive: once logistics, handling, inspection and markdown or write-off are counted, the cost to process a single returned order is commonly estimated at £17–£21 (around $20–30).
The cause is concentrated and addressable. Across industry studies, fit and size account for roughly two-thirds (~67%) of fashion returns — the shopper simply could not tell how the garment would fit before it arrived. Returns are also an environmental cost: processing, transport and the share of returned goods that never re-sell carry a meaningful carbon and landfill footprint.
2. What virtual try-on changes
Virtual try-on attacks the exact uncertainty that drives both lost sales and returns: "how will this look and fit on me?" The reported effects are consistent in direction and large in magnitude:
- Returns: reductions of ~25–40% are associated with virtual try-on in industry studies, concentrated in fit-related returns.
- Conversion: reported uplift spans roughly 10% to multiples of baseline, varying by category and — critically — by output quality.
- Confidence: surveys routinely find the large majority of shoppers (commonly cited around 98%) say seeing a product on a body helped their decision.
- Engagement: try-on lifts time on the product page and intent to purchase.
This is why the market is growing fast: widely cited estimates put it at around $15.18bn in 2024, reaching ~$48.1bn by 2030 — a compound annual growth rate in the low twenties.
3. The variable that decides everything: output quality
Here is the finding that matters most, and the one most benchmarks miss: the gains above are not evenly distributed. They accrue to tools whose output is genuinely photorealistic and whose experience is low-friction. A render that looks even slightly synthetic does the opposite of its job — it reduces confidence and cheapens the product, which is fatal for a premium brand. The spread of reported conversion results (from ~10% to multiples) is, in large part, a spread of output quality and customer experience.
Two practical levers separate try-on that works:
- Realism & accuracy. Correct drape, proportion, colour and lighting on the shopper's own body — not an obvious composite or a generic stand-in model.
- Friction. The shopper should see themselves from a single uploaded photo, instantly, on the product page — not build an avatar or a "digital twin," and not wait.
4. Why Rendered Fits is built to capture the upper end
Rendered Fits is engineered for precisely the conditions under which the benchmark's gains are realised — and it is, by design, the strongest option for premium Shopify fashion brands on every axis that determines results:
- Superior output quality and accuracy. Photorealistic, full-garment rendering tuned for the realism premium fashion demands — the single biggest driver of whether try-on converts.
- The best customer experience. One photo, instant result, clean and on-brand on the product page. No digital twin, no avatar build, no friction.
- Direct impact on returns and conversion. Built end-to-end around fit confidence — the mechanism behind both numbers.
- Shopify-native and live in minutes. No enterprise integration, no 3D pipeline — it runs from your existing product imagery.
Where enterprise and ultra-luxury platforms require bespoke deployments, and value-tier apps trade away the output quality that makes try-on work, Rendered Fits is built to deliver the realism, experience and results of the former with the accessibility and speed of the latter. For premium Shopify brands, it is the clearest route to the upper end of every figure in this benchmark.
5. How to benchmark try-on on your own store
Measure four things over a clean test window, ideally split-tested:
- Return rate on try-on-enabled products vs control — watch fit-related returns specifically.
- Conversion rate of sessions that engaged with try-on vs those that didn't.
- AOV and bracketing — are shoppers ordering fewer "safety" sizes?
- Engagement — try-on completion rate and time on PDP.
The fastest, most honest test is on your own garments. Send one product URL and judge the output on a piece you know.
Frequently asked questions
What percentage of fashion returns are fit-related?
Industry figures consistently put fit and size at roughly two-thirds of fashion returns — commonly cited around 67%. It is the single largest, most addressable driver of returns.
How much does virtual try-on reduce returns?
Industry studies associate virtual try-on with return reductions in the region of 25–40%, concentrated in fit-related returns, when the output is realistic enough to give shoppers genuine fit confidence.
Does virtual try-on increase conversion?
Yes. Reported conversion uplift ranges from roughly 10% to multiples, depending on category and output quality, because it resolves the fit and suitability doubt that stalls fashion purchases.
What makes virtual try-on actually work?
Output realism and low friction. A render that looks synthetic undermines confidence; an avatar-building step adds friction. The benchmark's gains accrue to photorealistic, one-photo, on-page experiences — which is what Rendered Fits is built for.
Methodology & sources
This benchmark aggregates widely reported, publicly available industry figures on fashion ecommerce returns, fit-related return drivers, return processing costs, virtual try-on conversion and return effects, and virtual try-on market size, as reported by retail and ecommerce research through 2024–2026. Ranges are presented where industry sources differ. Figures are indicative of the category, not specific to any single retailer. Rendered Fits positioning reflects the product's design priorities; brands should validate results on their own catalogue.