Virtual Try-On vs. Size Charts: Which Actually Reduces Returns More?
Retailers have relied on size charts for decades to help customers choose the right fit online. But with 72% of fashion returns still caused by fit issues, it's clear that size charts alone aren't enough. Virtual try-on technology has emerged as a powerful alternative—but how do the two approaches compare when it comes to reducing returns, increasing conversion, and building customer confidence?
We compared the data so you don't have to guess.
The Size Chart Reality
Size charts are the industry standard: measurements, conversion tables, and "how to measure" guides. They're low-cost and easy to implement. But the numbers tell a different story.
What the Data Shows
| Metric | With Size Charts Only | With Virtual Try-On |
|---|---|---|
| Return rate (fit-related) | ~28–35% of orders | ~18–22% of orders |
| Conversion lift | Baseline | +50–94% on try-on products |
| Customer confidence | Low (guesswork) | High (visualization) |
| Size-related support tickets | High | Significantly lower |
| Average order value | Baseline | +15–25% with "complete the look" |
Sources: Industry benchmarks (NRF, IHL Group); virtual try-on case studies (2024–2025).
Why Size Charts Fall Short
Size charts have fundamental limitations:
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No body shape – They show measurements, not how the garment will look on the customer's body. Two people with the same measurements can have very different shapes.
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Brand inconsistency – A "medium" or "size 10" varies by brand, category, and even style. Customers can't reliably translate charts across your catalog.
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Static and passive – Customers must measure themselves, find the right row, and hope. There's no feedback loop or visual confirmation.
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High cognitive load – Converting measurements to a size and then to confidence is hard. Many shoppers abandon or guess, which drives returns.
The Virtual Try-On Difference
Virtual try-on doesn't replace size charts—it adds a visual fit layer on top of them. Customers see how garments look on their own body (or a representative avatar), which addresses the main driver of returns: uncertainty about fit and look.
How the Two Work Together
- Size charts – Set expectations (e.g., "this runs small") and support customers who prefer text/numbers.
- Virtual try-on – Reduces uncertainty by showing fit and style on the customer, which leads to fewer wrong-size and wrong-style returns.
When combined with AI size recommendations, virtual try-on can suggest a size based on the customer's try-on and body data, so you get the best of both: clarity from the chart and confidence from the visualization.
Head-to-Head: Key Metrics
Return Reduction
- Size charts only: Fit-related returns typically stay in the 28–35% range of online orders because customers still guess.
- Virtual try-on (with size recs): Fit-related returns often drop to 18–22%, with many case studies showing 25–35% overall return reduction.
Virtual try-on wins on return reduction because it reduces the "wrong size" and "didn't look like I thought" returns that size charts alone cannot fix.
Conversion and Engagement
- Size charts: Little to no measurable lift in conversion; they're expected table stakes.
- Virtual try-on: 50–94% conversion lift on product pages where try-on is available (source: AR/VR commerce studies, 2024). Higher engagement (time on page, try-ons per session) correlates with higher conversion.
Virtual try-on wins on conversion because it increases confidence and reduces purchase friction.
Customer Confidence and Support
- Size charts: Confidence is low; support tickets about size and fit remain high.
- Virtual try-on: Customers who use try-on report higher confidence and fewer size-related support requests, as they've already "seen" the fit.
Virtual try-on wins on confidence and support load when the experience is fast and accurate.
Cost and Implementation
- Size charts: Low cost, quick to implement, easy to maintain.
- Virtual try-on: Higher upfront cost and integration effort, but ROI of 300–450% is common when return reduction and conversion lift are factored in.
For ROI-focused brands, virtual try-on wins over the long term despite higher implementation cost.
When to Use Which (Or Both)
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Size charts only – Reasonable for very low AOV, minimal returns, or brands not yet ready to invest in try-on. You still need clear, consistent size charts and fit notes.
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Virtual try-on – Best when returns and fit uncertainty are pain points, when you want to lift conversion and AOV, and when you can support mobile-first, fast-loading try-on.
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Both – Ideal for most fashion brands: keep size charts and fit guidance, add virtual try-on (and optionally AI size recommendations) on key categories to reduce returns and increase conversion.
Conclusion
Size charts are necessary but not sufficient: 72% of fashion returns are fit-related even with size charts in place. Virtual try-on adds a visual, confidence-building layer that consistently reduces returns and increases conversion in the data we've seen. The best strategy for most retailers is to keep size charts and add virtual try-on where it matters most—then measure return rates, conversion, and support tickets to validate the impact for your own business.
Sources
- NRF / IHL Group – retail return benchmarks
- AR/VR commerce and virtual try-on case studies (2024–2025)
- Industry fit-and-returns research (e.g., 72% fit-related returns)
Want to see how virtual try-on could reduce your returns and lift conversion? Join our waitlist for early access to try-on technology that works alongside your size charts.