Short answer: Size charts help shoppers choose a size, but they can't show how a garment will look on them. Virtual try-on shows the look, but it doesn't measure anyone or recommend a size. So check your return reasons: size complaints call for a better chart, and style complaints are where try-on helps. Many stores can use both.
Your size chart is there. Your shoppers are reading it. And they're still returning items that don't fit.
This isn't a failure of your sizing data—it's a fundamental limit of how size charts work. When a customer browses your product page and sees measurements (chest: 38", length: 28"), they're trying to predict how a garment will look and feel on their body. But a flat number can't answer the questions that actually matter: "Will this fit my shoulders? How does the fabric drape? Will this look right on me?"
The Return Problem: Why Size Charts Miss the Mark
In Loop Returns' platform data, 56% of apparel returns are about fit or size and 25% about style or preference. Vendors quote higher fit figures, so treat any single number as a rough guide, and read your own return reasons first.
For a Shopify apparel merchant, returns aren't just inconvenient—they're revenue killers. Every returned item costs you in processing, restocking, and lost customer lifetime value. We break down what each return really costs an apparel store separately, and how to set up Shopify returns so each one is tagged with its reason.
The paradox is this: merchants add size charts with good intentions, shoppers consult them, and returns stay high. This happens because size charts and the return problem are mismatched solutions to the problem they're meant to solve.
Why Size Charts Don't Solve Size-Related Returns
A size chart is a reference tool. It tells your customer what a "Medium" means at your brand and provides measurements so they can compare against garments they already own. But size charts operate in a world of abstractions—numbers, comparisons, assumptions—while the real problem is concrete and visual: "Will this item look right on me?"
Three factors explain why static measurement data fails:
Inconsistent sizing across brands. An XL at one retailer is not an XL at another. Fabric type, cut, gender-specific tailoring, and international production all create variations. A size chart that's accurate for one brand doesn't resolve the fundamental question: "How does this brand size compared to what I usually wear?"
Body proportions vary. Two shoppers might wear the same chest size but have different shoulder breadth, sleeve preferences, or torso length. A chart that lists "chest" and "length" doesn't capture whether the garment will fit someone 5'6" differently than someone 5'10"—or whether it will accommodate different body shapes altogether.
Measurements don't show how fabric behaves. Cotton stretches. Linen drapes. A knit item is cut differently than a woven one. A measurement tells you dimensions; it doesn't tell you whether an item will be snug or loose after the first wash, how a fabric will move with the body, or whether the cut will skim or cling. This is information no size chart can convey.
Size charts also need manual updates for new products and brand-specific offsets, so they can go out of date. If you're choosing an app to manage them, our guide to Kiwi Size Chart and its alternatives compares pricing and features.
Virtual Try-On: Solving the Fit-Confidence Problem
Virtual try-on tools approach the problem differently. Instead of providing data for the shopper to interpret, they answer the core question directly: "What will this look like on me?"
Here's how it works: a shopper uploads a photo of themselves from their phone or webcam. An AI system generates a realistic preview of them wearing the product, without leaving your store. They see the fit, the drape, the way the item looks on a real human body—not an abstract mannequin, not a size chart, but a close approximation of how it will appear when it arrives. The technical side, how AI try-on renders fit and drape, is covered in our technology guide.
This is a fundamentally different category of tool. A size chart is descriptive; a virtual try-on is visual and personal. The shopper doesn't have to interpret data or make assumptions. They see it. That's the core idea behind a virtual fitting room on your product pages: the shopper sees the answer instead of calculating it.
The evidence so far comes mostly from vendors, so read it with care. DRESSX reports a 30% reduction in returns for brands using its virtual try-on, and a 2026 DRESSX study of 1.2 million shoppers found try-on users converted from product view to purchase at 3%, versus 2% for non-users. Shoppers who choose to use try-on are usually more interested to begin with, so that gap shows correlation, not proof. Traion AI Virtual Try-On brings this kind of preview to Shopify product pages. For more on what to expect, see our guide to virtual try-on effectiveness.
The logic is simple: a shopper who has seen the item on themselves is less likely to be surprised by how it looks when it arrives. That addresses style doubt. It doesn't tell them whether the medium or the large will fit.
Side-by-Side: Size Charts vs. Virtual Try-On
A fair comparison shows the strengths and limits of each approach.
Size charts excel at:
- Setting baseline expectations (what your brand's sizing means)
- Providing a quick reference for shoppers who already know their size
- Being simple and inexpensive to implement
- Working for straightforward product categories (commodity t-shirts, basics)
But size charts struggle with:
- Addressing the core uncertainty: "Will this fit me specifically?"
- Requiring ongoing maintenance (product updates, brand-specific corrections)
- Building purchase confidence for style-conscious shoppers buying fitted or on-trend items
Virtual try-on excels at:
- Providing visual confidence through personal visualization
- Answering "how will this look on me?" for color, cut and style
- Delivering a memorable, premium shopping experience that builds brand perception
- Requiring minimal merchant maintenance (your product photos already exist)
But virtual try-on has real limits:
- It doesn't measure the shopper or recommend a size
- Shopper has to upload a photo (small friction; most solutions make this fast)
- AI accuracy varies with lighting, angle, and garment complexity
- Works best with standard apparel (t-shirts, dresses, basic structured items); less reliable for complex tailoring or layered outfits
- Higher implementation lift than a simple size-chart app (though modern no-code solutions have closed this gap significantly)
Building a Return-Reduction Strategy: Using Both
These tools don't compete, so you rarely have to choose between them.
A practical setup looks like this: your size chart provides the ground truth ("here's what our sizing means"), and virtual try-on shows the look ("here's how it appears on you"). A shopper can check the chart for the size, then use try-on to see themselves wearing the item.
What you add first depends on the store. For a basics-focused shop, a size chart might be enough, and you can add one for free without an app. For a trend-driven store where shoppers hesitate over how a piece will look, try-on speaks more directly to that doubt. Your return reasons will tell you which group you're in.
Getting Started: A Practical Path
If you're considering virtual try-on for your Shopify store, start simple.
Many virtual try-on solutions are designed for easy onboarding. Traion, for example, is free to install from the Shopify App Store and includes 10 free try-ons so you can test the impact on your specific store and product type. No coding required; the Try Me button loads on your product pages automatically once you enable the app embed in your Shopify theme editor.
If the results look promising—higher conversion rate, lower return rate—you can move to a paid plan. Traion's pricing starts at $19 for 100 try-ons, with larger packages available as your volume grows.
Install free and see for yourself how much visual fit confidence can change your metrics, or see Traion's analytics dashboard first. If you're comparing vendors first, see virtual try-on apps for Shopify, compared on pricing and features.
The Bottom Line
Size charts will remain part of your toolkit. They're inexpensive, and they answer the question try-on can't: which size to order.
Virtual try-on solves a different problem, the doubt about how an item will look. If you're choosing where to start, let your return reasons decide: a pile of "too small" returns points to the chart, and a pile of "not what I pictured" returns points to try-on.