Beauty ecommerce returns and what virtual try-on fixes

Returns in beauty are hard to count. A lot of what is sold cannot be sent back once it is opened, for hygiene reasons, so the return rate on a beauty catalogue says less than it does in other categories. The cost lands on the customer who bought the wrong shade, could not return it and did not order again.

Virtual try-on is sold as the fix for that customer. It fixes part of the problem, and this article goes through which part.

Why beauty products come back, or stay in the drawer

The shade is wrong. The foundation is too pink, the nude too brown, the lipstick that looked warm on the page is cool on the lips. This is the biggest group and the one people mean when they talk about beauty returns.

The texture or finish is wrong. Right colour, wrong feel. A cream that sits heavy, a matte that goes chalky, a foundation with more coverage than the shopper wanted.

The product was not what the shopper thought it was. Wrong size, wrong applicator, a travel format where they expected the full one. That is a product-page problem.

It did not suit them for a reason a picture cannot carry. Fragrance, sensitivity, how it wore through the day.

Try-on helps with the first group, a little with the fourth, and does nothing for the second and third.

What returns cost, for scale

Return figures for beauty on its own are thin and mostly published by vendors. The industry-wide number comes from the National Retail Federation and Appriss Retail, who put total United States returns at 743 billion dollars in 2023, about 14.5 percent of retail sales.

For a figure you can use internally, measure your own repeat purchase rate on shade-dependent products against the rest of the range. The gap between the two is the size of the problem on your site.

What try-on does well

It shortens a long shade range. A grid of thirty foundations is hard to shop from, and try-on lets a shopper narrow it to two or three before buying anything.

It answers placement and shape questions. How wide a brow product reads, where a liner sits, whether a blush placement suits a particular face. Those are geometry, and face tracking is good at geometry.

It gives the shopper enough confidence to order. Shopify's merchant data from September 2020 showed products with 3D or AR content converting 94 percent more often than comparable products without it. That covers all categories rather than beauty on its own, but the mechanism is the same. A shopper who has seen the product in context commits more readily.

Skin tone, which try-on cannot settle

This is the limit that matters most, and a later software version will not remove it.

A makeup try-on shows a colour on a screen. Between the product and the shopper's eye sit four things nobody controls. The phone camera and its automatic white balance, the light in the room, the shopper's own screen and its colour profile, and the accuracy of the digital swatch. Each adds error, and the errors compound.

For a bright red lipstick that hardly matters. For foundation it matters completely, because the whole decision is whether a shade disappears into one skin tone. Undertone is a comparison between two colours, and if either has drifted the comparison tells you nothing.

So a shopper can use try-on to decide that a foundation range is worth a look and which two shades to consider. They cannot use it to be sure. A vendor who promises shade certainty on a phone camera is promising something the camera cannot deliver.

Shopify's AR shopping write-up gives facial complexion products the highest difficulty score of any beauty category, 41 percent. Self-tanning and body makeup come next at 38 percent each.

Texture, which it cannot settle either

Try-on renders colour and, at best, a rough idea of finish through a highlight. It does not carry weight, slip, how a cream goes on, whether a powder grabs, or what a formula looks like after six hours. Those are the reasons a shopper with the right shade still stops using the product.

What helps here is ordinary content. Swatch photos on a range of real skin tones, wear-test video, coverage and finish stated in words, and reviews filtered by skin type. It costs less than a try-on integration.

Which question each tool answers

The bottom two rows are the ones most beauty sites skip. A beauty catalogue is full of products where the question is physical rather than about colour. Styling tools, devices, brush sets, refill systems, gift boxes. A shopper who can turn one round in the page and see it at real size gets an exact answer, with no colour uncertainty in the way.

What a 3D model adds

Our own work is in furniture and home products, and the try-on we build is for eyewear rather than makeup, so this is the part we can speak to from experience.

When MELIMELI moved its sofas onto a configurator, a new fabric became a texture applied to an existing model instead of a photo shoot. A beauty range with many finishes or packaging variants works the same way. Model the object once and generate the variants from it.

Contura covers the size side. Customers configure a wood-burning stove online and see it in AR in their own room, and by the time they reach a retailer they tend to know which stove they want. A beauty product that shoppers misjudge on size has the same fix.

What we would do on a beauty site

Put try-on on the shade-dependent colour products and say in the copy that lighting affects the result. Put real swatches on a range of skin tones next to it, since that is what settles the shopper who is nearly sure. Add wear-test content for texture. Then build 3D models for every product where the question is physical, because that answer is exact and most beauty sites have not done it yet.

We build the first of those models for free so you can see it on your own page. Send us one product and keep the model. The solutions page lists what else we build and prices are on the site.

Sources

• National Retail Federation and Appriss Retail, 2023 Consumer Returns in the Retail Industry.
• Shopify, AR shopping, including complexion-product difficulty scores.
• Shopify, merchant data on 3D and AR content, September 2020.