Every returned parcel costs an Indian seller the forward shipping, the reverse pickup, the handling and often the product itself. Many returns are logged with a reason like "item not as expected", and that phrase is where product images come in.
This post sets out what reliable data exists on Indian return rates, which parts of the problem images can address, which they cannot, and what to look at in your own listings. It does not claim that CGI cuts returns by a fixed percentage, because I could not find a verifiable study that says so.
What the return-rate numbers say, and how old they are
Good public data on returns in India is scarce. These are the figures I could trace to a named source:
RedSeer, 2017 data, reported by The Indian Express on 10 April 2018. More than 30% of the $18.6 billion of e-commerce GMV was lost to cancelled or returned orders. By category: fashion 37 to 42%, large appliances 29 to 34%, FMCG 28 to 33% and mobiles 22 to 27%. These combine cancellations and returns, so they overstate returns alone.
NIFT, reported by Just-Style on 1 March 2018. The return rate in India is in the range of 20% to 40%, with poor garment fit given as the reason.
Return Prime, reported by Apparel Resources on 31 July 2024. 17.6% of online products returned, and 30 to 35% in some clothing and footwear categories, with size issues behind almost 40% of returns in those categories. Return Prime sells returns software, and the article's state-level numbers contain obvious errors, so use this as a rough indicator only.
The honest summary: apparel and footwear are the worst categories, rates run from the high teens to the low forties depending on source and definition, and the most solid public numbers are several years old. If you need your own benchmark, your order data is better than any of these.
Why products come back
The reasons split into groups that images affect differently.
RedSeer said in 2018 that returns were mainly due to shipment errors, meaning the wrong item was delivered or the product was very different from the one ordered, while cancellations were mostly a change of mind. An Amazon Fashion business head, quoted by FashionUnited in 2019, said most fashion returns happen because customers want an alternative size.
A LocalCircles survey reported in January 2025, with over 102,000 responses from shoppers in more than 327 districts, found that 48% of online shoppers had been stuck with a wrong product because of platforms' return and refund policies, and 20% had received a fake or counterfeit product at least once in the past year. That survey is about wrong and fake goods and policy gaps, not about photographs, but it is a reminder that not every return is an expectations problem.
What "item not as expected" actually covers
When a buyer says a product was not as expected, the gap usually falls into one of these:
Gap | Example | Can images help? |
|---|
Size and fit | Too small, or the shoulder runs narrow | Partly: size charts, model measurements, flat-lay dimensions |
Scale | The bottle is smaller than the buyer pictured | Yes: a size reference in the frame |
Colour and finish | Looks cream on screen, arrives white | Yes, if the image is colour-accurate |
Material and feel | Looks like leather, feels like plastic | Partly: close-ups help, touch cannot be shown |
Function | Cannot tell what it does or what is included | Yes: in-use scenes and a contents image |
Wrong, damaged or fake item | Different product delivered | No: this is a supply and logistics issue |
Images are a tool for the first five rows only, and only when they show the real product accurately.
Image types that reduce mismatch
These are common-sense fixes, not proven ones. I found no study that measures their effect for Indian sellers.
A size reference. Show the product next to a hand, a common object or a ruler, or give a dimension overlay. This is the cheapest fix for scale confusion.
Material and texture close-ups. A tight shot of the weave, grain, stitching or surface finish tells the buyer more than a wide shot can.
In-use scenes. The product on a shelf, in a hand or on a body shows how it actually sits and what size it is in context.
Colour-true main images. Heavy retouching and boosted saturation make colour returns more likely. If the product comes in three shades, show all three accurately.
A what's-in-the-box image. Prevents the "I thought it came with..." return, which is common with bundles and accessories.
Back, side and inside views. Shows zips, pockets, ports, compartments and anything else the buyer would check in a shop.
Our guide to the types of e-commerce product images covers how these fit into a full image set, and product listing images and conversion rate looks at the sales side.
Where CGI helps and where it can make returns worse
CGI is useful here because size references, close-ups and in-use scenes can be rendered from one 3D model without a new shoot. A model also lets you show every colour variant accurately, which reduces colour mismatch if the render is matched to the real product.
The risk is the opposite. A render that is smoother, brighter or more perfect than the product sets an expectation the parcel cannot meet. Fabric drape, food, and products whose real texture is the selling point are often better photographed. For anything rendered, ask the studio to match colour and material against a physical sample, and approve scale against real dimensions. Our e-commerce listing images and product rendering pages describe what is involved.
Does the law say anything about accurate images?
A summary of the Consumer Protection (E-Commerce) Rules, 2020 on IndiaLaw states that marketplace platforms must require sellers to supply accurate descriptions and images, and that sellers must accept returns and provide refunds for defective or misrepresented goods. I read that summary rather than the official gazette text, so check the current rules with the Department of Consumer Affairs or your marketplace's seller policy. The practical point stands: a listing that does not match the product is a policy and legal risk, not only a cost.
How to find out whether images are behind your returns
Start with your own data. Pull return reasons for your top 20 SKUs and group them: size, colour, material, function, damage, other. If one SKU has a cluster of colour returns, look at its images next to the real product under daylight.
Then change one thing on a small set of products, such as adding a size reference and a close-up, and compare return reasons before and after over a few weeks. Keep price, season and ad spend as steady as you can. This will not give a universal number, but it will tell you what works on your catalogue, which is the number you can act on.
How Smapit can help
Smapit is a Gurugram-based studio that builds 3D models, product renders, animations and e-commerce listing content for brands in India, the US and the UAE. If you have a few SKUs with high "not as expected" returns, send us the listings and a product sample or reference photos, and we can show how a size-reference or close-up image would look. You can request a free sample through the contact page.
Frequently asked questions
What is the return rate in Indian e-commerce?
There is no single reliable current figure. RedSeer's 2017 data, reported in 2018, put combined cancellations and returns at over 30% of GMV, and NIFT said in 2018 that returns in the country ran at 20% to 40%. Treat both as old ranges, not today's average.
Do better product images reduce returns?
They can remove one cause, which is a buyer expecting something different from what arrives. I did not find a verified study that puts a number on the reduction, so any specific percentage you hear should be treated as unproven.
Which returns can images not fix?
Wrong or counterfeit items, transit damage, late delivery and plain change of mind. RedSeer's 2018 note named change of mind as the main reason for cancellations, and a LocalCircles survey found many buyers were stuck with the wrong product.
Can a CGI image increase returns?
Yes, if the render looks cleaner, brighter or more uniform than the real product. Check colour, texture and scale against a physical sample before approving any render.
What should I track to find out if my images are causing returns?
Tag every return with a reason code, such as size, colour, material or function, and review the codes per SKU. A cluster of one reason on one product usually points to a specific image gap.
Sources: The Indian Express via RedSeer, "E-commerce firms losing over 30 per cent of GMV due to cancellations/returns," 10 April 2018; Just-Style, "India to use 3D body scanners in national sizing survey," 1 March 2018; Apparel Resources, report citing Return Prime, 31 July 2024; FashionUnited India, "Indian online apparel retailers face size issues, return clause," 2019; Moneylife, LocalCircles survey on wrong and fake products, 16 January 2025; IndiaLaw, "A summary of the Consumer Protection (E-Commerce) Rules, 2020."
