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Do product images increase sales? What the research shows

Yes, when they help a shopper judge the real product. Professionally made pictures lifted Airbnb occupancy, room settings lifted home décor sales on some sites but not on Amazon, and the wrong kind of extra picture went with more returns. Here is what the research found, and what to change first.

Updated · 8 min read · 12 studies cited

On this page
  1. Why images matter
  2. Image quality and sales
  3. Lifestyle vs white background
  4. Images and returns
  5. Renders and AI images
  6. Where to start
  7. FAQ
  8. Sources

Why do product images matter so much online?

The short answer

Because online, the picture stands in for the product. A shopper can't lift the chair, rub the fabric or see the sofa against their own wall, so they judge quality, size and fit from your images. Good images answer those questions. Weak ones leave a doubt, and the shopper settles it by leaving.

That makes product imagery less a matter of taste than of information. The questions below are ones sellers ask each other on the Amazon and Shopify seller forums, and type into Google.

Each answer comes from published research: field data from real listings where it exists, controlled experiments where it doesn't, and the limits the researchers themselves state. Every study is listed in full at the end, so you can check our reading against theirs.

Do better product images actually increase sales?

The short answer

Yes. The strongest evidence comes from Airbnb, where properties whose pictures were redone by professionals had 8.98% higher occupancy than comparable properties that kept the host's own.1

The study, published in Management Science in 2022, followed 7,423 Airbnb properties for 16 months. Shunyuan Zhang and colleagues compared properties before and after their pictures were redone by Airbnb's photographers with properties that kept the host's own, a design meant to separate the effect of the pictures from the effect of the home.

The researchers then asked what made the better pictures better. They scored 12 attributes covering composition, color and the figure-ground relationship (how clearly the subject stands out from its background), and found that most were related to demand in the direction their theory predicts. Those are qualities of the picture, not of the camera: composition, color and contrast can be designed into a rendered image as deliberately as into a photograph.

Airbnb rents stays, not products, so don't expect 8.98% to carry over to a sofa listing. The decision is the one your buyers make, though: judging a space, and the things in it, from pictures alone.

What “better” means for a product image

  • The product is the subject. It stands out clearly from its background, and the props don't compete with it.
  • Color and texture are true. Even light and no heavy filter or grade, so the finish looks the way it does in person.
  • The image is large enough to zoom. Shoppers who zoom are checking quality, and the returns research below shows why that matters.
  • Every angle answers a question. Front, back, a close-up of the material, and something that shows scale.

Do lifestyle images sell better than white backgrounds?

The short answer

Often, but not everywhere. Home décor shown in a room sold more on Wayfair and on an independent store, but not on Amazon.2 In experiments, a room setting raised purchase intent only when it suited the product.3

Rowena Summerlin, Wendy Powell and Eriko Fukuda tracked 36,152 shopping sessions and 1,410 sales of home décor across Amazon, Wayfair and an independent online store. On the two sites that encourage contextual images as the lead image in search results, those images lifted sales by 94% on Wayfair and 435% on the independent store. On Amazon, which doesn't encourage them in its main search results, there was no such relationship.

It is one study of one category, with 1,410 sales in all, so read the percentages as a direction rather than a forecast. The direction is clear: a room scene works where shoppers expect to see one.

Why does a room help at all? Experiments by Erik Maier and Florian Dost point to imagination. Compared with plain white, a fitting background helped shoppers picture the product in use, which raised how much they liked it and their intent to buy. A background that didn't fit the product brought no such benefit. The effect held for experience products, the kind people judge by look and feel, and not for search products judged on their specifications.

What this means for your listings

  • Lead with a clean product-only image where search results favor one. On Amazon the study found no lift from room scenes, so keep them among the additional images.
  • Lead with a room scene where it's shown first: your own store, home marketplaces that feature them, and Google Shopping, which has a separate lifestyle image attribute whose own example is furniture staged in a room.
  • Pick a room that suits the product. A bouclé armchair belongs in a living room. A setting that jars does no better than white.
  • Give every finish its own scene. A buyer choosing the sofa in sage wants to see the sofa in sage. Our guide to showing every finish online compares five ways to do it.

Can better product images reduce returns?

The short answer

Some can, and some can increase them. At a women's clothing retailer, shoppers who used zoom more returned less, while heavier use of extra alternative pictures went with more returns and lower net sales.4 Images reduce returns when they show what the product is really like.

Prabuddha De, Yu Hu and Mohammad Rahman measured how much each shopper used each feature on each product they bought, then linked that to what came back. Color swatches showed no effect on returns either way. The study is from 2013 and covers one apparel retailer; we cite it because it measures what shoppers actually did, purchase by purchase.

The paper doesn't settle why the two features pull in opposite directions. Our reading, and the one we'd act on: zoom lets a shopper inspect the real thing before buying, while extra flattering views can raise expectations the delivery then fails to meet.

Images that set the right expectations

  • Make detail zoomable. Upload images large enough to show the weave, grain or finish up close.
  • Show scale. Use a person, a familiar object or a room with real proportions. The Baymard Institute's research on in-scale images shows how often shoppers look for this.
  • Keep color honest. A grade that turns oatmeal into cream is a return waiting to happen.
  • Cut views that only flatter. Every image should answer a question a buyer has.

If you're preparing swatches for rendering, our guide to preparing swatches and product images covers how to keep color and pattern scale true.

Do renders and AI images work as well as a shoot?

The short answer

For attention, they can be. In a field test of more than 173,000 ad impressions, AI-generated banner ads competed with professional stock photography, and the best reached up to 50% higher click-through.5 Whether they sell depends on one thing: the product in the image has to be the product that ships.

A disclosure first: Skip Shoot makes AI-generated renders of real products in rooms, so we have a stake in this answer. Here is what the evidence shows, and what it doesn't.

Jochen Hartmann, Yannick Exner and Samuel Domdey also gathered 254,400 human ratings and found that AI-generated marketing images can surpass human-made ones in quality, realism and aesthetics. Those were ads and general marketing images, though, not pictures of one specific product that a buyer will hold up against the real thing.

What we could not find is a peer-reviewed study comparing rendered and photographed images of the same products on sales or returns. If you see a precise figure for that, ask where it comes from.

The returns research gives the test to apply instead.4 An image that drifts from the real product, in fabric, pattern scale, color or shape, sets an expectation the delivery can't meet. So whether an image is photographed, modeled in 3D or generated, judge it on accuracy first.

Check each marketplace's rules as well. Google Merchant Center, for one, requires AI-generated product images to carry metadata that says so. Skip Shoot's downloads don't add it, so add it before you upload. Our comparison with photoshoots, 3D studios and AI image tools puts them side by side.

What should you change first?

Start where the evidence is strongest and the cost is lowest.

  1. Audit your best sellers. Note which have no zoomable detail, no sense of scale or no room scene.
  2. Fix accuracy before beauty: true color, true scale and enough resolution to zoom.
  3. Add a room scene that suits each product wherever the platform shows lifestyle images first.
  4. Keep every category page consistent, with the same background and angle for each product. The FAQ below explains why.
  5. Then work out the cost per finish: products, times finishes, times images. Our guide to what photography costs per finish walks through it.

The research doesn't say pretty pictures sell. It says pictures that let a buyer judge the real product sell, and keep selling after the box is opened.

Frequently asked questions

How many product images should a listing have?

Enough to answer every question a shopper would settle by picking the product up: front, back and sides, a close-up of the material, a view that shows scale, and the product in use. A study of an online marketplace where millions of products change hands found that both the number and the quality of a listing's images can be used to raise sales. Images mattered especially for products whose appearance is important.

Source: Di et al. 2014, Proceedings of the 7th ACM International Conference on Web Search and Data Mining

Does image quality affect conversion rate?

Yes, though it isn't the only factor. In a study of about 75,000 product images from second-hand marketplaces, higher image quality went with a higher likelihood that an item sold, although view count predicted sales better. The same study found that high-quality pictures taken by sellers earned more trust than stock imagery.

Source: Ma et al. 2019, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

Should all product images have the same background?

On a category or search page, yes. Four studies found that mixing white and contextual backgrounds in a product overview lowered shoppers' evaluation of the assortment and their satisfaction with their choice by 6% to 19%, more than mixing angles did (3% to 6%). Use room scenes for every product in a grid or for none, and save the mix for the product page. Nielsen Norman Group's guidance on product images in listings reaches the same conclusion from usability testing.

Source: Maier 2019, International Journal of Electronic Commerce

Do product images with hands in them work better?

Only if the hand is touching the product. Across eight studies using images, GIFs and virtual reality, seeing a hand in contact with a product raised shoppers' sense of owning it and how much they valued it, and the authors conclude that a hand nearby is not enough. For a throw or a cushion, a hand pressing into the fabric does that work.

Source: Luangrath et al. 2022, Journal of Marketing Research

Can pictures make up for not being able to touch a product?

Partly. A study that combined nearly 13,000 purchases on Taobao with two experiments found that pictures showing the product being touched, by a hand or by another object, made up for some of the missing sense of touch and raised purchase intention, because they helped shoppers imagine how it feels. Which kind of touch worked best depended on the product.

Source: Leng et al. 2022, Frontiers in Psychology

Do customers trust AI-generated product images?

It depends on the product. In a 2026 experiment set on a Taobao-style product page, labeling images as AI-generated lowered how authentic and appealing shoppers found practical, utilitarian products, while the drop in appeal was not detectable for products bought for pleasure. Labels and platform rules are changing fast, so the safest AI image is one that shows the product exactly as it ships.

Source: Yang et al. 2026, Frontiers in Computer Science

Why do customers return products bought online?

Often because the product didn't suit them, more than because it was poor. A study of online buyers found that uncertainty about whether a product would match their needs had a significantly stronger effect on returns than uncertainty about its quality. Images eased doubts about quality, while doubts about fit eased when buyers could read product discussions, so pair your pictures with dimensions, views that show scale and answers to common questions.

Source: Hong and Pavlou 2014, Information Systems Research

Put one of your own products in a room that suits it, true to its finish.

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Sources

Cited in Chicago style. Each number matches a citation in the text, and each study links to the record where we found it.

  1. Zhang, Shunyuan, Dokyun Lee, Param Vir Singh, and Kannan Srinivasan. “What Makes a Good Image? Airbnb Demand Analytics Leveraging Interpretable Image Features.” Management Science 68, no. 8 (2022): 5644–5666. https://doi.org/10.1287/mnsc.2021.4175.

    Found on Semantic Scholar

  2. Summerlin, Rowena, Wendy Powell, and Eriko Fukuda. “To What Extent Do Contextual Image Backgrounds Affect Purchase Intention for Online Home Décor Products?” In Proceedings of the 2024 10th International Conference on e-Society, e-Learning and e-Technologies (ICSLT), 99–105. ACM, 2024. https://doi.org/10.1145/3678610.3678611.

    Found on Semantic Scholar · Free to read

  3. Maier, Erik, and Florian Dost. “Fluent Contextual Image Backgrounds Enhance Mental Imagery and Evaluations of Experience Products.” Journal of Retailing and Consumer Services 45 (2018): 207–220. https://doi.org/10.1016/j.jretconser.2018.09.006.

    Found on CORE · Free to read

  4. De, Prabuddha, Yu (Jeffrey) Hu, and Mohammad S. Rahman. “Product-Oriented Web Technologies and Product Returns: An Exploratory Study.” Information Systems Research 24, no. 4 (2013): 998–1010. https://doi.org/10.1287/isre.2013.0487.

    Found on Semantic Scholar

  5. Hartmann, Jochen, Yannick Exner, and Samuel Domdey. “The Power of Generative Marketing: Can Generative AI Create Superhuman Visual Marketing Content?” International Journal of Research in Marketing 42, no. 1 (2025): 13–31. https://doi.org/10.1016/j.ijresmar.2024.09.002.

    Found on Semantic Scholar · Free to read

  6. Di, Wei, Neel Sundaresan, Robinson Piramuthu, and Anurag Bhardwaj. “Is a Picture Really Worth a Thousand Words? On the Role of Images in E-Commerce.” In Proceedings of the 7th ACM International Conference on Web Search and Data Mining, 633–642. ACM, 2014. https://doi.org/10.1145/2556195.2556226.

    Found on Semantic Scholar

  7. Ma, Xiao, Lina Mezghani, Kimberly Wilber, et al. “Understanding Image Quality and Trust in Peer-to-Peer Marketplaces.” In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 511–520. IEEE, 2019. https://doi.org/10.1109/WACV.2019.00060.

    Found on arXiv · Free to read

  8. Maier, Erik. “The Negative Effect of Product Image Inconsistency on Product Overviews During the Online Product Search.” International Journal of Electronic Commerce 23, no. 1 (2019): 110–143. https://doi.org/10.1080/10864415.2018.1512281.

    Found on Semantic Scholar

  9. Luangrath, Andrea Webb, Joann Peck, William Hedgcock, and Yixiang Xu. “Observing Product Touch: The Vicarious Haptic Effect in Digital Marketing and Virtual Reality.” Journal of Marketing Research 59, no. 2 (2022): 306–326. https://doi.org/10.1177/00222437211059540.

    Found on Semantic Scholar

  10. Leng, Xionghui, Xiaoyu Zhou, Shuting Wang, and Yibin Xiang. “Can Visual Language Convey Tactile Experience? A Study of the Tactile Compensation Effect of Visual Language for Online Products.” Frontiers in Psychology 13 (2022): 1034872. https://doi.org/10.3389/fpsyg.2022.1034872.

    Found on PubMed Central · Free to read

  11. Yang, Xiran, Jiaxi Liu, Changlin He, and Junjie Chu. “Beyond the Label Itself: How Disclosed Content Shapes Consumer Responses to AI-Generated Product Imagery in E-Commerce.” Frontiers in Computer Science 8 (2026): 1860932. https://doi.org/10.3389/fcomp.2026.1860932.

    Found on DOAJ · Free to read

  12. Hong, Yili (Kevin), and Paul A. Pavlou. “Product Fit Uncertainty in Online Markets: Nature, Effects, and Antecedents.” Information Systems Research 25, no. 2 (2014): 328–344. https://doi.org/10.1287/isre.2014.0520.

    Found on Semantic Scholar · Free to read