How to A/B Test Amazon Listing Images (and Do AI Images Convert?)

The only reliable way to know whether a new image helps is to test it. Amazon's Manage Your Experiments (MYE) lets eligible sellers split traffic between two versions of an image and compare results. This guide explains how it works, what to test first, how to read the outcome and what we can and can't say about AI images and conversion.

What Manage Your Experiments is

MYE is a tool in Seller Central that runs a split test on listing content. It shows each version to a share of shoppers and reports the results. Guides from SellerLabs and SellerSprite describe it, and they differ on some details such as eligibility thresholds and recommended test length. Amazon's own help pages are the source to trust for current rules, so check them before you plan a test.

• Who can use it: generally brand-registered sellers, with the eligible ASINs shown in the tool. Eligibility can change.
• What you can test: commonly the main image, other images, titles, bullets, descriptions and A+ Content, depending on what Amazon offers at the time.
• How it splits: shoppers see version A or version B. You do not choose who sees which.

What to test first

1. The main image. It drives clicks from search results, so it often has the largest effect. Test one change, such as a different angle, a tighter crop or a different pack view.
2. The second and third images. These answer the first questions: what it does, how big it is.
3. A+ Content. Test a redesigned page against your current one. Our Basic vs Premium guide explains the options.

How to run a clean test

1. Change one thing. If you change the main image and the title together, you won't know which worked.
2. Pick a product with steady traffic. Low-traffic listings take too long to give a clear answer. Guides disagree on the minimum, so lean on the tool's own guidance.
3. Let it run long enough. Several weeks is common advice, and the tool can end a test early when the result is clear. Don't stop it on a good day.
4. Avoid big changes meanwhile. A price change, a promotion or a stock-out can distort the result.
5. Write down your hypothesis first. For example: 'A closer crop raises click-through.'

How to read the results

• Look at the conclusion the tool reports, not just the raw difference between versions. Small gaps with little data can be noise.
• Check the right metric. A main image test mostly affects click-through and sales, while content below the fold affects conversion on the page.
• A flat result is a result. It means the change didn't matter, so you can pick the cheaper or the better-looking version.
• Keep the winner, then test the next idea. Gains from a series of small tests add up.

Do AI images convert?

There is no verified dataset that settles this, and anyone who quotes a precise lift for 'AI images' should be asked for the source. What we can say is this:

• Conversion follows clarity, not origin. A clear, accurate, well-designed image does well whether it was photographed, designed or AI-made.
• AI changes the economics of testing. When a new set costs about $10 rather than hundreds of dollars, you can afford to test more versions. See the cost comparison.
• Accuracy is the risk. An AI image with a wrong colour or misspelled word can lose sales. Check every image against your product, see common AI image mistakes.
• Labelling matters. Photorealistic AI-generated people need a label, see the rules.

A simple testing plan for AI images

1. Make a new set from your real product photos. See how to make listing images with AI.
2. Check every image against the real product, then upload the set.
3. If your account is eligible, run the new second image or A+ page against the old one in MYE. If it isn't, compare periods carefully and note what else changed.
4. Keep the version that wins, then test a different idea.

If you want to see what AI-made sets look like before you test, browse the examples. APlus Listing makes a full set and A+ page from an ASIN for 10 credits ($10), and regenerating an image costs 1 credit, which makes it cheap to produce a variant to test. Whether it converts better for your product is something only a test can tell you.