# Testing a Studio Shot Against a Customer Photo

Page: https://alidemirbas.com.tr/en/lab/ab-test-library/tests/testing-a-studio-shot-against-a-customer-photo

## Hypothesis

If shoppers want to see the product in use, an authorized customer photo may make the purchase decision easier; if they want a clear look at the details, a studio shot may. Which main image brings the higher purchase conversion rate may therefore vary with the product category and what shoppers look for.

## When to run this test

Questions asking whether the product looks like this in real life, returns citing a mismatch with expectations, or reviews that complain about the images show shoppers are unsure what they will actually get. The expected behavior is that the image that fits the shopper's need lifts add-to-cart and purchases together. If the complaints come from wrong color or size information, fix the product details first. Before launch, get usage permission from the photo's owner and make sure both images have adequate resolution and framing; otherwise you are testing quality, not photo type.

## Primary metric

**Purchase conversion rate (CR)**: Compare purchases per assigned visitor across the two photo types.

## Other metrics

- **Add-to-cart rate**: Check whether shoppers take the first purchase step.
- **Return rate**: Track returns over the same follow-up period; do not attribute every return to the image.
- **Page load time**: Check the loading cost of each hero image.

## What to check before and during the test

- **Hero image**: Compare one studio photo with one genuine customer photo of the same product.
- **Gallery**: Keep the remaining images, image count and layout unchanged.
- **Category**: Review results within relevant product categories.
- **Mixed gallery**: Testing a gallery that mixes studio and customer photos is a separate comparison.
- **Price band**: Review predefined price bands without applying one product's result to all products.

## What to watch when reading the result

- Purchase conversion rate picks the winner; if add-to-cart rises while purchases stay flat, the leading image has not won. If page load time differs between the arms, the gap may come from speed rather than photo type. Read return rate only after both arms have had an equal follow-up window, and do not pin individual returns on the image.
- This comparison speaks for the product and category you tested; repeat it by category and check the predefined price bands before rolling it out across the catalog. If the product description changed too, the photo type's effect cannot be pulled apart from the description's. Keep comparing as an A/B test rather than dropping the studio image outright.
- A single product may get limited traffic, so a small conversion difference may not become clear quickly. See how many days this product's traffic needs to fill the sample per variant from the [sample size calculator](https://alidemirbas.com.tr/en/calculators/sample-size-calculator), and add the return follow-up window on top. Do not pick a winner before that time is up.

## Frequently Asked Questions

### What should be checked before using customer photos?

Verify usage permission, adequate resolution and comparable framing. Inspect loading cost. A poor-quality image introduces an implementation difference beyond photo source.

### Can changed returns be attributed to the image?

Randomization can estimate the experience’s effect, not each individual return reason. Use equal follow-up and interpret conditional return rates with conversion and product mix.

### Can one product’s photo result be applied to the whole catalog?

Validate relevant categories and predefined price bands. Do not replace the whole catalog’s imagery on the strength of one product comparison.