# Marking Already-Viewed Products A/B Test Example

Page: https://alidemirbas.com.tr/en/lab/ab-test-library/tests/marking-already-viewed-products-ab-test-example

## Hypothesis

If product cards a shopper has already opened show a small “Viewed” label, shoppers may keep their place in a long list and move on to products they have not checked yet. The purchase conversion rate could rise as a result, though a label that makes products look ruled out could reduce reconsideration and lower it.

## When to run this test

Visitors who reopen the same product within a short time or scroll up and down without opening anything new in session recordings, or feedback from people who could not find a product again, show shoppers losing their place in long category lists. If the label works, shoppers view more distinct products, time in listing does not grow, and that discovery carries through to purchases. If you keep history across sessions, tell visitors clearly and check the consent you need up front; if you cannot, limit the test to in-session history.

## Primary metric

**Purchase conversion rate**: Compare purchases among all eligible assigned visitors; do not limit the analysis to visitors who clicked a product marked “Viewed.”

## Other metrics

- **Unique products viewed**: Check whether visitors view a wider range of products.
- **Repeat product views**: Track whether visitors reopen the same product.
- **Return visits to a product**: Separate later visits from immediate repeat views; fewer returns are not automatically better.
- **Time in listing**: Make sure browsing time does not increase unreasonably.

## What to check before and during the test

- **Label**: Add a small “Viewed” label to a product card after the product page has been opened.
- **Keep constant**: Keep sorting, card styling and eligible products unchanged.
- **History window**: Predefine and keep the history window and reset rule fixed.
- **Reconsideration**: Check whether visitors revisit and purchase products marked as viewed.
- **Separate tests**: Do not include dimming the card, changing the reset period or removing the product from the list.

## What to watch when reading the result

- Fewer repeat views or more unique products viewed do not make the label a winner; what counts is the purchase conversion rate across all eligible assigned visitors. If return visits to marked products and purchases of them fall, the label may be making those products look ruled out.
- Make sure the label does not fade the card or make the product hard to read; if it does, do not attribute the gap to the label's reminder effect. The result is limited to the list length you tested, so do not generalize from a short list to very long ones.
- If you keep history across sessions, track later visits separately from immediate repeat views; part of the label's effect may only show when shoppers come back. For a small or unclear purchase difference, check whether chance could explain it with the [A/B test significance calculator](https://alidemirbas.com.tr/en/calculators/ab-test), and run the test for its full planned length before you decide.

## Frequently Asked Questions

### When should a viewed label appear, and for how long?

Add it after the product page has been visited. Define the history window and reset rule before launch. Keep them stable during this comparison.

### Could viewed labels make products seem rejected?

Yes. Check whether marked products are revisited and purchased. Do not fade them so heavily that the label effectively removes them from consideration.

### Is less repeated viewing a positive result?

Not by itself. Use purchase conversion as the primary outcome. Distinguish immediate repeat views from later visits rather than assuming fewer returns mean easier decisions.

### Can viewed labels create problems on shared devices?

Yes. They may reveal another person’s interests. Explain cross-session storage, assess applicable consent requirements and preserve clearing controls. Use consistent retention and deletion rules in both arms.