# Testing a Recently Viewed Row on an App Home Screen

Page: https://alidemirbas.com.tr/en/lab/ab-test-library/tests/testing-a-recently-viewed-row-on-an-app-home-screen

## Hypothesis

If returning visitors can reopen the products they last viewed with one tap from the homepage, they no longer need to go back through search or categories to find them. Fewer detours could then lift the purchase conversion rate (CR) among returning visitors with eligible browsing history.

## When to run this test

Search logs where returning visitors type the same product queries again, or session recordings of them taking long routes back to a product page they already viewed, make a recently viewed strip worth testing on the homepage. If the strip works, these visitors reach product pages by a shorter path and total purchases rise. If visitors find the product and still do not buy, the problem is not navigation, so look at the product page before adding a strip.

## Primary metric

**Purchase conversion rate (CR)**: Compare purchases per assigned returning visitor with eligible browsing history.

## Other metrics

- **Strip click-through rate**: Use this only as a diagnostic in the variant, where the strip exists.
- **Product detail visits**: Measure total product-page visits, not only clicks from the strip.
- **Bestseller clicks**: Review clicks on bestsellers alongside total purchase conversion.
- **Homepage exit rate**: Make sure exits do not increase.

## What to check before and during the test

- **Strip**: Add a strip of products the visitor viewed most recently.
- **Layout**: Keep placement, product count and dimensions fixed.
- **Eligibility**: Exclude purchased and unavailable products using the same predefined rules.
- **Incrementality**: Check whether strip interactions lead to additional purchases or redirect existing browsing.
- **Platform**: Review the same comparison within each supported platform.

## What to watch when reading the result

- Strip click-through rate exists only in the variant, so treat it as a diagnostic rather than grounds for a launch. If bestseller clicks fall while total product detail visits stay flat, clicks have simply shifted; purchase conversion is the measure that counts. Also confirm the homepage exit rate does not rise, and keep the strip small enough that it does not fill the whole first screen, or the rest of the page gets harder to reach.
- The finding covers this strip layout, the current recommendation algorithm and returning visitors with eligible history; if you change the algorithm in the same test, you cannot tell which change caused the difference. Read each platform on its own, and leave products from private browsing sessions out of the strip.
- Returning visitors with eligible history may be a small pool, so an unclear result often just means too little data. Give the [sample size calculator](https://alidemirbas.com.tr/en/calculators/sample-size-calculator) today's conversion rate and the lift worth detecting, then see how many days of eligible visitors per arm it takes to reach the sample per variant.

## Frequently Asked Questions

### Does a high strip click-through rate prove value?

No. Clicks may move from other homepage blocks. Use purchases per eligible returning visitor as the primary outcome and inspect total navigation, not strip clicks alone.

### Should previously purchased products appear in the strip?

It depends on repurchase needs. Define purchase and stock rules beforehand, preserve history-clearing preferences and do not assume private browsing can be reliably detected.

### How can homepage clutter be assessed?

Monitor exits and total product-detail reach. Do not let the strip fill the first screen. More strip clicks without more overall discovery can indicate redistribution rather than incremental value.