# Simplifying the Menu Item Set: Navigation A/B Test

Page: https://alidemirbas.com.tr/en/lab/ab-test-library/tests/simplifying-the-menu-item-set-navigation-ab-test

## Hypothesis

If a predefined set of submenu items is removed, visitors may scan the menu faster when it first opens and move to the right category. Purchase conversion could rise as a result, but if the removed items lead to categories people actually want, losses on those paths could cancel the gain.

## When to run this test

Simplification is worth trying when menu click data shows some submenu items are almost never chosen, or session recordings show visitors scanning a long list by eye. With the leaner menu, visitors should reach their target category in fewer steps and less time while category coverage rate holds steady. If rarely chosen items are skipped because their names are unclear, look at the naming first. Write down up front which items you will remove and which other paths lead to them; leave primary categories in place and do not ship anything that slows the page alongside the menu change.

## Primary metric

**Purchase conversion rate (CR)**: Compare purchases among all eligible assigned visitors over the same follow-up period; category discovery helps explain the result.

## Other metrics

- **Time to reach a category**: Use the same start and category-arrival events in both arms.
- **Navigation steps**: Count clicks or selections needed to reach predefined target categories.
- **Category coverage rate**: Track visitors who reach a product page within predefined target categories.
- **Search usage**: Track searches for items removed from the menu; more search is not automatically a failure.

## What to check before and during the test

- **Removal**: Remove only predefined subcategory links.
- **Access**: Keep other navigation paths to target pages available.
- **Steps**: Use the same event definitions to compare navigation paths.
- **Arrival**: Measure time from the same start event to the target category; report visitors who never arrive.
- **Discovery**: Review access to affected categories and overall product discovery.

## What to watch when reading the result

- Let purchase conversion among all assigned visitors pick the winner. Do not treat a rise in search usage as failure on its own, but if searches for removed items climb while category coverage rate falls, the simplification may have narrowed discovery too far.
- Read the effect in the categories tied to the removed items, not only in the overall average; overall conversion can hold steady while those categories lose out. Report visitors who never reach the target separately from time to reach a category, or the time will look better than it is.
- Keep icons and visual style the same throughout, and check that the sticky mobile menu does not cover critical areas; either problem makes it hard to tie the gap to the number of items. If the affected categories get little traffic, the difference may stay unclear for a while. Get the visitors needed per arm from the [sample size calculator](https://alidemirbas.com.tr/en/calculators/sample-size-calculator) using those categories' conversion rate and the minimum effect you want to catch, then divide by their daily visitors per arm to see how many days you need.

## Frequently Asked Questions

### Which menu items can be removed?

Remove only predefined submenu items, preserving main categories and other routes to affected pages. Excessive removal can limit discovery.

### Does increased search use mean simplification failed?

Not necessarily. Track searches for removed items and access to affected categories alongside category coverage. Use purchase conversion as the primary outcome.

### How can the navigation cost of simplification be measured?

Count steps to predefined categories and measure arrival time using common events. Report visitors who never arrive. A simpler menu may be easier to scan while making some destinations harder to reach.