# Should the Cart Quantity Selector Stand Out?

Page: https://alidemirbas.com.tr/en/lab/ab-test-library/tests/should-the-cart-quantity-selector-stand-out

## Hypothesis

If the quantity selector is easier to notice in the cart, shoppers may be more inclined to change quantities right there. Items per order and average order value (AOV) could both go up as a result.

## When to run this test

Session recordings of visitors hunting for the small quantity control, or heatmaps where the plus and minus buttons get almost no clicks, suggest shoppers miss the selector in the cart. Once it is easier to spot, quantity changes and items per order should rise while order completion holds steady. If quantity updates are slow or reload the full page, fix that first; otherwise you cannot separate the effect of visibility from the effect of the lag.

## Primary metric

**Average order value (AOV)**: Measure revenue per completed order and read this buyer-only average alongside revenue per visitor.

## Other metrics

- **Items per order**: Compare the average number of items in completed orders.
- **Order completion rate**: Track order completion among assigned visitors as cart value changes.
- **Revenue per visitor (RPV)**: Compare revenue per assigned visitor, including visitors who do not purchase.

## What to check before and during the test

- **Change**: Choose one visual treatment to make the quantity selector more noticeable; keep its position and behavior fixed.
- **Keep constant**: Keep the selector position, remove-item control, price display and other cart actions unchanged.
- **Behavior**: Read quantity changes alongside average order value.
- **Interpretation**: Track removals and revenue per visitor; investigate accidental removals only with a predefined interaction signal.
- **Separate tests**: Test other positions or styles separately.

## What to watch when reading the result

- AOV only averages the shoppers who buy. If items per order and AOV go up while order completion or revenue per visitor slips, the gain may be paid for with lost buyers, so do not ship the change as it stands.
- Look at changes in the item removal rate too, but do not infer accidental removals from that rate alone; check them against the interaction signal you defined in advance. Keep the remove and favorite icons in place and visible in this test, or you will not know which change caused the difference.
- This result covers the visual treatment you chose and this cart layout, and each device should be read on its own. On mobile, check that the plus and minus buttons do not overlap and that the larger controls do not overshadow the main CTA.
- With a small AOV gap, keep in mind that a few large orders can move the average on their own. Run the order completion numbers for both arms through the [A/B test significance calculator](https://alidemirbas.com.tr/en/calculators/ab-test); if the gap is not significant, that still does not prove order completion held steady. Average order value is outside that calculator's scope, so treat its difference with the same care.

## Frequently Asked Questions

### Why is average order value insufficient on its own?

AOV includes purchasers only. Use it as the primary outcome, with order completion and revenue per visitor as guardrails. More units per completed order can conceal a decline in the number of buyers.

### Could a more prominent quantity control cause accidental removal?

It could. Monitor removals, but use defined interaction evidence or usability observations to identify accidental ones. Preserve the delete and favorite controls rather than changing their position or appearance simultaneously.

### What should be checked on mobile?

Make plus and minus controls easy to use, distinguish them from deletion and display the updated total clearly. Keep quantity-update behavior consistent between arms. Diagnose refresh or latency problems separately.