# Size Chart or Personalized Size Advice A/B Test

Page: https://alidemirbas.com.tr/en/lab/ab-test-library/tests/size-chart-or-personalized-size-advice-ab-test

## Hypothesis

If recommendation-first help takes over the work of reading the measurement chart, more visitors may pick a size, though entering height and weight could make some of them drop off. Chart-first help adds no such step. Whichever effect wins out could raise size selection completion for that format.

## When to run this test

Visitors who open the size chart and leave without choosing, sizing questions in support tickets, or fit problems that keep showing up in return reasons point to a hard size decision. If recommendation-first help leads, shoppers who fill in the form reach a size faster and form abandonment does not cancel out that gain; if chart-first help leads, the extra step costs more than it saves. If the chart itself has missing or wrong measurements, fix it first, and do not start the test until the recommendation method is validated for these categories and has enough data behind it.

## Primary metric

**Size selection completion**: Compare size selections per eligible assigned visitor, including visitors who do not finish the recommendation form.

## Other metrics

- **Add-to-cart rate**: Check whether the help format changes the first purchase step.
- **Size-related return rate**: Track fit-related returns for orders that complete the same follow-up period.
- **Recommendation acceptance rate**: Report selection of the recommended size as a diagnostic measure in the recommendation arm.
- **Input form abandonment**: Track visitors who leave the height and weight form unfinished.

## What to check before and during the test

- **Help format**: Compare a size-chart-first experience with a recommendation-first experience that still provides access to the chart.
- **Evidence**: Keep supporting claims and social proof unchanged.
- **Inputs**: Keep input fields fixed and optional.
- **Model**: Validate and hold the recommendation method constant for this comparison.
- **Category**: Include only categories supported by the recommendation method.

## What to watch when reading the result

- Compare the two on size selection completion per eligible assigned visitor, counting visitors who do not finish the recommendation form. Recommendation acceptance exists only in one arm, so treat it as a diagnostic. If selections rise while add-to-cart rate stays flat or the size-related return rate goes up, do not call the recommendation arm the winner.
- Leave size ranges alone in this test; if the format and the ranges change together, you cannot tell which one caused the difference. The findings cover only categories the recommendation method supports, and returns should be compared for purchases past the follow-up period.
- Do not present a recommendation built on thin data as if it were certain, since a wrong size skews the comparison as well. When the gap is unclear, use input form abandonment to see where the form loses visitors, and check whether the completion difference could be chance with the [A/B test significance calculator](https://alidemirbas.com.tr/en/calculators/ab-test).

## Frequently Asked Questions

### Is the size chart removed in the recommendation arm?

No. Keep it available as a fallback. This comparison changes which help is presented first rather than removing access to product measurements.

### How are users who abandon the recommendation form counted?

Include them among all assigned eligible visitors. Use size-selection completion as the primary outcome. Form abandonment and recommendation acceptance are diagnostics.

### Should the test launch before the recommendation method is ready?

No. Validate and fix the method first. Do not present weak-data recommendations as highly certain, and restrict eligibility to supported categories.

### Can height and weight be required?

Not in this optional-input comparison. Keep the defined fields optional and stable. Do not change size ranges while introducing the recommendation method.