# One-page checkout on PrestaShop: which conversion gains to measure

> Published gains range from 5 to 40%, which is enough to disqualify them all. Why A/B testing is difficult on a funnel, how to run a valid before-and-after measurement, and the five indicators that explain what moves.

- Page: <https://www.datafirefly.com/en/2026/08/26/one-page-checkout-on-prestashop-which-conversion-gains-to-measure/>
- Language: en
- Published: 2026-08-26
- Last updated: 2026-08-26
- Other languages: [fr](https://www.datafirefly.com/2026/08/26/checkout-une-page-prestashop-mesurer-conversion/index.md), [es](https://www.datafirefly.com/es/2026/08/26/checkout-en-una-pagina-en-prestashop-que-ganancias-de-conversion-medir/index.md), [de](https://www.datafirefly.com/de/2026/08/26/one-page-checkout-in-prestashop-welche-konversionsgewinne-messen/index.md), [it](https://www.datafirefly.com/it/2026/08/26/checkout-in-una-pagina-su-prestashop-quali-guadagni-di-conversione-misurare/index.md), [pl](https://www.datafirefly.com/pl/2026/08/26/checkout-na-jednej-stronie-w-prestashop-jakie-wzrosty-konwersji-mierzyc/index.md), [pt](https://www.datafirefly.com/pt/2026/08/26/checkout-uma-pagina-prestashop-medir-conversao/index.md), [nl](https://www.datafirefly.com/nl/2026/08/26/checkout-op-een-pagina-prestashop-conversie-meten/index.md)
- Index: <https://www.datafirefly.com/en/2026/llms.txt>

The figures circulating about one-page checkout range from 5 to 40% conversion gain. That spread is enough to disqualify all of them: those measurements come from stores whose starting point, catalog and average cart have nothing to do with yours. A store whose native funnel is already well maintained will not gain what a store with a five-step checkout will gain.

The only measurement that concerns you is your own. Here is how to establish it without going wrong.

## Why A/B testing is difficult here

On a landing page, A/B testing is simple. On an order funnel, three obstacles stack up.

**Volume.** To detect a 10% gap on a 2% conversion rate, you need several thousand sessions per variant. A store with a hundred orders a month will never reach significance in a reasonable time.

**Session consistency.** A customer who sees version A and comes back three days later must see version A again, otherwise you are measuring noise. That requires persistent tagging.

**Risk.** A bug on variant B for a week costs revenue directly, unlike a test on a secondary page.

For most stores, before-and-after measurement remains the only practicable approach. It is less rigorous, but it becomes acceptable if you control three things.

## Before-and-after measurement, done properly

**Comparable periods.** Four weeks before, four weeks after, avoiding promotional periods, school holidays and public holidays. Comparing November to September makes no sense.

**A stable traffic mix.** If you launch an advertising campaign the week of the change, you are measuring the campaign, not the checkout. Check the breakdown by source before and after, it must be close.

**One change at a time.** This is the most broken rule. Modifying the checkout and adding a payment method in the same week makes any conclusion impossible.

## The five indicators to record

Overall conversion rate is the result, not the diagnosis. Five intermediate measurements explain what is moving.

1. **Checkout completion rate**: finalised orders relative to entries into the funnel. This is the primary indicator, and it isolates the funnel from the rest of the site.
2. **Step-through rate.** On a multi-step funnel, it reveals which step drives people away. On a single-page funnel, it is replaced by the completion rate of each block.
3. **Median completion time.** The median, not the average, which a single customer who went to lunch is enough to distort.
4. **Form error rate.** How many submissions fail, and on which fields. This is often where the real problem hides: an overly strict phone number format costs more than an extra step.
5. **Abandonment rate after shipping costs are shown.** It deserves its own measurement, because it is the most cited cause of abandonment and it has nothing to do with funnel ergonomics.

## Segment by device, always

The mobile conversion rate is structurally lower than the desktop rate, often by a factor of two. A global average therefore masks everything: a 15% gain on mobile and a 3% loss on desktop can produce a flat average.

Since the gains from a simplified checkout are almost always concentrated on mobile, not segmenting amounts to concluding that the change brought nothing.

Segment by customer status too. A registered customer, with saved addresses, does not meet the same friction as a new visitor. The gain is mostly measured on the latter.

## Interpretation traps

**Seasonality.** Conversion rate varies naturally over the year. Also compare with the same period the previous year to distinguish your effect from the underlying trend.

**The novelty effect.** The first days after a change sometimes show flattering figures that normalise afterwards. Wait at least two weeks before concluding.

**Average cart.** A checkout that converts better but lowers the average cart can be a bad deal. Look at revenue per session, not only conversion rate.

## What actually moves

From what is observed in practice, the gain from a simplified checkout rarely breaks down the way people imagine. It comes mainly from three sources: reducing the number of fields, removing the obligation to create an account, and showing shipping costs earlier in the journey.

Moving from several steps to a single page matters less than those three elements taken together. That is one more reason to measure step by step rather than conclude globally: you will then know which of those levers produced the effect, and which one is still available to pull.

The  groups the funnel on PrestaShop 8 and 9: entry on a single page, streamlined address form, ordering without mandatory account creation and shipping costs shown before the last step.