Shopify price testing: how to A/B test your prices

Shopify price testing means showing two prices for the same product to two randomly split groups of visitors at the same time, charging each group the price it saw, and keeping the price that earns more gross profit per visitor. The charged price is the hard part. A test that only changes the number on the product page breaks at checkout, so you need a tool that changes the cart price too. Test price moves of 10% or more, run each test for at least two full weeks, and judge it on profit per visitor rather than conversion rate.

How a Shopify price test changes what the buyer pays

Shopify checkout charges the price stored on the variant, or the price after a discount or cart function runs. Your theme can show any number it likes on the product page, but the checkout will not follow it. That leaves three ways to run a real price test:

Comparing this month's sales at $66 with last month's at $60 is not a price test. A promo email, a competitor's sale or a payday weekend can move sales more than the price did. Both prices have to run in the same weeks to the same kind of traffic.

Pick the products and price points to test

Price tests need orders, and orders are spread across your catalogue. Start with the products that carry the most traffic.

1. Export product page sessions and orders for the last 90 days. Shopify Analytics gives you sessions by landing page and orders by product.
2. Pick a hero product or a group of similar products. A store with 40,000 monthly product page visitors often gets half of them on its top 10 products. Testing a 10% increase across those 10 at once pools their traffic into one test.
3. Leave out anything in an active promotion, on a subscription price, or sold wholesale at a fixed list price.
4. Choose one change per test. Each of these is a hypothesis, and each needs its own test:

Your buyers are not the buyers in anyone's case study. A $6 increase that one store's visitors ignore might cost another store a third of its orders. Test one change, read the result, and let it decide the next test. If $66 wins, the next test might try $72. If it loses badly, price sensitivity is real for that product and the next test moves to the bundle discount instead.

For free shipping thresholds specifically, the Shopify checkout optimization guide covers where that variant runs and how it shows up in your checkout funnel.

Judge the winner on profit per visitor

A higher price usually lowers conversion rate. That is expected, and it does not mean the test lost. The question is whether the margin on each order makes up for the orders you lose.

Here is an example with made-up numbers for a $60 product that costs $24 to make and ship:

Conversion rate fell 8%. Revenue per visitor barely moved. Gross profit per visitor rose about 7%, and that is the number that pays your bills. A dashboard showing only conversion rate would have told you to kill the winner.

Use gross profit per visitor as the primary metric for any price or discount test, with revenue per visitor and conversion rate as supporting numbers. Measure on real Shopify orders, with refunds taken out, rather than on a pixel firing on the thank you page. A price that wins on day-one orders and then gets returned more often is not a winner.

How much traffic a Shopify price test needs

Price tests are expensive in traffic because the effect you are looking for is often a small change in conversion rate. Take the example store above: 40,000 monthly product page visitors across the products in the test, converting at 2.5%. At 95% significance and 80% power:

At this traffic, a 10% price change across a group of products is a realistic test. A $1 change on one SKU is not, because the difference it makes is too small to measure in any reasonable time. Run your own numbers in the sample size calculator before you launch, and see the A/B testing ROI calculator to turn a lift into monthly revenue.

Run the test without confusing shoppers or breaking your ads

Price tests carry risks that a headline test does not. Plan for each before launch:

• Keep each visitor on one price. A shopper who sees $60 on Monday and $66 on Thursday will email you. Assignment has to persist across sessions and devices where possible.
• Check your Google Shopping feed. Google Merchant Center flags products when the landing page price does not match the feed price. Either exclude Shopping traffic from the test or keep the feed price equal to what that traffic sees.
• Be careful with "sale" variants in the EU. Under the EU Omnibus Directive, a price reduction you advertise has to show the lowest price from the previous 30 days. A compare-at price in a test variant counts as an advertised reduction.
• Brief your support team. Give them one line to use if a customer spots both prices, and a rule for refunding the difference.
• Hold your other promotions. A sitewide 20% off code during the test swamps a 10% price difference.

Then run it like any reliable A/B test:

1. Set the sample size before launch and do not look at the result until you reach it.
2. Run for at least two full weeks, so weekday, weekend and payday buyers land in both groups.
3. Call a winner at 95% significance and no lower.
4. If the variant loses or comes out flat, remove it from the store and write down what it taught you about that product's buyers.
5. Ship the winning price to everyone, update the feed and any ads that quote the price, and watch return rates for the following month.

Who should run your price tests

Intelligems has done more than any other app to make price testing normal on Shopify, and Shogun, ABConvert and Convert all offer price tests too. We're glad these tools exist. The catch is that a tool runs the test and leaves the rest to you: picking the products, setting the price points, working out the sample size, briefing support, checking the feed and reading profit per visitor at the end. For a founder, that is a lot of hours for one test. The Shopify A/B testing tools guide compares what each app can test.

Retainer agencies take that work off your plate, and they are paid the same each month whether your margin moves or not. We compared both routes in DIY vs agency A/B testing.

Cascayd is outcome-based. You add a pixel, give GA4 access and install a Shopify app with read access to orders. We research your catalogue and funnel, design and build each test with AI assistance and a human checking every step, and send it to you in Slack with the reasoning. You approve it or ask for changes. One test runs at a time, and the next hypothesis comes from what the last one taught us. Cascayd is paid only from a share of the measured uplift. See how the Shopify CRO service works.

Your next step

Pull last quarter's product page sessions and orders, and find the five products that together take the most traffic. Work out their combined gross margin per order. If a 10% price increase on those five would clear the traffic bar in the table above, that is your first price test.

If you want it scoped, built and judged on real orders without adding hours to your week, book a call and tell us about your store.