A/B Testing on Shopify: Examples, Tools & How to Get Started

A/B testing is one of the most effective tools for optimizing your Shopify conversions and sales without breaking the bank.

With a moderate budget, it offers actionable insights, helpful data, and the opportunity to test them in real time.

On the other hand, A/B testing requires decent amounts of traffic; so you may want to wait until your store has at least 10,000+ monthly visitors before getting started.

There are other critical things to know, so let's dive into how to successfully run A/B tests on your Shopify store and grow your sales.

Go to sections:

• What is A/B testing

• When A/B testing makes sense for a Shopify store

• Traditional Shopify CRO vs instant CRO

• Common A/B testing mistakes

• Examples of A/B testing from Shopify stores

• How to start A/B testing on Shopify

• A/B test ideas for your store

What is A/B testing?

A/B testing is an experimental method for improving Shopify conversions and sales by testing two versions of a store element (product page layout, discount, or popup) to see which one performs better. Traffic is split between them (but can be adjusted manually), and results show which drives more engagement.

The goal of A/B testing is to find the most effective and impactful changes to a Shopify store, based on actual visitor engagement and customer behavior. This allows to make informed decisions rather than relying on assumptions.

A/B testing tools for Shopify often include—or integrate with—features like heatmaps, clickmaps, and conversion funnels to give a fuller picture of customer behavior. Heatmaps and clickmaps, for example, show where users click or lose interest and leave.

When does A/B testing make sense for Shopify?

Before launching A/B tests on your Shopify store, set yourself up for success by understanding the essentials—this will help you make smarter, data-driven decisions from the start.

Make sure your store gets at least 30,000 monthly visitors

To determine how much traffic your A/B tests need, you’ll have to factor in your baseline conversion rate, the improvement you hope to achieve, and two statistical inputs: a 95% confidence level and 80% power (meaning a 20% risk of missing a true winner).

For example, if your store converts at 3% and you want to detect a 5% conversion uplift (which is 3.15%), you'll need around 38,000 conversions (not views) per each campaign variation to achieve statistical significance:

Use heatmaps and visitor session recordings to guide your test ideas

These CRO tools (like Hotjar, Smartlook, or Clarity) show how different visitors interact with your site—where they click, scroll, or hesitate—helping you spot friction points in their journey and prioritize what to test.

Start with a measurable goal tied to a key store metric

Choose one priority like improving product page conversions or increasing average order value. Then design a test around that goal—for example, offer different product bundles or try a new upsell popup campaign.

Focus on testing one variable at a time

Changing multiple things at once makes it hard to know what drove the result. Start small (like, testing two different discount sizes or product descriptions) so you can confidently link changes to results.

Choose the right timing for your tests

Avoid testing during high-traffic events like Black Friday sales or big email campaigns as they can skew behavior. Run tests during stable periods and let them run long enough to reach valid results.

Set a clear success metric and let the test run its course

Choose one clear primary metric tied to a major goal, such as purchase rate, revenue per visitor, or email signup rate.

Aim for at least 1,000 conversions per variation—200 is the minimum and only works for detecting large uplifts with lower precision. Example: if your Shopify store has a 3% conversion rate and you want to detect a 10% uplift, you’ll need around 8,500 visitors per variation.

How instant CRO overcomes the limitations of traditional A/B testing on Shopify

Traditional Shopify CRO tools usually involve long A/B tests that many folks find frustrating — especially since these tests often don’t reach reliable results. Plus, if a site doesn’t have millions of visitors, it’s tough to run meaningful A/B tests at all.

A faster way to see results is by using simple, ready-to-go “plays” that tackle common CRO issues quickly.

For example, if the homepage has a high bounce rate, instead of waiting a month for an A/B test to finish, a new welcome offer can be tested right away with a targeted campaign and its impact measured fast.

We call this approach "Instant Shopify CRO," or "next-gen CRO."

Here are some proven "instant Shopify CRO plays" that deliver fast impact:

• Welcome offer: Welcome new visitors with an instant 10% off and showcase best-selling products (+12-18% list growth)

• Exit product picks: Regain interest by showing “you may like” items right before visitors leave (+5-8% sessions saved)

• Cart upsell: Offer the perfect add-on immediately after “Add to cart” (+10-15% average order value)

• Exit signup saver: Capture abandoning visitors’ emails with a last-chance incentive (+5-8% cart recovery rate)

• Spin to win: Gamify email capture with a prize wheel to boost engagement (25–40% signup rate)

• Trending products: Show “hot right now” products in a live block to increase clicks (+15-20% click-through rate)

Common A/B testing mistakes to avoid

Even with the right A/B tools and traffic, it’s easy to fall into traps that lead to inconclusive or misleading results.

These common Shopify A/B testing mistakes include:

• Testing without enough traffic or conversions. If your Shopify store doesn’t have enough visitors, results won’t be reliable. Consider using only your high-traffic pages or delay A/B testing until you reach 10,000+ monthly visitors

• Changing multiple variables at once. Multivariate tests require far more data—if you’re not there yet, stick to single-variable tests to keep learnings clear

• Ending tests too early. Even if one campaign variant seems to be winning, don’t stop until your A/B tool reaches statistical significance (95% confidence)

• Ignoring the control group. A/B testing without a baseline comparison can give a false sense of progress—always measure against “no campaign” when possible.

Example of A/B Testing on Shopify

OddBalls: A/B testing of gamified lead capture campaigns

OddBalls ran a “Spin-to-win” email capture campaign during the 2024 shopping season with two key goals: grow their email list and drive sales.

The campaign used A/B testing to fine-tune performance across different customer segments: new and returning visitors.

Campaign setup

They built two versions of the campaign:

Version 1

The first campaign was made for returning prospects (visitors who hadn’t subscribed yet were offered a chance to get up to 20% off in exchange for their email):

Version 2:

This campaign below was made for existing subscribers.

Visitors had the chance to win a bigger discount (up to 20%) and could spin the wheel first to see their prize before sharing their email.

It was a smart move since it flipped the usual process—giving the discount first and then asking for something in return—which made things feel a bit easier:

Both featured colorful designs, 5–10% discount incentives, and integrations with Klaviyo for email capture and coupon distribution. They also used custom targeting rules, such as only showing the campaign on mobile or after a user scrolled.

A/B test variations

To improve performance, they ran multiple A/B tests, including:

• Triggering the popup on landing vs. on exit for returning visitors

• Testing whether the post-close tab should remain visible for subscribers

• Comparing layouts with simpler vs. more detailed designs

Results

• 2,100+ new subscribers

• 25.3% click-through rate

• 558 conversions

• £31,443 in revenue

Key takeaways

• Segmenting campaigns by visitor type (new visitor vs. subscriber) allowed for more personalized experiences and better results

• Mobile optimization was critical for engagement, with over 7,200 displays coming from mobile visitors

• Earlier engagement performed better: triggering the popup on page load beat exit-intent triggers for returning prospects

• Simpler design choices (removing post-close tabs) led to small but measurable conversion improvements (62 vs 53 orders)

• Small discounts (5% and 10%) were enough to drive meaningful engagement without cutting too far into profit margins

• Continuous testing revealed unexpected wins that wouldn’t have been caught without A/B experimentation

This test shows how gamified experiences combined with A/B testing and smart segmentation can transform a simple email capture into a high-converting project.

Learn more: OddBalls The Spin to Win Challenge

How to start A/B testing on Shopify

A good starting point for Shopify A/B testing is email lead capture campaigns.

They have high visibility and immediate performance metrics, making them perfect for testing small variations with real impact on revenue.

Below, I'll walk you through an example of how to prepare and run your first Shopify A/B test including an optional control group for more advanced analysis.

A/B test idea: Boost lead capture and first-order revenue with a welcome campaign that includes AI-powered product recommendations

About 62% of Shopify stores use a welcome campaign to offer a discount in exchange for an email address. Yet, most of them deliver the discount via email, creating a less-than-ideal customer experience by requiring users to check their inboxes to retrieve it.

So, do customers go and open the welcome email?

Do they copy the code and make a purchase—or never act?

One way to improve post-signup performance is to enrich the second step of the campaign. Instead of simply showing the discount, we will add product suggestions and a link to keep browsing.

Success metrics:

• Email signup rate (for both variants)

• Click-through rate from the second step (applying discounts or clicking recommendations)

• Conversion rate (first order within session or X days)

• Average order value (optional)

• Revenue per visitor

Here’s an A/B test to find out what performs best—I'll include an example of a slightly modified campaign from a Shopify store (Nutrimuscle) to illustrate.

Variant A

Standard email capture campaign

• Step 1: Email capture form

• Step 2: Static thank-you screen with discount code that can be applied to the cart

• Goal: Measures the impact of a simple lead capture flow

Variant B

An email capture campaign with AI product recommendations (bestsellers)

• Step 1: Email capture form

• Step 2: Discount code + a few AI product suggestions and a link to more products

• Goal: Test if adding product suggestions and a link to browse more items increases the number of pages viewed per visitor and boosts sales from first-time buyers

Note: We've changed only one design element in the the second variant (the product recommendations section) to stay in line with the best practice of testing only one variable at a time.

Control group

No campaign is shown

• Goal: Measures baseline performance without any campaign interference

• Helps assess whether the welcome offer helps overall shopping experience and first-time sales

• The new visitors will see the Nutrimuscle's homepage (below) without any other popups or forms

How to run this A/B test on a Shopify website

I'll implement this test with the Wisepops Experiments platform, a powerful A/B/n testing feature designed to provide detailed visitor engagement insights and revenue data for Shopify stores with no dev help.

Learn more about the Experiments Platform.

If you'd like to follow along, access a free 14 day trial here:

no cc needed, use for free for 14 days

Follow the steps to run this Shopify A/B test:

1. Design the first variant

2. Create an A/B test

3. Add a control group (optional)

4. Set a test goal

5. Customize the second variant

6. Launch the test

Step 1: Design the first variant

Use the drag-and-drop popup builder to design a multi-step discount campaign (Popups > New popup campaign).

I'll add an image and a short welcome message describing the offer.

So, step one collects the email...

...while step two displays the discount code.

You can also choose between allowing visitors to copy the discount or apply it to the shopping cart in one click.

If you'd like to use unique discount codes from your Shopify account, connect your store to access them directly in the Wisepops campaign editor.

Learn more: adding unique discounts.

Step 2: Create an A/B test

Choose to "A/B test" the campaign back in the overview dashboard:

The A/B testing menu will appear.

Since we're changing only one step in the same campaign, choose Add variant > Copy of this campaign.

The first campaign will be duplicated and ready for you to customize:

By default, visitors will be evenly split between the two variants, with 50% assigned to Variant A and 50% to Variant B.

This balanced split gives you the best chance of reaching statistical significance as quickly as possible, since both variants get enough data to detect meaningful performance differences.

Step 3: Add a control group (optional)

Enable the control group by toggling it on.

This will exclude a portion of visitors—33% by default—from viewing any variants, allowing you to measure baseline performance:

Note: Adding a control group in this Shopify A/B test gives you a true baseline—which helps you isolate the impact of your lead capture campaign, and not just which version performs better.

Step 4: Set a test goal

Choose a primary goal metric (such as clicks or conversions) to define the criteria for ending your experiment.

I'll go with Click-through rate (CTR)—other data like revenue per visitor will be gathered and displayed in the Wisepops analytics a bit later.

Click Done once ready.

Note: The test concludes once one of the variants achieves statistical significance—a 95% confidence level—compared to the other or the control group.

Step 5: Customize the second variant

Once you choose Done, you will land in the campaign dashboard.

There, choose the second campaign variant—we still need to change it by adding product recommendations.

In the campaign editor, go to the second step.

Then, choose a category of product recommendations in Blocks:

There are six categories available, but I will choose the bestsellers because first-time customers may be more likely to be interested in them:

Add that category to the campaign (and I'll put it below the discount code section) and start customizing the design.

On the left, you'll have two tabs: Products and Add to cart:

In Products, you can customize recommended items and specify the total number to display.

To add products, simply enter their IDs separated by commas, like this:

id:4567770, id:4572093

Here’s how you can exclude products (no apostrophes):

Product ID: e.g., "id:435342655" (include "id:")

Exact title: e.g., "White summer shirt"

Partial title: e.g., "sweater" (matches "Yellow summer sweater" and "Red winter sweater")

More on adding products.

Step 6: Launch the test

Save the second campaign variant and change the project's status from Draft to Published in your dashboard.

The A/B test will start tracking data in your Shopify store once the first visitor interacts with one of the variants.

Learn more about A/B testing on Shopify with Wisepops: CRO experiments.

A/B test ideas for Shopify stores

Below is a curated list of Shopify A/B test ideas structured around all stages of the ecommerce funnel: top, middle, and bottom.

Each idea includes a short description and a potential impact range based on real campaigns and use cases.

Top-of-funnel: engage & capture new visitors

Mid-funnel ideas: product discovery & consideration

Bottom-of-funnel: conversion & recovery

Summary

The difference between a good Shopify store and a great one often comes down to how well they do A/B testing. With every experiment, from your welcome offer to your post-purchase flow, you gather data that tells you what actually grows your business.

If you have the traffic and tools for A/B testing on Shopify, treat it like a powerful growth engine. Consider starting with a few high-impact ideas from the list above, measure results, and keep iterating.

Related resources:

• 10 Shopify success stories

• Apps to create exit campaigns on Shopify

• How to market a Shopify store: 14 strategies