10+ A/B Testing Mistakes in eCommerce + Solutions [2025]

Are you running A/B tests but not seeing the desired results?

Well, you’re probably doing it the wrong way. A/B testing is a great tool to fix your user experience and increase your conversion rate.

But it needs to be conducted with a proper method, strategy, tool, and data. So basically, there are a lot of factors to be considered for the success of your A/B tests.

In this blog post, we’ll explore all the common A/B testing mistakes and their solutions. Avoiding these mistakes can help you boost your conversion rate and profit margin.

Let’s jump into it straightaway!

TL;DR — Common A/B Testing Mistakes + Solutions

10+ Common Mistakes to Avoid in A/B Testing

First off, A/B testing has a huge scope in eCommerce. 

There are several aspects that can be tested, from the homepage to the checkout page, to even landing pages. So, whether it’s about the common mistakes in A/B testing products or landing pages, we’ve got it all covered.

Mistake 1: Conducting an A/B Test Without Defining the Hypothesis

This is one of the most common and frequently observed mistakes.

“Let me try to run an A/B test to change the theme of my product page today. It’ll be a cool experiment.” — No, that’s not the right approach to A/B testing.

An A/B test must start with a hypothesis — or it’s just an assumption or guesswork.

If you’re running an A/B test without a well-defined hypothesis, the results obtained from it could be just random and have no practical implementation. In fact, you might end up causing it damage rather than improving your conversion rate.

Solution — How to Define Hypothesis for A/B Tests:

Craig Sullivan, a Conversion Optimization Expert,  has prepared a Hypothesis Kit that could be used to clearly define your hypothesis. It has three major components: theory, validation, and the expected outcome.

Here’s the example as explained by Craig:

You can customize the elements given in parentheses, based on your business case, and create your own hypothesis from this template.

Mistake 2: Testing Too Many Variables at Once

Testing too many variables makes it quite difficult to evaluate the result. You won’t be able to figure out which variable influenced the positive/negative result.

Let’s take a hypothetical example to understand this better. Imagine Heinz wants to conduct an A/B test on its homepage’s above-the-fold section — and you change the headline copy, hero image, and CTA button at the same time, in a single test. 

After the test, if you find a 10% increase in conversion rate, you won’t be able to decide whether the positive outcome was a result of a change in the headline copy, hero image, or CTA button. You get the idea, right?

Solution — Test ONE Variable at a Time:

Shortlist the key components of your page that you define as crucial elements in your hypothesis. Prioritize the two most crucial elements and test those two elements at a time. In the above example, we could prioritize the headline copy and the hero image to be tested first.

Mistake 3: Ending the A/B Test Too Soon OR Without Achieving Statistical Significance

You don’t want to go with the result achieved by fluke. That’s why you must ensure the result is reliable by allowing your test the required time and achieving statistical significance.

Solution — How to Achieve Statistical Significance:

Experts suggest the acceptable statistical significance level should be 95% or 90% at the minimum. In simple terms, it means if you run the same A/B test 100 times, you’ll get the same result 95 times. Thus, you’re 95% confident about the accuracy of your test, and it’s not just a random result.

You can use this A/B Testing Significance Calculator — created by Neil Patel, a renowned digital marketing expert. 

Pro tip: As a best practice, run your A/B test for at least two weeks. Also, pick those two (or more) weeks carefully. Make sure not to run any promotional campaign during this time that could influence customer behavior.

Mistake 4: Running the A/B Test with Insufficient Traffic

So, we talked about ideal time duration and statistical significance. But another thing brands miss to consider is the volume of traffic. In order to perform a valid A/B test that generates reliable results, you must have enough traffic data.

If the A/B test results are gained through insufficient traffic data, you might end up with no fruitful results for all your efforts.

Solution — Evaluate Your Traffic Data Before the A/B Test:

Review your historical traffic data and see if the traffic trends have been good enough. If you find that you haven’t been able to generate sufficient traffic in the past, you can run paid ads to make sure your A/B test gets enough traffic.

Mistake 5: Not Using a Reliable A/B Testing Tool

A/B testing can be a complex process, but with the right tool, your job becomes much easier. Don’t use any random tool to play with A/B Testing.

Solution — Use GemX — CRO & A/B Testing Tool:

With years of experience and expertise in the Shopify CRO domain, we’ve built this dedicated A/B testing app for you. GemX can help you run reliable A/B tests for your landing pages, product pages, or any other store pages. 

Also, GemX offers you advanced features such as custom traffic routing, funnel analytics, and integration with major page builders. Of course, if you’re already a GemPages user, you can easily connect it with GemX as well.

Running an A/B test with GemX is quite easy. Just go to the GemX dashboard, click “Create new experiment”, select your control and variant — and hit “Start experiment”.

Also, you can edit your test or view analytics whenever you want.

Start your first A/B test with a 14-day free trial now!

Mistake 6: Not Prioritizing the Right Pages and/or Elements

Ask yourself these questions before running the A/B test: 

• What exactly are you testing? 

• Is that a high-impact page for your business? 

• Is the element you’re testing significantly important for the results?

For example, A/B testing your product page is far more critical than A/B testing your contacts page. Similarly, on whichever page you finalized, what element are you going to test? For example, testing your CTA button’s color is a more sensible idea than testing your size chart’s color scheme.

Again, let’s take a hypothetical example for this scenario: Let’s say French Connection needs to run an A/B test on its product page for an issue of a high cart abandonment rate. The highlighted elements are crucial factors that can impact the cart abandonment rate.

 

Pro tip: When there are multiple elements that could possibly be tested, you can use data from a heatmap tool or customer feedback. It will help you identify the most significant elements and run your tests more effectively.

Solution — Define High-Impact Pages & Elements:

Defining high-impact pages depends on your business goals. For example, if you’re looking to increase your recurring revenue, the subscription offer page would be a high-impact page for your business.

When talking about elements, focus on the conversion-focused elements. For example, if you’re running an A/B test for your product page, your conversion-focused elements would be product title, CTA, social proof, trust and security badges, etc. 

Learn more: Top 20+ A/B Testing Ideas You Should Try

Mistake 7: Not Considering the External Factors

You need to make sure the time you choose to run the test is appropriate to find results without the influence of external factors.

The external factors include the holiday season, promotional sale on a competitor’s store (or your own), or other market conditions that might heavily impact customer behavior.

Solution — Choose the Right Time and Perform Tests in the Same Timeframe:

Run your tests during regular days that don’t cover a festive or other promotions. Also, if you’re running multiple A/B tests for the same objective, make sure all tests are conducted in the same timeframe to avoid the influence of external factors. 

Mistake 8: Not Segmenting the Test Audience

Traffic may include visitors who can be segmented into different groups, such as first-time visitors and returning visitors. Also, they can be segmented based on the traffic sources.

If you don’t segment your visitors, you won’t be able to see the real or complete picture.

Solution — Define and Set Up Clear Segmentation:

Segment your audience into appropriate groups as per your business case. Evaluate the results for all the segments to find any unusual behavior or patterns.

Mistake 9: Disregarding the Insights with No Documentation

Regardless of the outcome, every test generates some insights for your business. Whether the test was successful or a failure, it tells you something about the customer behavior or other patterns related to device type, offer, and so on.

If you’re not documenting all such insights, you’re missing out on key findings that could be helpful to your marketing team in the future.

Solution — Create a Central Database for All A/B Tests, Outcomes, and Insights:

Document the outcomes and observations from all A/B tests at a centralized location with proper structure. If it’s supposed to be a detailed database, make sure to prepare it in a way that provides a quick overview.

For example, if you’re using Notion for documentation purposes, you can create a full-page database with properties like A/B Test Name, Page Type, Timeframe, Experiment Status, and Summary of Results.

 

Mistake 10: Not Tracking Key Metrics

If you’re not measuring the key metrics in the first place, you’re not going to conduct proper A/B tests and accurately analyze the results.

Solution — Prepare a Checklist of Key A/B Testing Metrics:

Here’s the checklist of the key metrics that you should track and analyze before, during, and even after conducting A/B tests:

Learn more: Key Metrics for A/B Testing Success: Strategies You Need to Know

Mistake 11: Improper Evaluation and Implementation

Once the test is completed, you need to make a certain decision based on the evaluation. But if your evaluation is wrong in the first place, it won’t lead to fruitful implementation. 

Solution — How to Evaluate Your A/B Test Results and Proceed with Implementation:

Check all the basic aspects we already discussed: Statistical significance, traffic data, and timeframe — and then evaluate your A/B test results.

Evaluate if you need to reiterate the test. If one test has failed, it may not always mean there’s no other opportunity to improve the performance.

Learn more: How to Run a Proper Shopify A/B Testing on Your Store 

Final Thoughts on Nailing Your A/B Test

A/B testing is all about experiments — but you must remember that those experiments have to be data-driven. You must start with data analysis and even end the tests with more data in the form of insights.

Last but not least — install the GemX: CRO & A/B Testing app with a FREE trial — and start conducting A/B tests that actually help you with conversion rate optimization.

To learn more about other eCommerce marketing strategies, tools, and best practices — check out more resources on the GemPages Blog. Also, join the GemPages Facebook community to network and learn from like-minded entrepreneurs and experts.

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