How to Master Shopify A/B Testing for Your Small Business

Are you running a web shop on Shopify and looking to boost your sales and engagement? You’ve come to the right place. This guide dives into Shopify A/B testing, specifically tailored to you – whether you’re a small business owner or an established e-commerce store.

What is Shopify AB Testing and Why Should You Care?

Simply put, A/B testing, or split testing, is like a scientific experiment for your Shopify store. Imagine you’re a chef experimenting with recipes to find out what your customers love the most. A/B testing does just that but for your website. You test two versions of your web page (A and B) to see which one gets more thumbs-ups from your customers – be it more sales, sign-ups, or whatever goal you’re aiming for.

Getting Started with Shopify AB Testing

1. Define Your Goals: What do you want to achieve with your A/B test? More newsletter sign-ups? Increased sales of a particular product? Clear goals lead to clear results.

2. Pick One Thing to Test at a Time: Let’s keep it simple. Change one element – like the colour of your ‘Buy Now’ button or the headline of your product page – and see how it performs against the original.

3. Choose Your Battle Warriors: Use tools like Optimizely, VWO, or AB Tasty, which are great for Shopify stores. They’ll help you set up your tests without needing a degree in rocket science.

Defining A/B Testing Metrics

When setting objectives, you should identify the primary metric that you want to improve. This metric should be the most important metric that you want to improve while supporting metrics provide additional context and help validate the hypothesis.

Check out our guide on the most important metrics for growth.

Crafting Your Hypothesis

Think of a hypothesis as your best-educated guess. For example, “Changing my ‘Add to Cart’ button to a bright orange will increase clicks by 20%.” It’s specific, measurable, and sets a clear expectation.

Formulating a good hypothesis is critical to A/B experimentation. A good hypothesis should be based on evidence, identifying the change, the users it will impact, the expected impact, and how it will benefit the business. A good hypothesis helps protect you from your own biases and makes data-driven decision-making more accessible.

An example of a hypothesis for an A/B testing experiment is: “Adding a live chat feature to the website will result in a higher conversion rate compared to not having a live chat feature.”

An example of a null hypothesis could be: “Adding a live chat feature to the website will not result in a higher conversion rate compared to not having a live chat feature.”

The null hypothesis is simply a statement that there is no difference between the groups in the experiment. If the p-value is low, it means that the result is unlikely to have occurred by chance alone, and thus the null hypothesis is rejected.

The p-value is a measure of the probability of obtaining a result at least as extreme as the observed result if the null hypothesis is true. It is commonly used to assess the statistical significance of an experiment.

Running Your Shopify AB Test

1. Set It Up: Use your chosen A/B testing tool to create the two versions of your webpage.
2. Let It Run: Give your test enough time – usually a few weeks – to gather meaningful data. Don’t rush; patience is key.
3. Monitor Regularly: Keep an eye on your site’s health – like page loading times and overall functionality. You don’t want your test to affect the user experience negatively.

Implementing Your Successful Changes

1. Analyze and Learn: Understand why the winning version worked better. Was it more visually appealing? Easier to navigate?

2. Roll Out Gradually: Implement the successful changes across your site. It’s a good idea to do this gradually and monitor for any unforeseen issues.

3. Keep Testing: Remember, A/B testing is an ongoing process. There’s always room for improvement.

Decoding the Results

Once your test is done, it’s time to dive into the data. Your A/B testing tool will show you which version performed better based on your goal. If version B wins, consider making that change permanent. If there’s no clear winner, that’s okay too – every test is a learning opportunity.

Health Metrics

Health metrics are used to monitor the overall health of a product during experiments. They are the product’s general performance indicators, such as page load times, app crashes, and errors. They should be monitored in all experiments to detect unexpected changes.

It’s essential to monitor health metrics during the experimentation process to ensure that the changes you make to your product do not impact its overall health.

Shopify AB Testing Examples

Here are two A/B testing examples you could do:

1. Changing the font and colour of buttons: A hypothesis behind an experiment like this would be that changing the font and colour of buttons would improve navigation and lead to more purchases on the website. A primary metric would be more purchases, and supporting metrics could include a lower bounce rate and increased engagement. A health metric could be page load time.
2. Introducing a payment receipt feature: A hypothesis could be that introducing a payment receipt feature would reduce the number of customer service tickets received. A primary metric could be customer service tickets, and supporting metrics could include a lower bounce rate and increased engagement. A health metric could be app crashes.

It’s important to isolate a single change and test it against a control group so you are able to measure the impact of each change accurately.

Shopify AB Testing Tools

Not all A/B testing tools are created equal, especially for Shopify. Some popular options include:

Kameleoon: Kameleoon is a versatile A/B testing tool that integrates with Shopify. It utilizes advanced machine learning to analyse user behaviour and highlights areas for improvement. Key features include personalisation, split testing, heat maps, comprehensive analytics, user targeting, multivariate testing, and easy integration with Shopify, enabling even those without technical skills to start A/B testing​​.

Lomio: Lomio is an effective, low-cost A/B testing solution for e-commerce brands using Shopify. It allows for product, content, and theme A/B testing, enhancing customer shopping experience and potentially increasing sales and customer loyalty. Lomio’s plans are tailored to different sizes of stores, making it accessible for businesses with varying order volumes​​.

Product + Upsell A/B Testing: This app is focused on optimising product details and conversions through A/B testing. It enables Shopify merchants to test variables like product prices, images, titles, and descriptions without the need for technical expertise or subscription fees. Additionally, it offers the ability to A/B test post-purchase upsells to boost average order value and revenue, providing a comprehensive tool for enhancing both product appeal and upselling opportunities​

Common Pitfalls to Avoid

To avoid these mistakes, it is important to plan the experiment carefully, ensure that there is sufficient traffic, allow enough time for the experiment to run, track engagement metrics, and analyze the results.

• Jumping to Conclusions Too Soon: Let your test run its course. Early results can be misleading.
• Testing Too Many Things at Once: This can muddy the results. Stick to one change per test.
• Ignoring the Data: Trust the numbers, not just your gut.

Shopify is an accessible and powerful tool to drive sales for your business. Shopify A/B testing is a great way for you to understand user behaviour and continually optimise your site for better conversion.

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