If you’re using Shopify for your ecommerce business, then you're already on the right track. But to truly optimize your store, you should leverage one of the most powerful strategies available to make data driven decisions for growth: A/B testing.
In this guide, we’ll explain what A/B testing is, why it’s crucial for your Shopify store, how to set it up on your Shopify landing pages, and practical advice on how to implement it effectively.
What Is A/B Testing?
A/B testing, also known as split testing or A B testing or AB testing, compares two versions of a webpage, app, or other digital asset to see which one performs better. There are two versions in every A/B test or split test—only one independent variable varies between the two versions—and each version is shown to two different groups of people.
From this test, you can then measure which version achieves the desired goal more effectively, such as achieving higher conversion rates or engagement.
A/B testing takes the guesswork out of ecommerce store and marketing funnel optimization by using data to make decisions. For example, you can test two different custom product page layouts on your Shopify store to see which generates more sales, or compare two marketing email subject lines to find out which gets a higher open rate.
Once you have collected significant data to determine which version is better, you can then implement these changes to any part of your conversion funnel: your advertisements, marketing emails, landing pages, or even your cart pages.
With Replo, you can build, test, and iterate on the landing pages for your ecommerce site in a matter of minutes. Any insights that you collect from third party or on-site A/B tests can be quickly and easily implemented using our no-code drag and drop landing page editor.
For a deeper dive into everything you should know about A/B testing to increase conversions, check out our complete guide.
Who Should Use A/B Testing For Their Shopify Store?
A/B testing can be a powerful tool for optimizing and improving the performance of Shopify stores, but it's not necessarily right for everyone.
Here are some guidelines on who should consider using A/B testing:
Ecommerce Stores with Significant Traffic: To get statistically significant results, you need a decent sample size. Experts recommend having at least 1,000 monthly unique visitors to a page before A/B testing it. Stores with lower traffic may need to run tests for a long time to reach conclusive results.
Established Stores Looking to Optimize: New stores still figuring out their core products, target audience, and value proposition may not be ready for A/B testing yet. It's better suited for mature stores that have the basics in place and are looking to improve metrics like conversion rates.
Stores with Clear, Measurable Goals: A/B testing works best when you have specific metrics you want to improve, such as click-through rates, email signups, or sales of a certain product. Stores with vague goals like "improve the site" will struggle to implement effective tests.
Stores Willing to Invest Time and Resources: Proper A/B testing requires careful planning, implementation, and analysis. Stores need to be prepared to devote adequate time and potentially money (for testing tools) to the process. Shortcuts often lead to misleading results.
Examples of Stores That Could Benefit:
- An established fashion retailer wanting to reduce cart abandonment
- A beauty brand looking to increase email newsletter signups
- An electronics store trying to improve conversion rates on key product pages
- A subscription box service aiming to optimize their checkout flow
These are established businesses with clear, measurable goals for improvement. A/B testing provides a data-driven way to achieve those goals. According to VWO, average revenue per unique visitor for ecommerce sites is $3, and a successful A/B test usually gives this value a 50% lift.
It is no wonder that the ecommerce industry uses A/B testing most actively, with 39% of the customers at VWO, a third party A/B testing service, coming from the ecommerce industry.
What Elements of a Shopify Page should I A/B test?
When running A/B tests on your Shopify store, it's essential to focus on elements that can significantly impact user experience and conversion rates.
Here are some key page elements to consider testing:
CTA Buttons: Experiment with different text, colors, sizes, and placement of your call-to-action buttons to find the most effective combination for driving clicks and conversions.
Landing Page Layouts: Test various layouts, content arrangements, and visual hierarchies on your landing pages to determine which design resonates best with your target audience and encourages them to take action.
Headers and Subheaders: Try out different header and subheader copy, styles, and sizes to capture visitors' attention and clearly communicate your value proposition
Images and Videos: Evaluate the impact of different product images, lifestyle photos, and videos on user engagement and conversion rates. Test variations in quality, style, and placement.
Product Descriptions: Experiment with the length, format, and content of your product descriptions to find the optimal balance between providing necessary information and keeping visitors engaged.
Checkout Process: Test different checkout flows, form fields, and payment options to streamline the process and reduce cart abandonment rates.
Pricing Presentation: Try out various pricing displays, such as emphasizing discounts, showing price comparisons, or bundling products, to find the most appealing presentation for your audience.
Social Proof Elements: Experiment with the placement and design of customer reviews, ratings, and testimonials to build trust and credibility with potential buyers.
Promotional Banners: Test different banner designs, copy, and offers to grab visitors' attention and drive them to take advantage of your promotions.
Navigation and Menu Items: Evaluate the effectiveness of your site navigation by testing different menu structures, item labels, and categorization to help users find what they need quickly and easily. The menu spans different pages on your site, including non-product related pages such as blog posts, so it’s important to pay attention to them too.
Remember, the elements you choose to test will depend on your specific goals and the unique characteristics of your Shopify store.
Focus on testing elements that have the potential to make a significant impact on your key performance indicators, such as conversion rates, average order value, and customer engagement.
How to Set Up and Run A/B Tests in Your Shopify Store
Here's a step-by-step guide to running A/B tests on your Shopify store to improve your conversion rates and revenue:
Define Your Goal and Hypothesis: Clearly outline what you want to achieve with your A/B test, such as increasing add-to-cart rates or reducing cart abandonment. Form a hypothesis about what changes might lead to the desired outcome.
Choose an A/B Testing Tool: Shopify has a built-in A/B testing tool called Shopify Experiments for Shopify Plus customers. Other popular third-party tools include Google Optimize, Optimizely, VWO, and Convert. These tools integrate with your Shopify store and provide an interface for creating and managing tests.
Set Up Your Test: In your chosen A/B testing tool, create a new test and specify the page or element you want to test, such as a product page or checkout button. Create your control (original) and variant versions by making the desired changes, such as altering copy, images, or layout.
Define Your Target Audience and Sample Size: Determine who you want to include in your test, such as all visitors or a specific segment. Use an A/B test calculator to determine the minimum sample size needed for statistically significant results, based on your current conversion rate and the minimum detectable effect you want to measure.
Launch and Monitor Your Test: Start your test and let it run until you've reached the predetermined sample size or time period. Monitor the results in your A/B testing tool's dashboard, which will track key metrics like conversion rates, revenue, and statistical significance.
Analyze Results and Implement Changes: Once your test concludes, analyze the results to determine the winning variation. If the results are statistically significant, implement the winning changes permanently on your Shopify store. If the test was inconclusive, consider running a follow-up test with a larger sample size or different variations.
Document and Iterate: Record your test results, insights, and any implemented changes. Use this knowledge to inform future A/B tests and continuously optimize your Shopify store's performance.
Key Things to Keep in Mind:
- Test one element at a time for clear results.
- Avoid running multiple tests simultaneously on the same page.
- Give tests sufficient time to reach statistical significance.
- Prioritize testing elements that have the greatest impact on your key metrics.
- Regularly test and iterate to continuously improve your store's user experience and conversion rates.
How to Create Compelling Hypotheses for A/B Tests
Creating strong, testable hypotheses is a critical step in running effective A/B tests that drive meaningful improvements to your website or app. Here’s how you can get started:
Identify a Problem or Opportunity: Start by analyzing your website data, potential customer feedback, and competitor research to pinpoint specific areas where you're underperforming or see potential for improvement. Maybe your checkout flow has high abandonment rates, or a key landing page has low conversion rates.
Propose a Solution: Based on your analysis, suggest a change that you believe will improve the metric you've identified. This proposed solution becomes your hypothesis.
For example: "Adding trust badges to the checkout page will reduce abandonment rates by reassuring users about site security."
Tie Hypotheses to Business Goals: Your hypotheses should always link back to overarching business objectives, such as increasing revenue, acquiring more leads, or improving customer retention. This ensures you're prioritizing tests that can have a real impact.
Be Specific: Vague hypotheses like "changing button color will increase clicks" are hard to meaningfully test or learn from. Instead, get specific: "Changing the CTA button from gray to bright blue will increase clicks by 10%." Specificity allows you to set clearer success criteria.
Provide a Rationale: Always include a "because" statement in your hypothesis that explains why you expect your proposed change to have the impact you're predicting. This forces you to justify your hypothesis based on data, research, or user psychology principles.
Keep It Simple: Generally, it's best to test one variable at a time in A/B tests to get clear results. If your hypothesis introduces multiple changes at once, you won't know which one made the difference. Prefer simple, isolated hypotheses to start.
Prioritize Your Hypotheses: Use a framework like the ICE method (Impact, Confidence, Ease) to score and prioritize your hypotheses based on their potential impact, your confidence in them, and the ease of implementing them. Test the highest priority items first.
Remember, not every hypothesis will be proven true—and that's okay! Failed hypotheses still provide valuable learning that can inform future tests.
The key is to keep testing and iterating based on the results. Over time, well-crafted hypotheses will help you zero in on the changes that make the biggest difference for your users and your business.
How To Prioritize Your A/B Testing Hypotheses
Use a Prioritization Framework: Prioritization frameworks provide a structured way to evaluate and rank test ideas. With these frameworks, you can decide which hypothesis makes the most sense to test first. Some popular ones include:
- PIE (Potential, Importance, Ease): Rates ideas based on their potential impact, importance to the business, and ease of implementation. However, the criteria can be subjective.
- ICE (Impact, Confidence, Ease): Assesses ideas on their potential impact, your confidence in the idea, and ease of implementation. But if you're very confident, testing may not be needed.
- PXL: A more objective framework that aims to address issues with PIE and ICE. It uses specific questions to score ideas and allows for customization to your business.
Make sure to choose a framework that aligns with your business goals and provides consistency in prioritization.
Ground Hypotheses in Data: Effective test ideas should be based on data, not just gut instinct. Analyze user behavior, customer feedback, and analytics to identify problem areas and form data-driven hypotheses.
Hypotheses with supporting data should be prioritized over those without. Data collection takes time but leads to more impactful tests.
Align with Business Goals: Prioritize tests that have the greatest potential impact on core business metrics like revenue, leads, or retention. Minor UI changes may not be as valuable as tests targeting key flows like checkout or signup.
Allow for Iteration: Tests that open up possibilities for iteration and further learning can be more valuable than one-off tests. Prioritize tests that fit into a larger experimentation roadmap.
Balance Difficulty with Impact: Consider both the potential impact and the difficulty of implementing the test (technical complexity, time required, etc). Aim for a mix of high-impact/high-effort and low-effort/medium-impact tests.
Be Open to Changing Priorities: While having a prioritization system is critical, allow for some flexibility. New data or changing business needs may require adjusting test priorities.
Make sure to use a prioritization approach that is objective, aligned with business goals, and grounded in data—while still allowing room for iteration and adaptation as you learn.
With consistent prioritization over time, you can better focus your testing program on the highest impact experiments.
Examples of A/B Testing Formats
There is no one-size-fits-all format for A/B testing. In fact, they can fit many forms depending on what you want to test and optimize across your conversion funnels.
Here are some of the most common A/B testing formats with examples, so you can decide which format works best for your Shopify business:
Variant (or Classic) A/B Tests: This is the most basic and widely used type of A/B test. You create two different versions of a single webpage, show each version to half of your traffic, and measure which one performs better against a goal like conversions.
For example, you could test two different hero images on your homepage to see which one results in more product sales.
Multivariate Tests: In this type of test, you're evaluating multiple elements and how they interact with each other to affect performance.
For example, you might test three different headlines, two images, and two call-to-action buttons—resulting in 12 possible combinations (3x2x2). Multivariate tests are useful for finding the best combination of elements on a complex page.
Split URL Tests (or Redirect Tests): This involves testing two entirely different web pages against each other using separate URLs. Half of the traffic is sent to the original URL (control) and the other half to a variation URL. This is useful for testing radical redesigns or very different landing pages for the same campaign.
Multi-Page Funnel Tests: These allow you to test changes across an entire sequence of pages, like an organic search to checkout flow or signup process. You can see how changes on one page impact behavior further down the funnel.
For example, you could test a redesigned "add to cart" button and measure if it increases purchases on the final checkout page.
Personalization Tests: Here you're dynamically showing different content to different user segments based on attributes like location, past behavior, or demographics. The goal is delivering targeted experiences to each group.
For instance, showing returning customers a "welcome back" message vs. a generic headline for new visitors.
All in all, the right type of A/B test depends on your specific goals and the scope of changes you want to make. To start, we recommend going with simple variant tests, then progressing to more advanced formats like multivariate and personalization as you gain experience and look to optimize further.
How Timing is Crucial for A/B Testing on Shopify
Timing plays a crucial role in the success of your A/B tests on Shopify. We’ve compiled a list of considerations for you to time your tests more effectively:
Test Duration: Experts recommend running A/B tests for a minimum of two weeks, ideally three to four weeks. This allows enough time to account for variations in user behavior throughout the week and gather a representative sample size. Avoid ending tests prematurely, even if you reach your target sample size earlier.
Seasonality and Events: Be mindful of seasonal trends, holidays, and special events that could skew your test results. For example, running a test during a major sale like Black Friday might not reflect typical user behavior. Consider running tests during more "normal" periods to get accurate insights.
Customer Shopping Patterns: Study your store analytics to understand when customers tend to shop for your product or service. Some audiences are more active on weekends, while others shop more during weekdays. Tailor your test timing to capture a balanced mix of your key customer segments.
Promotional Campaigns: Avoid launching A/B tests simultaneously with major promotional campaigns, as the increased traffic and altered user behavior could lead to inaccurate test results. If you need to run a test during a promotion, factor the campaign into your analysis.
Time to Statistical Significance: The time required to reach statistically significant results will vary based on your store's traffic and the impact of the changes you're testing. Tests of high-impact elements like CTAs may conclude faster than tests of smaller changes.
Iteration and Re-Testing: Remember that A/B test results aren't permanent. User preferences evolve over time, so consider re-running tests periodically to ensure your optimizations are still effective. Continuously iterate and test to stay aligned with shifting customer behavior.
Strategically timing your Shopify A/B tests and running them for a sufficient duration means you can gather more accurate insights to inform your conversion rate optimization decisions. A good rule of thumb is to prioritize testing during periods that represent your typical traffic and customer behavior, and to avoid skewed results from one-off events or campaigns.
How To Ensure Your Shopify A/B Tests Are Statistically Significant
One of the greatest pitfalls of running A/B tests is assuming that you have a “winning” combination for your online store, just because one variation performs numerically better.
Instead, a winner can not be determined until you’ve been able to determine that the result is statistically significant, and not just a random result.
To ensure that you are not jumping to conclusions based on your A/B results, follow these key tips:
Determine the Right Sample Size: Calculating the minimum required sample size before running your test is crucial for achieving statistically significant results. The sample size depends on factors like your baseline conversion rate, minimum detectable effect, and desired confidence level.
Use an A/B test sample size calculator to determine how many visitors each variation needs. Aim for a sample size that gives you 80-95% statistical power at a 95% confidence level.
Let Tests Run to Completion: Avoid stopping tests prematurely, even if early results look promising. Ending a test too soon can lead to inaccurate conclusions due to small sample sizes.
Let your test run until it has reached the predetermined sample size or time duration (usually at least 1-2 business cycles). Resist the urge to peek at results frequently or make decisions based on interim data, as this can introduce bias. Trust the math and wait for the final numbers.
Use Proper Statistical Significance Thresholds: Statistical significance is usually measured by the p-value, which indicates the probability that the observed results happened by random chance.
The standard p-value threshold is 0.05, meaning there's a 5% chance the results are due to chance. Some experimenters use an even stricter cut-off of p < 0.01 (99% confidence level) to be extra certain. Choose your significance threshold upfront and stick to it when evaluating results.
Account for Multiple Comparisons: If you're testing many variations at once or peeking at the data often, you may need to adjust your significance threshold to account for the increased likelihood of false positives.
Techniques like the Bonferroni correction divide your original p-value threshold by the number of variations to keep the overall error rate in check. Many A/B testing tools handle this math for you.
Ensure Proper Randomization: To achieve unbiased, trustworthy results, it's critical that visitors are randomly split between your control and treatment groups. Use A/B testing software that performs this randomization automatically.
Avoid common randomization pitfalls like assigning repeat visitors to the same variation they originally saw, which can skew results.
Look Beyond p-values: While statistical significance is important, it doesn't tell the full story. A test can be statistically significant but have a very small actual effect. Always consider the practical significance of your results in a greater strategic context as well.
Calculate the percent difference in conversion rates and apply it to your real-world goals and revenue projections. Sometimes a "losing" variation can still be worth implementing if it promises worthwhile gains.
The keys to statistically valid A/B tests are careful planning, sufficient sample sizes, patience to reach significance, and a holistic interpretation of the final results. With these guidelines, you can be confident your experiments are producing reliable, actionable insights.
Common A/B Testing Mistakes to Avoid
A/B testing is a powerful tool for optimizing your Shopify store, but it's easy to make mistakes that can lead to inaccurate results and wasted time.
We’ve talked to several ecommerce agencies, and here are some of the most common A/B testing pitfalls you should be aware of:
Not Having a Clear Hypothesis: Every A/B test should start with a specific, measurable hypothesis about what you expect to happen. Running tests based on hunches or guesses often leads to inconclusive results.
Calling Tests Too Early: Ending a test before it has reached statistical significance can lead to false conclusions. Let tests run their full course, even if early results look promising, to ensure reliability.
Testing Too Many Variables at Once: Changing multiple elements in a single test makes it impossible to know which change caused a difference in the resulting desired action. Test one variable at a time for clear insights.
Running Multiple Tests Simultaneously: Conducting several A/B tests on the same page at the same time can cause interaction effects that skew results. Run tests sequentially instead.
Ignoring Statistical Significance: Just because one variation performs numerically better doesn't mean it's a true winner. Always use proper statistical significance thresholds (usually 95% confidence) before drawing conclusions.
Testing the Wrong Pages: Prioritize testing high-impact pages like your homepage, product pages, checkout funnel, and pages that rank in search engine results. Testing low-traffic or less important pages is often a waste of time.
Failing to Iterate: A/B testing is an ongoing process. Don't give up if a test produces negative or neutral results—use that data to inform new hypotheses and tests.
Not Segmenting Traffic: Different types of visitors may behave differently. Segment your test results by factors like traffic source, device, new vs. returning visitors, etc., to uncover deeper insights.
Use Shopify A/B Testing To Optimize Your Landing Pages For Conversions
https://www.youtube.com/watch?v=n9Elx-Lt6Eo
No doubt, you now have a much better understanding of the how’s and why’s behind A/B testing. Not only is it a powerful tool that can significantly enhance your Shopify store's performance, it can also give you a data-backed understanding of your customers' behavior.
\With this, you can tailor the content of your pages to match their preferences and optimize the store to drive more traffic and conversions.
Use A/B testing built right into Replo to experiment and learn faster than ever. Now, rather than relying on external tools and integrations, our own build-in testing feature allows you to launch experiments with different page variations in a matter of clicks. Try it out and access a step-by-step walkthrough of A/B testing on Replo with this video tutorial.
Any insights drawn from A/B tests can be quickly implemented in our easy-to-use, no-code drag and drop landing page editor that offers all the flexibility and functions necessary to fully customize your site. It’s tailored to the needs of ecommerce teams and directly integrates with your Shopify store, so you can better turn your pages into an acquisition funnel that converts.
While you can dive into building pages from scratch, we’d recommend leveraging our collection of hundreds of landing page templates inspired by top brands and high converting pages. Browse through them to get started and save time by adapting a template (or however many as you like) for your own site.
Need a hand with landing page building and all things CRO? No worries.
Tap into our community of Experts for hire and 24/7 support to help you make the most out of Replo. Visit us to get started, or reach out to schedule a demo with us. Join our Slack community and follow us on X to stay updated.