Introduction to A/B Testing
A/B testing, also known as split testing, is a method used to compare two versions of a webpage, email, ad, or other marketing material to determine which one performs better. The goal of A/B testing is to increase conversions and other metrics important to your business.
A/B testing is essential for ecommerce businesses because even a small improvement in conversion rates can result in significant revenue gains. With the fierce competition online, A/B testing provides data-driven insights to gain an advantage over competitors.
This beginner's guide will provide an overview of how to set up and run A/B tests in Shopify. We’ll cover:
- How to choose elements on your store to test
- Setting up A/B tests properly in Shopify
- Running tests and collecting data
- Analyzing results to find a winning variation
- Implementing the winning variation site-wide
- A/B testing best practices
- Common mistakes to avoid
- How to optimize tests for higher conversions
Whether you’re new to split testing or want a refresher, this guide will help you run effective A/B tests and make data-driven decisions to increase your Shopify store’s conversions. Let’s get started!
Choosing Elements to Test
When setting up your first A/B test, it's important to carefully consider which elements on your Shopify store to test. You want to focus on high-impact areas that could potentially lead to significant improvements in conversions and revenue.
Some of the key elements to test include:
- Page elements - Headers, descriptions, testimonials, trust badges, guarantees, etc. Try different formats, lengths, styles to see what resonates most with customers.
- Calls to action - Button text, color, size, placement. Test different verbs like "Add to Cart" vs "Buy Now" and colors like green vs red buttons.
- Product copy - Titles, descriptions, bullet points. Try different text length, formatting, tone, keywords.
- Images - Main images, thumbnails, product photos. Test different angles, compositions, zooms, placements.
- Pricing - Test offering sales, discounts, bundled pricing, limited time offers. Make sure to test at different price points.
- Cart & Checkout - Test order bumps, discounts, trust symbols, guarantees, testimonials. Removing friction here can lift conversions.
The key is to identify elements that customers regularly encounter and interact with on your store. Changes to these can compound over time. Start with your highest traffic pages and most popular products to maximize impact. Prioritize improvements to mobile experience as well.
Setting Up an A/B Test in Shopify
Setting up an effective A/B test in Shopify requires some strategic planning and configuration. Here are the key steps:
Install the Shopify A/B Testing App
The first step is to install the Shopify A/B testing app. This provides the interface and tools to set up and run A/B tests directly within your Shopify store. The app is available on the Shopify app store for free.
Once installed, you can access the app from your Shopify admin. You may need to enable the app if it doesn't appear automatically.
Choose a Goal for Your Test
Before creating your A/B test, think carefully about what you want to accomplish. Ask yourself - what is the purpose of this test and how will I measure success?
Common goals are increasing conversion rate, average order value, sign ups, engagement etc. Be as specific as possible in defining your goal. The A/B testing app allows you to choose from pre-set goals or create a custom one.
Set Up the Test Variations
Now it's time to set up the elements you want to test. Using the app, you can choose the store page and the specific component you want to A/B test.
For example, testing a product page header, call to action button, image etc. Create the two (or more) variations - the original and the proposed change(s). The app lets you preview the variants side-by-side.
Choose a Winner Metric
You'll need to designate how you want the winning variant determined. Generally this will be whichever variation meets your defined goal - whether that's more conversions, lower bounce rate etc. You can also set a specific "winner" period if needed.
The Shopify A/B test app will automatically analyze the results and highlight the winning variation after sufficient data is collected.
Launch the Test
Review all test configurations and preview the variants one more time. Make sure the changes are displaying properly. When ready, launch the live test! The app will begin splitting traffic and tracking interactions.
Now you can monitor performance and await the final results. Be sure to let the test run long enough to collect statistically significant data before declaring a winner.
Running an A/B Test
Once you've set up your A/B test in Shopify, it's time to run the experiment and let the data come in. Here are some tips for running an effective test:
- Length of test - Most tests should run for at least 1-2 weeks. This gives enough time to gather sufficient data and allows for natural fluctuations in traffic. Don't stop the test too early before statistical significance is achieved.
- Number of visitors needed - You typically want at least 1000 unique visitors per variation to gain statistically significant results. The more visitors and data, the more confident you can be in the test winner. Use Shopify's sample size calculator to estimate visitors needed.
- Let the test run - Allow the test to run its full course before choosing a winner. Don't get impatient and end the test prematurely. Stick to the predetermined test length.
- Avoid making changes during the test - Don't alter anything on the pages being tested during the experiment. This could skew results. Make all changes after the test concludes.
- Check in periodically - Monitor analytics periodically to ensure the test is running smoothly. Look for any anomalies or technical issues. But avoid drawing conclusions until the test has finished.
- Be patient - Have patience and let the data come in. Refrain from guessing the winner early on or changing the test based on initial assumptions. Hold out for statistically significant data.
Running an effective A/B test requires discipline and patience to follow best practices. But sticking to these tips will help you accurately determine the winning variation based on website visitor behavior.
Analyzing Results
Once you've collected enough data, it's time to analyze the results of your A/B test. There are a few key things to look for:
Statistical Significance
Statistical significance refers to whether the difference between the variants is likely due to chance or not. To measure this, Shopify calculates a p-value, which tells you the probability that the results occurred randomly. The lower the p-value, the more statistically significant the results. Generally, a p-value under 0.05 is considered statistically significant.
Confidence Level
The confidence level indicates how sure you can be that the winning variant actually outperformed the others. On Shopify, A/B tests are set to a 95% confidence level by default. This means there's a 95% chance that the winning variant actually did better. The more visitors and conversions your test gets, the higher the confidence level will be.
Determining a Winner
Shopify makes it easy to see which variant performed best. In the results, the "winning" variant will be marked in green. To determine a winner, Shopify looks for a variant that is both statistically significant and has the highest conversion rate. If no variant passes the statistical significance test, then no winner is determined.
The most important thing is that the winning variant outperforms the original by a big enough margin to have a meaningful impact on your store. Even small gains can add up over time. If the test results aren't totally conclusive, you may want to run the test again to further confirm which variant works best for your store.
Implementing Winning Variation
Once you've identified the winning variation in your A/B test, it's time to make that variation live on your site. Here are some tips for implementing the winning variation:
- Make the change across your entire website, not just on the specific page you tested. The winning variation likely will lift other areas of your site too.
- Put a plan in place to continue monitoring the performance of the winning variation after it goes live. Check your key metrics like conversion rate, revenue, etc and watch for any changes. You want to ensure the lift you saw in testing persists.
- Consider adding the winning variation to other similar pages on your site. For example, if you tested button color on your product page and found a new color drove more conversions, update buttons on other product pages to the new color too.
- Don't immediately stop all other variations. You may want to keep the losing variations live but at a lower traffic allocation to monitor if performance changes over time.
- Document what you changed and the impact it had so you can reference for future tests. Track each optimization you make to better understand what works for your business.
- Set a timeline for when you'll test again. A/B testing is an ongoing process, not a one-time effort. Continuously test new ideas to improve your store.
Carefully monitoring performance and optimizing based on test results will help maximize the lifts you see from A/B testing. Treat your winning variation as the start of an ongoing optimization, not the end of a single test.
A/B Testing Best Practices
When setting up and running A/B tests, follow these best practices:
- Test one thing at a time - Only test one element or change per A/B test. This makes it easier to determine which variation drove results. Testing multiple changes at once makes it difficult to pinpoint what impacted conversions.
- Test relevant, high-traffic pages - Focus on high-priority or high-traffic pages, as these will provide sufficient sample size. Don't waste time testing low-impact pages. Consider product, collection or other key pages.
- Allow for an adequate sample size - In order for statistical significance, allow each variation to receive sufficient traffic. As a general rule, 300+ conversions per variation is recommended. Test durations of 1-2 weeks often achieve this.
- Equally split traffic - Split traffic 50/50 between A and B variations. Uneven splits make it hard to properly compare variations. Shopify lets you easily split traffic evenly.
- Be patient - Don't stop a test too soon. Allow time for each variation to accumulate traffic for statistical significance. One to two weeks is often sufficient duration.
Following these best practices will result in high-quality A/B tests that yield actionable data and insights. Focusing on key pages and allowing enough time for an adequate sample size is key for valid results.
Common Mistakes to Avoid
When running A/B tests in Shopify, it's important to avoid some common pitfalls that can lead to inaccurate or misleading results. Here are some of the most common mistakes to avoid:
Stopping tests too early - One of the biggest mistakes is stopping an A/B test too early before statistical significance is reached. Ending a test prematurely means you don't have enough data to say which variation actually performed better. Be patient and let tests run their full course to get valid results.
Making too many changes at once - Resist the urge to test too many elements on a page at the same time. If you test multiple variables simultaneously, you won't be able to isolate the impact of each one. Stick to testing just one or two isolated changes per test.
Not having a hypothesis - Every A/B test should start with a hypothesis of how you expect a change to impact customer behavior. This gives you something specific to measure. Without a hypothesis, you won't know if a test succeeded or not.
Not tracking the right metrics - Carefully choose which metrics to track that will indicate whether a variation achieved your goals. Don't just look at overall conversion rate - identify the key steps you want to optimize for.
Not testing properly - Be sure to set up your tests correctly so that variations are shown randomly and a sufficient sample size is reached. Invalid test setups can lead to misleading or meaningless data.
Changing too many variables - When running multiple tests, be careful not to change too many variables on the same page at the same time. Overlapping tests make it impossible to understand what change impacted metrics.
By avoiding these common mistakes, you can run effective A/B tests in Shopify that provide reliable data on how to optimize your store for conversions. Focus on running isolated, statistically valid tests with proper tracking to get the most actionable results.
Optimizing for Conversions
A/B testing is a powerful tool for increasing conversions on your Shopify store. Here are some tips for using A/B testing to maximize conversions:
- Test checkout process - The checkout process is one of the most critical parts of the customer journey. Try variations like adding/removing fields, simplifying steps, offering guest checkout, etc.
- Test calls-to-action (CTAs) - Experiment with your CTAs by changing the text, color, size, placement etc. Strong CTAs can significantly improve conversions.
- Test page layout - Make changes to your product and landing page layouts with A/B testing. Prioritize testing elements like headers, images, add to cart button placement etc.
- Test copy - Tweaking your copy with different headlines, descriptions and value propositions can impact conversions. Always A/B test new copy.
- Test images - Use A/B testing to determine which images resonate best with your audience. Consider variables like size, number of images and image styling.
- Test offers - Try adding promotions, discounts or free shipping offers as variants to boost conversions. But avoid steep discounting which could devalue your brand.
- Test social proof - Adding social proof elements like reviews, testimonials and trust badges could provide the credibility needed to convert visitors.
- Test page speed - Faster page load speeds can increase conversions. Try optimizing images or reducing HTTP requests to improve page speed.
- Test mobile experience - With increased mobile traffic, ensure your mobile experience is optimized for completing purchases through testing.
By continually testing, you can gain invaluable insights into what resonates with your customers and maximize your conversion rates over time.
Conclusion
A/B testing is an invaluable practice for Shopify store owners looking to optimize their stores and increase conversions. By testing variations of elements like product descriptions, calls-to-action, images, and more, you can gain concrete data on what resonates best with your target audience.
When done correctly, A/B testing provides you with insights that opinions and assumptions alone cannot. The data it provides allows you to confidently make changes to your store, knowing that you're providing the best possible experience for converting customers.
Some key benefits to keep in mind are:
- A/B testing removes guesswork and provides data to support optimization decisions. You know what works based on real customer engagement data.
- Testing can lead to significant increases in conversions and revenue. Even small changes like button color or text can lift conversions when optimized.
- The process builds a culture of constant testing and improvement. You learn more about your customers and can continuously refine the shopping experience.
- Testing different elements prevents stagnation. You can experiment without fully committing to a change.
To recap some final tips:
- Start with an element you think has room for improvement and can be easily changed. Don't try to test too many variables at once.
- Use an A/B testing app like Google Optimize to remove friction from setting up and running tests.
- Analyze results carefully before determining a winner. Look at statistical significance, not just which variation performed slightly better.
- Be patient and run tests for an adequate length of time to collect enough data.
- Focus on elements that impact conversions directly, like calls-to-action.
- Continuously test new elements after implementing winning variations from past tests.
Following these A/B testing best practices will lead to a data-driven optimization process that boosts conversions over time.