And always ask yourself the golden questions:
Recommended reading: How to formulate a smart A/B test hypothesis (and why they’re crucial)
One of the most common pre-testing blunders is failing to segment different populations in your A/B test. It’s tempting to lean on the overall conversion rate (CVR) as your go-to metric because it’s straightforward. But this shortcut can lead to skewed results and misguided decisions, leaving you with a pile of unqualified leads and wasted resources.
When you focus on the overall CVR, you ignore the nuances of your diverse audience. Different segments—new visitors, returning users, mobile users, and so forth—each have unique behaviors and preferences.
Without segmenting your traffic, you might end up optimizing for visitors who aren’t your primary target, boosting conversion rates among less valuable segments while missing out on potential gains from your target audience. This misalignment means your optimizations won’t drive the impactful results you’re aiming for.
After you’ve taken those steps, get even better results with our AI-powered optimization tool Smart Traffic. It can automate and deepen personalization by considering variables like device type, location, and more. It dynamically routes visitors to the page variant most likely to convert, taking the guesswork out of audience segmentation.
By focusing on specific audience segments, you’ll gather stronger insights that lead to more effective optimizations. Plus, with Smart Traffic, you can sit back and let the automation work its magic, ensuring each visitor gets a personalized experience that drives conversions.
Sure, your “About Us” page is awesome, filled with delightful designs and fun, informative copy. But is it worth your time running an A/B test on? If it isn’t directly driving any conversions, then the answer is “no.” You’re plenty busy already, so it’s best to spend your time and effort on pages that will actually make some magic happen.
By aligning your A/B tests with the customer journey, you’ll focus your efforts on the most impactful areas, driving meaningful improvements in your conversion rates.
If your page is a ghost town, your A/B test results will be spookily unreliable. Without significant traffic, you’re not going to hit that sweet 95% statistical significance mark, meaning your results are about as trustworthy as a fortune cookie prediction. (The 95% statistical significance is an industry standard, and it means that if you ran the experiment 20 times, you would likely get the same results 19 times.)
Check your traffic and conversions using a sample size calculator (we’ve got a pretty good one). If you’re running low on visitors, remember: A/B testing isn’t the only trick in your CRO (conversion rate optimization) toolbox. Try using surveys or heatmaps instead, or do holdout experiments where a small group isn’t exposed to changes, helping you spot long-term effects.
Standard A/B testing operates on the assumption that users don’t influence each other, but this isn’t always true in the online world. Users interact, share experiences, and even sway each other’s decisions.
These interactions can mess with your results, leaving you scratching your head over misleading data. Imagine running a test where Group A sees a new feature and Group B doesn’t. If Group A users rave about it on social media or through word of mouth, Group B users might get influenced, skewing your test results.
Ignoring these interactions can lead to inaccurate conclusions and flawed optimization strategies. If you think your test results are solely based on isolated user behavior, you might miss out on understanding how social influence and network effects impact your data.
To get a clearer picture of user behavior, use network A/B testing to account for group interactions or avoid them altogether. Here’s how you can do it:
By accounting for these interactions, you’ll gain a more accurate understanding of user behavior, leading to better, more reliable optimization decisions.
One of the most overlooked aspects of A/B testing is failing to involve your colleagues from different departments. When only a few individuals handle the testing process, you miss out on valuable insights and innovative ideas that could significantly impact your results. Collaboration across departments brings in fresh perspectives and diverse experiences, which can lead to more effective and creative testing strategies.
Another reason why it’s beneficial to keep other teams in the loop is because your test might impact different areas of marketing or down-funnel activities. For example, you might soft launch a feature that other teams weren’t aware was available to customers, leading to possible confusion.
Involve team members from different departments in the A/B testing process. Here’s how to do it effectively:
Recommended resource: Paid media experiment brief—use this template to plan, build, and optimize your experiments so you can run more experiments, more efficiently.
Now let’s dive into the common mistakes that happen during the testing process and how to avoid them like a pro.
It’s tempting to think that a stunning design will naturally lead to higher conversions. But a beautifully designed page won’t always take the cake. Design is important, but only if it supports the real star of your page: the copy. A visually appealing page might not necessarily resonate with your audience or drive them to take action.
Start with strong, persuasive copy and then create a design that complements it. Always prioritize functionality and clarity over aesthetics. Test different design elements to see which ones truly enhance user experience and drive conversions.
Testimonials can be powerful, but they aren’t a guaranteed win. It’s a common mistake to assume that adding testimonials will always boost conversions without testing them. Even elements as trusted as testimonials need to be tested to ensure they’re effective for your specific audience and context.
Approach testimonials with the same scrutiny as any other content. Test different formats, placements, and styles to find out what resonates best with your audience.
In the pursuit of higher conversions, it’s easy to lose sight of your brand’s unique voice and personality. Over-optimizing for conversion can sometimes dilute what’s special about your brand, leading to a disconnect with your loyal customers.
Maintain a balance between optimizing for conversions and preserving your brand’s voice. Use A/B testing to find the sweet spot where your brand’s personality shines through while also driving conversions.
We get it—waiting is hard. But cutting your test short is like leaving a cake half-baked. Without enough time, your results won’t reach statistical significance, and you’re just gambling with your data.
Stay disciplined. Don’t stop your test before hitting the 95% significance mark. Let your A/B testing tool declare a winner, or better yet, wait until you’ve reached your pre-calculated minimum sample size. Patience is a virtue, especially in A/B testing.
Some A/B testing tools can slow your site by up to a second. It may not sound like much but it’s actually a big deal since, according to Google, 53% of users abandon mobile sites that take more than three seconds to load. If your site slows down, your conversion rate will likely drop, skewing your test results.
Run an A/A test first—test your tool without any changes to see if it impacts your site’s performance. This will help you identify any performance issues before your real test starts.
Tools with server-side loading (just like our own A/B testing tool) can help avoid delays and flickers, so you’ll get the cleanest, most accurate results.
Simplicity is key. Running multiple tests simultaneously can muddle your results. While it’s okay to test different versions of a single element, running too many tests at once demands a larger sample size and complicates your analysis.
Limit yourself to just the number of tests that you and your team can handle without getting overwhelmed, and focus on significant elements like your CTA button or headline. By keeping things streamlined, you’ll gather clearer, more actionable insights.
Traffic fluctuates, and comparing results from different periods can mislead you. Comparing a high-traffic Wednesday to a low-traffic Tuesday is like comparing apples to oranges. Seasonal events or external factors can further distort your results.
Run your tests over similar and comparable time periods to get consistent data. For instance, if you’re an ecommerce retailer, don’t compare holiday season traffic with post-holiday slumps—instead, try comparing similar holiday seasons across different years. Consistency is crucial for reliable insights.
Tweaking your test mid-way is the quickest route to invalid results. Whether it’s adjusting traffic allocation or altering variables, mid-test changes can skew your data and lead to false conclusions.
Set your parameters and stick to them. If you absolutely need to make changes, start a new test. Consistency ensures that your results are valid and actionable.
You’ve run your A/B test, collected your data, and declared a winner. But hold your confetti, because the end of the test doesn’t mean the end of your work. There are several common mistakes that can mess with your results after the test is over, if you don’t know how to avoid them.
Between waiting for statistical significance and making incremental changes, A/B tests demand high maintenance. That’s why thorough documentation is crucial to squeeze every drop of learning from your experiments. Without proper records, you miss out on valuable insights, waste resources, and lack direction for future tests.
Create a template for documenting internal A/B tests and ensure everyone sticks to it. Your documentation should include:
Recommended resource: Paid media experiment brief template
It’s easy to shrug off a failed hypothesis and move on, especially if you’ve been waiting weeks for the results. But giving up too soon means you’re not fully digesting your learnings.
If your hypothesis was grounded in data but the test didn’t achieve the desired result, tweak your approach and try again. Here are your options:
Convincing A/B test results can be persuasive, but overestimating their implications can lead to trouble. For example, if adding a sign-up pop-up increases your mailing list on one page, it doesn’t mean you should plaster pop-ups everywhere. Overdoing it might annoy users and increase your bounce rate.
Go slow and steady with your changes. Remember, an A/B test answers a specific question. Implement changes gradually and monitor their impact carefully before rolling them out site-wide.
Accurate measurement is as crucial as accurate testing. If you don’t measure results properly, your data becomes unreliable, making it impossible to make informed decisions.
Ensure your A/B testing solution integrates with Google Analytics for better control and insights. This way, you can track your test results accurately and gain actionable insights.
It’s tempting to copy what worked for others, but what works for one company might not work for yours. Every business is unique, and blindly following case studies can lead you astray.
Use case studies as a reference point to generate ideas, but develop your own A/B testing strategy tailored to your audience. This approach ensures that your tests are relevant and effective for your specific needs.
Recommended reading: How a three-word A/B test led to triple-digit conversion growth
A 2% or 5% increase in conversion might seem insignificant, but small gains add up over time. Ignoring them is one of the biggest A/B testing mistakes you can make.
Embrace small wins. Look at them from a 12-month perspective—even a small percentage of steady, continuous growth can lead to huge returns over a full year.
Without a clear plan, A/B tests can become a random guessing game. It’s challenging to draw significant conclusions without a strategic approach, leading to wasted resources and fleeting wins.
Even with a decent sample size, confidence level, and test duration, your test results can be invalid due to several threats like the instrumentation effect (when a flawed instrument skews the data), the selection effect (when you incorrectly assume that a small portion of the traffic represents all of the traffic), and the broken code effect (when the page didn’t display properly on certain devices or browsers).
A winning variation for one segment might not work for another. It’s crucial to segment your audience and understand different user behaviors.
Like we mentioned before, it’s crucial to segment your users by demographics, behavior, and source when analyzing data. This approach helps you understand how different groups interact with your changes and ensures you’re optimizing for the right audience.
Changes that improve one metric might negatively impact another. It’s essential to consider the overall effect on your site’s performance.
Monitor downstream impacts carefully. Ensure that improvements in one area don’t lead to declines in another. This holistic approach helps maintain a balanced and effective optimization strategy.
Innovator and inventor Thomas Edison once said, “I have not failed. I’ve just found 10,000 ways that won’t work.” The same principle applies to A/B testing results. It’s not about “pass” or “fail”—when you run testing experiments, you’ll get results that are either impactful or inconclusive, but don’t just throw the non-impactful ones away.
Look at your inconclusive results through a different lens: They show you what not to do. These results will reveal the factors that don’t have a strong impact on your conversion, so you can instead focus on what matters.
A/B testing is a powerful tool in your conversion optimization arsenal, but it’s not foolproof. From planning your hypotheses to analyzing your results, every step of the testing process requires careful attention to avoid common pitfalls. By steering clear of these mistakes—whether they occur before, during, or after your tests—and following A/B testing best practices, you can ensure your experiments yield meaningful, actionable insights.
Ready to start your own A/B testing journey (with fewer mistakes along the way)? Check out our A/B testing tool, which is built into the Unbounce builder. It’s super easy to build your own pages, test them, and analyze the results—optimization and higher conversion rates are just a few clicks away.
March 20, 2024 . 7 minute read
June 13, 2024 . 12 minute read
May 24, 2024 . 13 minute read
June 27, 2024 . 8 minute read
April 30, 2024 . 17 minute read
March 18, 2024 . 28 minute read
March 19, 2024 . 23 minute read
March 16, 2024 . 29 minute read
Get actionable insights, expert advice, and practical tips that can help you create high-converting landing pages, improve your PPC campaigns, and grow your business online.
And always ask yourself the golden questions:
Recommended reading: How to formulate a smart A/B test hypothesis (and why they’re crucial)
One of the most common pre-testing blunders is failing to segment different populations in your A/B test. It’s tempting to lean on the overall conversion rate (CVR) as your go-to metric because it’s straightforward. But this shortcut can lead to skewed results and misguided decisions, leaving you with a pile of unqualified leads and wasted resources.
When you focus on the overall CVR, you ignore the nuances of your diverse audience. Different segments—new visitors, returning users, mobile users, and so forth—each have unique behaviors and preferences.
Without segmenting your traffic, you might end up optimizing for visitors who aren’t your primary target, boosting conversion rates among less valuable segments while missing out on potential gains from your target audience. This misalignment means your optimizations won’t drive the impactful results you’re aiming for.
After you’ve taken those steps, get even better results with our AI-powered optimization tool Smart Traffic. It can automate and deepen personalization by considering variables like device type, location, and more. It dynamically routes visitors to the page variant most likely to convert, taking the guesswork out of audience segmentation.
By focusing on specific audience segments, you’ll gather stronger insights that lead to more effective optimizations. Plus, with Smart Traffic, you can sit back and let the automation work its magic, ensuring each visitor gets a personalized experience that drives conversions.
Sure, your “About Us” page is awesome, filled with delightful designs and fun, informative copy. But is it worth your time running an A/B test on? If it isn’t directly driving any conversions, then the answer is “no.” You’re plenty busy already, so it’s best to spend your time and effort on pages that will actually make some magic happen.
By aligning your A/B tests with the customer journey, you’ll focus your efforts on the most impactful areas, driving meaningful improvements in your conversion rates.
If your page is a ghost town, your A/B test results will be spookily unreliable. Without significant traffic, you’re not going to hit that sweet 95% statistical significance mark, meaning your results are about as trustworthy as a fortune cookie prediction. (The 95% statistical significance is an industry standard, and it means that if you ran the experiment 20 times, you would likely get the same results 19 times.)
Check your traffic and conversions using a sample size calculator (we’ve got a pretty good one). If you’re running low on visitors, remember: A/B testing isn’t the only trick in your CRO (conversion rate optimization) toolbox. Try using surveys or heatmaps instead, or do holdout experiments where a small group isn’t exposed to changes, helping you spot long-term effects.
Standard A/B testing operates on the assumption that users don’t influence each other, but this isn’t always true in the online world. Users interact, share experiences, and even sway each other’s decisions.
These interactions can mess with your results, leaving you scratching your head over misleading data. Imagine running a test where Group A sees a new feature and Group B doesn’t. If Group A users rave about it on social media or through word of mouth, Group B users might get influenced, skewing your test results.
Ignoring these interactions can lead to inaccurate conclusions and flawed optimization strategies. If you think your test results are solely based on isolated user behavior, you might miss out on understanding how social influence and network effects impact your data.
To get a clearer picture of user behavior, use network A/B testing to account for group interactions or avoid them altogether. Here’s how you can do it:
By accounting for these interactions, you’ll gain a more accurate understanding of user behavior, leading to better, more reliable optimization decisions.
One of the most overlooked aspects of A/B testing is failing to involve your colleagues from different departments. When only a few individuals handle the testing process, you miss out on valuable insights and innovative ideas that could significantly impact your results. Collaboration across departments brings in fresh perspectives and diverse experiences, which can lead to more effective and creative testing strategies.
Another reason why it’s beneficial to keep other teams in the loop is because your test might impact different areas of marketing or down-funnel activities. For example, you might soft launch a feature that other teams weren’t aware was available to customers, leading to possible confusion.
Involve team members from different departments in the A/B testing process. Here’s how to do it effectively:
Recommended resource: Paid media experiment brief—use this template to plan, build, and optimize your experiments so you can run more experiments, more efficiently.
Now let’s dive into the common mistakes that happen during the testing process and how to avoid them like a pro.
It’s tempting to think that a stunning design will naturally lead to higher conversions. But a beautifully designed page won’t always take the cake. Design is important, but only if it supports the real star of your page: the copy. A visually appealing page might not necessarily resonate with your audience or drive them to take action.
Start with strong, persuasive copy and then create a design that complements it. Always prioritize functionality and clarity over aesthetics. Test different design elements to see which ones truly enhance user experience and drive conversions.
Testimonials can be powerful, but they aren’t a guaranteed win. It’s a common mistake to assume that adding testimonials will always boost conversions without testing them. Even elements as trusted as testimonials need to be tested to ensure they’re effective for your specific audience and context.
Approach testimonials with the same scrutiny as any other content. Test different formats, placements, and styles to find out what resonates best with your audience.
In the pursuit of higher conversions, it’s easy to lose sight of your brand’s unique voice and personality. Over-optimizing for conversion can sometimes dilute what’s special about your brand, leading to a disconnect with your loyal customers.
Maintain a balance between optimizing for conversions and preserving your brand’s voice. Use A/B testing to find the sweet spot where your brand’s personality shines through while also driving conversions.
We get it—waiting is hard. But cutting your test short is like leaving a cake half-baked. Without enough time, your results won’t reach statistical significance, and you’re just gambling with your data.
Stay disciplined. Don’t stop your test before hitting the 95% significance mark. Let your A/B testing tool declare a winner, or better yet, wait until you’ve reached your pre-calculated minimum sample size. Patience is a virtue, especially in A/B testing.
Some A/B testing tools can slow your site by up to a second. It may not sound like much but it’s actually a big deal since, according to Google, 53% of users abandon mobile sites that take more than three seconds to load. If your site slows down, your conversion rate will likely drop, skewing your test results.
Run an A/A test first—test your tool without any changes to see if it impacts your site’s performance. This will help you identify any performance issues before your real test starts.
Tools with server-side loading (just like our own A/B testing tool) can help avoid delays and flickers, so you’ll get the cleanest, most accurate results.
Simplicity is key. Running multiple tests simultaneously can muddle your results. While it’s okay to test different versions of a single element, running too many tests at once demands a larger sample size and complicates your analysis.
Limit yourself to just the number of tests that you and your team can handle without getting overwhelmed, and focus on significant elements like your CTA button or headline. By keeping things streamlined, you’ll gather clearer, more actionable insights.
Traffic fluctuates, and comparing results from different periods can mislead you. Comparing a high-traffic Wednesday to a low-traffic Tuesday is like comparing apples to oranges. Seasonal events or external factors can further distort your results.
Run your tests over similar and comparable time periods to get consistent data. For instance, if you’re an ecommerce retailer, don’t compare holiday season traffic with post-holiday slumps—instead, try comparing similar holiday seasons across different years. Consistency is crucial for reliable insights.
Tweaking your test mid-way is the quickest route to invalid results. Whether it’s adjusting traffic allocation or altering variables, mid-test changes can skew your data and lead to false conclusions.
Set your parameters and stick to them. If you absolutely need to make changes, start a new test. Consistency ensures that your results are valid and actionable.
You’ve run your A/B test, collected your data, and declared a winner. But hold your confetti, because the end of the test doesn’t mean the end of your work. There are several common mistakes that can mess with your results after the test is over, if you don’t know how to avoid them.
Between waiting for statistical significance and making incremental changes, A/B tests demand high maintenance. That’s why thorough documentation is crucial to squeeze every drop of learning from your experiments. Without proper records, you miss out on valuable insights, waste resources, and lack direction for future tests.
Create a template for documenting internal A/B tests and ensure everyone sticks to it. Your documentation should include:
Recommended resource: Paid media experiment brief template
It’s easy to shrug off a failed hypothesis and move on, especially if you’ve been waiting weeks for the results. But giving up too soon means you’re not fully digesting your learnings.
If your hypothesis was grounded in data but the test didn’t achieve the desired result, tweak your approach and try again. Here are your options:
Convincing A/B test results can be persuasive, but overestimating their implications can lead to trouble. For example, if adding a sign-up pop-up increases your mailing list on one page, it doesn’t mean you should plaster pop-ups everywhere. Overdoing it might annoy users and increase your bounce rate.
Go slow and steady with your changes. Remember, an A/B test answers a specific question. Implement changes gradually and monitor their impact carefully before rolling them out site-wide.
Accurate measurement is as crucial as accurate testing. If you don’t measure results properly, your data becomes unreliable, making it impossible to make informed decisions.
Ensure your A/B testing solution integrates with Google Analytics for better control and insights. This way, you can track your test results accurately and gain actionable insights.
It’s tempting to copy what worked for others, but what works for one company might not work for yours. Every business is unique, and blindly following case studies can lead you astray.
Use case studies as a reference point to generate ideas, but develop your own A/B testing strategy tailored to your audience. This approach ensures that your tests are relevant and effective for your specific needs.
Recommended reading: How a three-word A/B test led to triple-digit conversion growth
A 2% or 5% increase in conversion might seem insignificant, but small gains add up over time. Ignoring them is one of the biggest A/B testing mistakes you can make.
Embrace small wins. Look at them from a 12-month perspective—even a small percentage of steady, continuous growth can lead to huge returns over a full year.
Without a clear plan, A/B tests can become a random guessing game. It’s challenging to draw significant conclusions without a strategic approach, leading to wasted resources and fleeting wins.
Even with a decent sample size, confidence level, and test duration, your test results can be invalid due to several threats like the instrumentation effect (when a flawed instrument skews the data), the selection effect (when you incorrectly assume that a small portion of the traffic represents all of the traffic), and the broken code effect (when the page didn’t display properly on certain devices or browsers).
A winning variation for one segment might not work for another. It’s crucial to segment your audience and understand different user behaviors.
Like we mentioned before, it’s crucial to segment your users by demographics, behavior, and source when analyzing data. This approach helps you understand how different groups interact with your changes and ensures you’re optimizing for the right audience.
Changes that improve one metric might negatively impact another. It’s essential to consider the overall effect on your site’s performance.
Monitor downstream impacts carefully. Ensure that improvements in one area don’t lead to declines in another. This holistic approach helps maintain a balanced and effective optimization strategy.
Innovator and inventor Thomas Edison once said, “I have not failed. I’ve just found 10,000 ways that won’t work.” The same principle applies to A/B testing results. It’s not about “pass” or “fail”—when you run testing experiments, you’ll get results that are either impactful or inconclusive, but don’t just throw the non-impactful ones away.
Look at your inconclusive results through a different lens: They show you what not to do. These results will reveal the factors that don’t have a strong impact on your conversion, so you can instead focus on what matters.
A/B testing is a powerful tool in your conversion optimization arsenal, but it’s not foolproof. From planning your hypotheses to analyzing your results, every step of the testing process requires careful attention to avoid common pitfalls. By steering clear of these mistakes—whether they occur before, during, or after your tests—and following A/B testing best practices, you can ensure your experiments yield meaningful, actionable insights.
Ready to start your own A/B testing journey (with fewer mistakes along the way)? Check out our A/B testing tool, which is built into the Unbounce builder. It’s super easy to build your own pages, test them, and analyze the results—optimization and higher conversion rates are just a few clicks away.
March 20, 2024 . 7 minute read
June 13, 2024 . 12 minute read
May 24, 2024 . 13 minute read
June 27, 2024 . 8 minute read
April 30, 2024 . 17 minute read
March 18, 2024 . 28 minute read
March 19, 2024 . 23 minute read
March 16, 2024 . 29 minute read
Get actionable insights, expert advice, and practical tips that can help you create high-converting landing pages, improve your PPC campaigns, and grow your business online.
To nail useful A/B testing results you need a rock-solid A/B test hypothesis, which is a clear, testable statement that predicts how changes to a landing page or element will impact user behavior. Here’s how to create a good hypothesis:
And always ask yourself the golden questions:
Recommended reading: How to formulate a smart A/B test hypothesis (and why they’re crucial)
One of the most common pre-testing blunders is failing to segment different populations in your A/B test. It’s tempting to lean on the overall conversion rate (CVR) as your go-to metric because it’s straightforward. But this shortcut can lead to skewed results and misguided decisions, leaving you with a pile of unqualified leads and wasted resources.
When you focus on the overall CVR, you ignore the nuances of your diverse audience. Different segments—new visitors, returning users, mobile users, and so forth—each have unique behaviors and preferences.
Without segmenting your traffic, you might end up optimizing for visitors who aren’t your primary target, boosting conversion rates among less valuable segments while missing out on potential gains from your target audience. This misalignment means your optimizations won’t drive the impactful results you’re aiming for.
After you’ve taken those steps, get even better results with our AI-powered optimization tool Smart Traffic. It can automate and deepen personalization by considering variables like device type, location, and more. It dynamically routes visitors to the page variant most likely to convert, taking the guesswork out of audience segmentation.
By focusing on specific audience segments, you’ll gather stronger insights that lead to more effective optimizations. Plus, with Smart Traffic, you can sit back and let the automation work its magic, ensuring each visitor gets a personalized experience that drives conversions.
Sure, your “About Us” page is awesome, filled with delightful designs and fun, informative copy. But is it worth your time running an A/B test on? If it isn’t directly driving any conversions, then the answer is “no.” You’re plenty busy already, so it’s best to spend your time and effort on pages that will actually make some magic happen.
By aligning your A/B tests with the customer journey, you’ll focus your efforts on the most impactful areas, driving meaningful improvements in your conversion rates.
If your page is a ghost town, your A/B test results will be spookily unreliable. Without significant traffic, you’re not going to hit that sweet 95% statistical significance mark, meaning your results are about as trustworthy as a fortune cookie prediction. (The 95% statistical significance is an industry standard, and it means that if you ran the experiment 20 times, you would likely get the same results 19 times.)
Check your traffic and conversions using a sample size calculator (we’ve got a pretty good one). If you’re running low on visitors, remember: A/B testing isn’t the only trick in your CRO (conversion rate optimization) toolbox. Try using surveys or heatmaps instead, or do holdout experiments where a small group isn’t exposed to changes, helping you spot long-term effects.
Standard A/B testing operates on the assumption that users don’t influence each other, but this isn’t always true in the online world. Users interact, share experiences, and even sway each other’s decisions.
These interactions can mess with your results, leaving you scratching your head over misleading data. Imagine running a test where Group A sees a new feature and Group B doesn’t. If Group A users rave about it on social media or through word of mouth, Group B users might get influenced, skewing your test results.
Ignoring these interactions can lead to inaccurate conclusions and flawed optimization strategies. If you think your test results are solely based on isolated user behavior, you might miss out on understanding how social influence and network effects impact your data.
To get a clearer picture of user behavior, use network A/B testing to account for group interactions or avoid them altogether. Here’s how you can do it:
By accounting for these interactions, you’ll gain a more accurate understanding of user behavior, leading to better, more reliable optimization decisions.
One of the most overlooked aspects of A/B testing is failing to involve your colleagues from different departments. When only a few individuals handle the testing process, you miss out on valuable insights and innovative ideas that could significantly impact your results. Collaboration across departments brings in fresh perspectives and diverse experiences, which can lead to more effective and creative testing strategies.
Another reason why it’s beneficial to keep other teams in the loop is because your test might impact different areas of marketing or down-funnel activities. For example, you might soft launch a feature that other teams weren’t aware was available to customers, leading to possible confusion.
Involve team members from different departments in the A/B testing process. Here’s how to do it effectively:
Recommended resource: Paid media experiment brief—use this template to plan, build, and optimize your experiments so you can run more experiments, more efficiently.
Now let’s dive into the common mistakes that happen during the testing process and how to avoid them like a pro.
It’s tempting to think that a stunning design will naturally lead to higher conversions. But a beautifully designed page won’t always take the cake. Design is important, but only if it supports the real star of your page: the copy. A visually appealing page might not necessarily resonate with your audience or drive them to take action.
Start with strong, persuasive copy and then create a design that complements it. Always prioritize functionality and clarity over aesthetics. Test different design elements to see which ones truly enhance user experience and drive conversions.
Testimonials can be powerful, but they aren’t a guaranteed win. It’s a common mistake to assume that adding testimonials will always boost conversions without testing them. Even elements as trusted as testimonials need to be tested to ensure they’re effective for your specific audience and context.
Approach testimonials with the same scrutiny as any other content. Test different formats, placements, and styles to find out what resonates best with your audience.
In the pursuit of higher conversions, it’s easy to lose sight of your brand’s unique voice and personality. Over-optimizing for conversion can sometimes dilute what’s special about your brand, leading to a disconnect with your loyal customers.
Maintain a balance between optimizing for conversions and preserving your brand’s voice. Use A/B testing to find the sweet spot where your brand’s personality shines through while also driving conversions.
We get it—waiting is hard. But cutting your test short is like leaving a cake half-baked. Without enough time, your results won’t reach statistical significance, and you’re just gambling with your data.
Stay disciplined. Don’t stop your test before hitting the 95% significance mark. Let your A/B testing tool declare a winner, or better yet, wait until you’ve reached your pre-calculated minimum sample size. Patience is a virtue, especially in A/B testing.
Some A/B testing tools can slow your site by up to a second. It may not sound like much but it’s actually a big deal since, according to Google, 53% of users abandon mobile sites that take more than three seconds to load. If your site slows down, your conversion rate will likely drop, skewing your test results.
Run an A/A test first—test your tool without any changes to see if it impacts your site’s performance. This will help you identify any performance issues before your real test starts.
Tools with server-side loading (just like our own A/B testing tool) can help avoid delays and flickers, so you’ll get the cleanest, most accurate results.
Simplicity is key. Running multiple tests simultaneously can muddle your results. While it’s okay to test different versions of a single element, running too many tests at once demands a larger sample size and complicates your analysis.
Limit yourself to just the number of tests that you and your team can handle without getting overwhelmed, and focus on significant elements like your CTA button or headline. By keeping things streamlined, you’ll gather clearer, more actionable insights.
Traffic fluctuates, and comparing results from different periods can mislead you. Comparing a high-traffic Wednesday to a low-traffic Tuesday is like comparing apples to oranges. Seasonal events or external factors can further distort your results.
Run your tests over similar and comparable time periods to get consistent data. For instance, if you’re an ecommerce retailer, don’t compare holiday season traffic with post-holiday slumps—instead, try comparing similar holiday seasons across different years. Consistency is crucial for reliable insights.
Tweaking your test mid-way is the quickest route to invalid results. Whether it’s adjusting traffic allocation or altering variables, mid-test changes can skew your data and lead to false conclusions.
Set your parameters and stick to them. If you absolutely need to make changes, start a new test. Consistency ensures that your results are valid and actionable.
You’ve run your A/B test, collected your data, and declared a winner. But hold your confetti, because the end of the test doesn’t mean the end of your work. There are several common mistakes that can mess with your results after the test is over, if you don’t know how to avoid them.
Between waiting for statistical significance and making incremental changes, A/B tests demand high maintenance. That’s why thorough documentation is crucial to squeeze every drop of learning from your experiments. Without proper records, you miss out on valuable insights, waste resources, and lack direction for future tests.
Create a template for documenting internal A/B tests and ensure everyone sticks to it. Your documentation should include:
Recommended resource: Paid media experiment brief template
It’s easy to shrug off a failed hypothesis and move on, especially if you’ve been waiting weeks for the results. But giving up too soon means you’re not fully digesting your learnings.
If your hypothesis was grounded in data but the test didn’t achieve the desired result, tweak your approach and try again. Here are your options:
Convincing A/B test results can be persuasive, but overestimating their implications can lead to trouble. For example, if adding a sign-up pop-up increases your mailing list on one page, it doesn’t mean you should plaster pop-ups everywhere. Overdoing it might annoy users and increase your bounce rate.
Go slow and steady with your changes. Remember, an A/B test answers a specific question. Implement changes gradually and monitor their impact carefully before rolling them out site-wide.
Accurate measurement is as crucial as accurate testing. If you don’t measure results properly, your data becomes unreliable, making it impossible to make informed decisions.
Ensure your A/B testing solution integrates with Google Analytics for better control and insights. This way, you can track your test results accurately and gain actionable insights.
It’s tempting to copy what worked for others, but what works for one company might not work for yours. Every business is unique, and blindly following case studies can lead you astray.
Use case studies as a reference point to generate ideas, but develop your own A/B testing strategy tailored to your audience. This approach ensures that your tests are relevant and effective for your specific needs.
Recommended reading: How a three-word A/B test led to triple-digit conversion growth
A 2% or 5% increase in conversion might seem insignificant, but small gains add up over time. Ignoring them is one of the biggest A/B testing mistakes you can make.
Embrace small wins. Look at them from a 12-month perspective—even a small percentage of steady, continuous growth can lead to huge returns over a full year.
Without a clear plan, A/B tests can become a random guessing game. It’s challenging to draw significant conclusions without a strategic approach, leading to wasted resources and fleeting wins.
Even with a decent sample size, confidence level, and test duration, your test results can be invalid due to several threats like the instrumentation effect (when a flawed instrument skews the data), the selection effect (when you incorrectly assume that a small portion of the traffic represents all of the traffic), and the broken code effect (when the page didn’t display properly on certain devices or browsers).
A winning variation for one segment might not work for another. It’s crucial to segment your audience and understand different user behaviors.
Like we mentioned before, it’s crucial to segment your users by demographics, behavior, and source when analyzing data. This approach helps you understand how different groups interact with your changes and ensures you’re optimizing for the right audience.
Changes that improve one metric might negatively impact another. It’s essential to consider the overall effect on your site’s performance.
Monitor downstream impacts carefully. Ensure that improvements in one area don’t lead to declines in another. This holistic approach helps maintain a balanced and effective optimization strategy.
Innovator and inventor Thomas Edison once said, “I have not failed. I’ve just found 10,000 ways that won’t work.” The same principle applies to A/B testing results. It’s not about “pass” or “fail”—when you run testing experiments, you’ll get results that are either impactful or inconclusive, but don’t just throw the non-impactful ones away.
Look at your inconclusive results through a different lens: They show you what not to do. These results will reveal the factors that don’t have a strong impact on your conversion, so you can instead focus on what matters.
A/B testing is a powerful tool in your conversion optimization arsenal, but it’s not foolproof. From planning your hypotheses to analyzing your results, every step of the testing process requires careful attention to avoid common pitfalls. By steering clear of these mistakes—whether they occur before, during, or after your tests—and following A/B testing best practices, you can ensure your experiments yield meaningful, actionable insights.
Ready to start your own A/B testing journey (with fewer mistakes along the way)? Check out our A/B testing tool, which is built into the Unbounce builder. It’s super easy to build your own pages, test them, and analyze the results—optimization and higher conversion rates are just a few clicks away.
March 20, 2024 . 7 minute read
June 13, 2024 . 12 minute read
May 24, 2024 . 13 minute read
June 27, 2024 . 8 minute read
April 30, 2024 . 17 minute read
March 18, 2024 . 28 minute read
March 19, 2024 . 23 minute read
March 16, 2024 . 29 minute read
Get actionable insights, expert advice, and practical tips that can help you create high-converting landing pages, improve your PPC campaigns, and grow your business online.
Ah, the gut feeling. It’s as tempting as that third cup of coffee, but just as risky. Starting an A/B test solely on a hunch or guess is very likely to lead to untrustworthy results and a heaping pile of disappointment.
To nail useful A/B testing results you need a rock-solid A/B test hypothesis, which is a clear, testable statement that predicts how changes to a landing page or element will impact user behavior. Here’s how to create a good hypothesis:
And always ask yourself the golden questions:
Recommended reading: How to formulate a smart A/B test hypothesis (and why they’re crucial)
One of the most common pre-testing blunders is failing to segment different populations in your A/B test. It’s tempting to lean on the overall conversion rate (CVR) as your go-to metric because it’s straightforward. But this shortcut can lead to skewed results and misguided decisions, leaving you with a pile of unqualified leads and wasted resources.
When you focus on the overall CVR, you ignore the nuances of your diverse audience. Different segments—new visitors, returning users, mobile users, and so forth—each have unique behaviors and preferences.
Without segmenting your traffic, you might end up optimizing for visitors who aren’t your primary target, boosting conversion rates among less valuable segments while missing out on potential gains from your target audience. This misalignment means your optimizations won’t drive the impactful results you’re aiming for.
After you’ve taken those steps, get even better results with our AI-powered optimization tool Smart Traffic. It can automate and deepen personalization by considering variables like device type, location, and more. It dynamically routes visitors to the page variant most likely to convert, taking the guesswork out of audience segmentation.
By focusing on specific audience segments, you’ll gather stronger insights that lead to more effective optimizations. Plus, with Smart Traffic, you can sit back and let the automation work its magic, ensuring each visitor gets a personalized experience that drives conversions.
Sure, your “About Us” page is awesome, filled with delightful designs and fun, informative copy. But is it worth your time running an A/B test on? If it isn’t directly driving any conversions, then the answer is “no.” You’re plenty busy already, so it’s best to spend your time and effort on pages that will actually make some magic happen.
By aligning your A/B tests with the customer journey, you’ll focus your efforts on the most impactful areas, driving meaningful improvements in your conversion rates.
If your page is a ghost town, your A/B test results will be spookily unreliable. Without significant traffic, you’re not going to hit that sweet 95% statistical significance mark, meaning your results are about as trustworthy as a fortune cookie prediction. (The 95% statistical significance is an industry standard, and it means that if you ran the experiment 20 times, you would likely get the same results 19 times.)
Check your traffic and conversions using a sample size calculator (we’ve got a pretty good one). If you’re running low on visitors, remember: A/B testing isn’t the only trick in your CRO (conversion rate optimization) toolbox. Try using surveys or heatmaps instead, or do holdout experiments where a small group isn’t exposed to changes, helping you spot long-term effects.
Standard A/B testing operates on the assumption that users don’t influence each other, but this isn’t always true in the online world. Users interact, share experiences, and even sway each other’s decisions.
These interactions can mess with your results, leaving you scratching your head over misleading data. Imagine running a test where Group A sees a new feature and Group B doesn’t. If Group A users rave about it on social media or through word of mouth, Group B users might get influenced, skewing your test results.
Ignoring these interactions can lead to inaccurate conclusions and flawed optimization strategies. If you think your test results are solely based on isolated user behavior, you might miss out on understanding how social influence and network effects impact your data.
To get a clearer picture of user behavior, use network A/B testing to account for group interactions or avoid them altogether. Here’s how you can do it:
By accounting for these interactions, you’ll gain a more accurate understanding of user behavior, leading to better, more reliable optimization decisions.
One of the most overlooked aspects of A/B testing is failing to involve your colleagues from different departments. When only a few individuals handle the testing process, you miss out on valuable insights and innovative ideas that could significantly impact your results. Collaboration across departments brings in fresh perspectives and diverse experiences, which can lead to more effective and creative testing strategies.
Another reason why it’s beneficial to keep other teams in the loop is because your test might impact different areas of marketing or down-funnel activities. For example, you might soft launch a feature that other teams weren’t aware was available to customers, leading to possible confusion.
Involve team members from different departments in the A/B testing process. Here’s how to do it effectively:
Recommended resource: Paid media experiment brief—use this template to plan, build, and optimize your experiments so you can run more experiments, more efficiently.
Now let’s dive into the common mistakes that happen during the testing process and how to avoid them like a pro.
It’s tempting to think that a stunning design will naturally lead to higher conversions. But a beautifully designed page won’t always take the cake. Design is important, but only if it supports the real star of your page: the copy. A visually appealing page might not necessarily resonate with your audience or drive them to take action.
Start with strong, persuasive copy and then create a design that complements it. Always prioritize functionality and clarity over aesthetics. Test different design elements to see which ones truly enhance user experience and drive conversions.
Testimonials can be powerful, but they aren’t a guaranteed win. It’s a common mistake to assume that adding testimonials will always boost conversions without testing them. Even elements as trusted as testimonials need to be tested to ensure they’re effective for your specific audience and context.
Approach testimonials with the same scrutiny as any other content. Test different formats, placements, and styles to find out what resonates best with your audience.
In the pursuit of higher conversions, it’s easy to lose sight of your brand’s unique voice and personality. Over-optimizing for conversion can sometimes dilute what’s special about your brand, leading to a disconnect with your loyal customers.
Maintain a balance between optimizing for conversions and preserving your brand’s voice. Use A/B testing to find the sweet spot where your brand’s personality shines through while also driving conversions.
We get it—waiting is hard. But cutting your test short is like leaving a cake half-baked. Without enough time, your results won’t reach statistical significance, and you’re just gambling with your data.
Stay disciplined. Don’t stop your test before hitting the 95% significance mark. Let your A/B testing tool declare a winner, or better yet, wait until you’ve reached your pre-calculated minimum sample size. Patience is a virtue, especially in A/B testing.
Some A/B testing tools can slow your site by up to a second. It may not sound like much but it’s actually a big deal since, according to Google, 53% of users abandon mobile sites that take more than three seconds to load. If your site slows down, your conversion rate will likely drop, skewing your test results.
Run an A/A test first—test your tool without any changes to see if it impacts your site’s performance. This will help you identify any performance issues before your real test starts.
Tools with server-side loading (just like our own A/B testing tool) can help avoid delays and flickers, so you’ll get the cleanest, most accurate results.
Simplicity is key. Running multiple tests simultaneously can muddle your results. While it’s okay to test different versions of a single element, running too many tests at once demands a larger sample size and complicates your analysis.
Limit yourself to just the number of tests that you and your team can handle without getting overwhelmed, and focus on significant elements like your CTA button or headline. By keeping things streamlined, you’ll gather clearer, more actionable insights.
Traffic fluctuates, and comparing results from different periods can mislead you. Comparing a high-traffic Wednesday to a low-traffic Tuesday is like comparing apples to oranges. Seasonal events or external factors can further distort your results.
Run your tests over similar and comparable time periods to get consistent data. For instance, if you’re an ecommerce retailer, don’t compare holiday season traffic with post-holiday slumps—instead, try comparing similar holiday seasons across different years. Consistency is crucial for reliable insights.
Tweaking your test mid-way is the quickest route to invalid results. Whether it’s adjusting traffic allocation or altering variables, mid-test changes can skew your data and lead to false conclusions.
Set your parameters and stick to them. If you absolutely need to make changes, start a new test. Consistency ensures that your results are valid and actionable.
You’ve run your A/B test, collected your data, and declared a winner. But hold your confetti, because the end of the test doesn’t mean the end of your work. There are several common mistakes that can mess with your results after the test is over, if you don’t know how to avoid them.
Between waiting for statistical significance and making incremental changes, A/B tests demand high maintenance. That’s why thorough documentation is crucial to squeeze every drop of learning from your experiments. Without proper records, you miss out on valuable insights, waste resources, and lack direction for future tests.
Create a template for documenting internal A/B tests and ensure everyone sticks to it. Your documentation should include:
Recommended resource: Paid media experiment brief template
It’s easy to shrug off a failed hypothesis and move on, especially if you’ve been waiting weeks for the results. But giving up too soon means you’re not fully digesting your learnings.
If your hypothesis was grounded in data but the test didn’t achieve the desired result, tweak your approach and try again. Here are your options:
Convincing A/B test results can be persuasive, but overestimating their implications can lead to trouble. For example, if adding a sign-up pop-up increases your mailing list on one page, it doesn’t mean you should plaster pop-ups everywhere. Overdoing it might annoy users and increase your bounce rate.
Go slow and steady with your changes. Remember, an A/B test answers a specific question. Implement changes gradually and monitor their impact carefully before rolling them out site-wide.
Accurate measurement is as crucial as accurate testing. If you don’t measure results properly, your data becomes unreliable, making it impossible to make informed decisions.
Ensure your A/B testing solution integrates with Google Analytics for better control and insights. This way, you can track your test results accurately and gain actionable insights.
It’s tempting to copy what worked for others, but what works for one company might not work for yours. Every business is unique, and blindly following case studies can lead you astray.
Use case studies as a reference point to generate ideas, but develop your own A/B testing strategy tailored to your audience. This approach ensures that your tests are relevant and effective for your specific needs.
Recommended reading: How a three-word A/B test led to triple-digit conversion growth
A 2% or 5% increase in conversion might seem insignificant, but small gains add up over time. Ignoring them is one of the biggest A/B testing mistakes you can make.
Embrace small wins. Look at them from a 12-month perspective—even a small percentage of steady, continuous growth can lead to huge returns over a full year.
Without a clear plan, A/B tests can become a random guessing game. It’s challenging to draw significant conclusions without a strategic approach, leading to wasted resources and fleeting wins.
Even with a decent sample size, confidence level, and test duration, your test results can be invalid due to several threats like the instrumentation effect (when a flawed instrument skews the data), the selection effect (when you incorrectly assume that a small portion of the traffic represents all of the traffic), and the broken code effect (when the page didn’t display properly on certain devices or browsers).
A winning variation for one segment might not work for another. It’s crucial to segment your audience and understand different user behaviors.
Like we mentioned before, it’s crucial to segment your users by demographics, behavior, and source when analyzing data. This approach helps you understand how different groups interact with your changes and ensures you’re optimizing for the right audience.
Changes that improve one metric might negatively impact another. It’s essential to consider the overall effect on your site’s performance.
Monitor downstream impacts carefully. Ensure that improvements in one area don’t lead to declines in another. This holistic approach helps maintain a balanced and effective optimization strategy.
Innovator and inventor Thomas Edison once said, “I have not failed. I’ve just found 10,000 ways that won’t work.” The same principle applies to A/B testing results. It’s not about “pass” or “fail”—when you run testing experiments, you’ll get results that are either impactful or inconclusive, but don’t just throw the non-impactful ones away.
Look at your inconclusive results through a different lens: They show you what not to do. These results will reveal the factors that don’t have a strong impact on your conversion, so you can instead focus on what matters.
A/B testing is a powerful tool in your conversion optimization arsenal, but it’s not foolproof. From planning your hypotheses to analyzing your results, every step of the testing process requires careful attention to avoid common pitfalls. By steering clear of these mistakes—whether they occur before, during, or after your tests—and following A/B testing best practices, you can ensure your experiments yield meaningful, actionable insights.
Ready to start your own A/B testing journey (with fewer mistakes along the way)? Check out our A/B testing tool, which is built into the Unbounce builder. It’s super easy to build your own pages, test them, and analyze the results—optimization and higher conversion rates are just a few clicks away.
March 20, 2024 . 7 minute read
June 13, 2024 . 12 minute read
May 24, 2024 . 13 minute read
June 27, 2024 . 8 minute read
April 30, 2024 . 17 minute read
March 18, 2024 . 28 minute read
March 19, 2024 . 23 minute read
March 16, 2024 . 29 minute read
Get actionable insights, expert advice, and practical tips that can help you create high-converting landing pages, improve your PPC campaigns, and grow your business online.
By Paul Park and Vincent Barr
.
Updated on July 4, 2024
.
14 min read
It has been referred to as “the biggest marketing blunder of all time.”
In 1985 the Coca-Cola Company introduced New Coke, a modified formula created to combat the growing popularity of its chief rival, Pepsi. Coke gambled that this new formula would be loved even more than the original flavor.
They couldn’t have been more wrong. After being swamped by a tidal wave of negative responses from the public, Coke brought back the original formula as “Coca-Cola Classic” and eventually abandoned New Coke entirely.
Some mistakes are huge (maybe do a pulse check before fundamentally changing your core product, eh, Coke?), while others are small and easily fixed. However, there’s one thing all mistakes have in common: They contain valuable lessons that can lead to brighter outcomes—as long as you reflect and learn from them.
In this post we’re covering the most common A/B testing mistakes, as well as how to avoid them and get the results—and marketing success—you’re aiming for.
Paul is a writer on Unbounce’s content team who lives and breathes storytelling. (It’s like oxygen but with better plotlines!) Ask him what he’s up to at any given moment and you’ll get answers ranging from folding paper dragons (y’know, origami) to catching up on the latest cool tech, and finding other ways to channel his inner geek.
» More blog posts by Paul Park
Vincent Barr is curious. He performs online marketing, web analytics, and split testing at MongoDB. He teaches digital marketing at General Assemb.ly. If you’re curious, you can Visit VincentBarr.com and join his mailing list.
» More blog posts by Vincent Barr
A/B testing might sound like a straightforward game of comparing A to B, but if you’re not careful, you could end up with a big ol’ mess of useless data. Here’s the lowdown on some rookie mistakes that can happen before you even launch your test.
Ah, the gut feeling. It’s as tempting as that third cup of coffee, but just as risky. Starting an A/B test solely on a hunch or guess is very likely to lead to untrustworthy results and a heaping pile of disappointment.
To nail useful A/B testing results you need a rock-solid A/B test hypothesis, which is a clear, testable statement that predicts how changes to a landing page or element will impact user behavior. Here’s how to create a good hypothesis:
And always ask yourself the golden questions:
Recommended reading: How to formulate a smart A/B test hypothesis (and why they’re crucial)
One of the most common pre-testing blunders is failing to segment different populations in your A/B test. It’s tempting to lean on the overall conversion rate (CVR) as your go-to metric because it’s straightforward. But this shortcut can lead to skewed results and misguided decisions, leaving you with a pile of unqualified leads and wasted resources.
When you focus on the overall CVR, you ignore the nuances of your diverse audience. Different segments—new visitors, returning users, mobile users, and so forth—each have unique behaviors and preferences.
Without segmenting your traffic, you might end up optimizing for visitors who aren’t your primary target, boosting conversion rates among less valuable segments while missing out on potential gains from your target audience. This misalignment means your optimizations won’t drive the impactful results you’re aiming for.
After you’ve taken those steps, get even better results with our AI-powered optimization tool Smart Traffic. It can automate and deepen personalization by considering variables like device type, location, and more. It dynamically routes visitors to the page variant most likely to convert, taking the guesswork out of audience segmentation.
By focusing on specific audience segments, you’ll gather stronger insights that lead to more effective optimizations. Plus, with Smart Traffic, you can sit back and let the automation work its magic, ensuring each visitor gets a personalized experience that drives conversions.
Sure, your “About Us” page is awesome, filled with delightful designs and fun, informative copy. But is it worth your time running an A/B test on? If it isn’t directly driving any conversions, then the answer is “no.” You’re plenty busy already, so it’s best to spend your time and effort on pages that will actually make some magic happen.
By aligning your A/B tests with the customer journey, you’ll focus your efforts on the most impactful areas, driving meaningful improvements in your conversion rates.
If your page is a ghost town, your A/B test results will be spookily unreliable. Without significant traffic, you’re not going to hit that sweet 95% statistical significance mark, meaning your results are about as trustworthy as a fortune cookie prediction. (The 95% statistical significance is an industry standard, and it means that if you ran the experiment 20 times, you would likely get the same results 19 times.)
Check your traffic and conversions using a sample size calculator (we’ve got a pretty good one). If you’re running low on visitors, remember: A/B testing isn’t the only trick in your CRO (conversion rate optimization) toolbox. Try using surveys or heatmaps instead, or do holdout experiments where a small group isn’t exposed to changes, helping you spot long-term effects.
Standard A/B testing operates on the assumption that users don’t influence each other, but this isn’t always true in the online world. Users interact, share experiences, and even sway each other’s decisions.
These interactions can mess with your results, leaving you scratching your head over misleading data. Imagine running a test where Group A sees a new feature and Group B doesn’t. If Group A users rave about it on social media or through word of mouth, Group B users might get influenced, skewing your test results.
Ignoring these interactions can lead to inaccurate conclusions and flawed optimization strategies. If you think your test results are solely based on isolated user behavior, you might miss out on understanding how social influence and network effects impact your data.
To get a clearer picture of user behavior, use network A/B testing to account for group interactions or avoid them altogether. Here’s how you can do it:
By accounting for these interactions, you’ll gain a more accurate understanding of user behavior, leading to better, more reliable optimization decisions.
One of the most overlooked aspects of A/B testing is failing to involve your colleagues from different departments. When only a few individuals handle the testing process, you miss out on valuable insights and innovative ideas that could significantly impact your results. Collaboration across departments brings in fresh perspectives and diverse experiences, which can lead to more effective and creative testing strategies.
Another reason why it’s beneficial to keep other teams in the loop is because your test might impact different areas of marketing or down-funnel activities. For example, you might soft launch a feature that other teams weren’t aware was available to customers, leading to possible confusion.
Involve team members from different departments in the A/B testing process. Here’s how to do it effectively:
Recommended resource: Paid media experiment brief—use this template to plan, build, and optimize your experiments so you can run more experiments, more efficiently.
Now let’s dive into the common mistakes that happen during the testing process and how to avoid them like a pro.
It’s tempting to think that a stunning design will naturally lead to higher conversions. But a beautifully designed page won’t always take the cake. Design is important, but only if it supports the real star of your page: the copy. A visually appealing page might not necessarily resonate with your audience or drive them to take action.
Start with strong, persuasive copy and then create a design that complements it. Always prioritize functionality and clarity over aesthetics. Test different design elements to see which ones truly enhance user experience and drive conversions.
Testimonials can be powerful, but they aren’t a guaranteed win. It’s a common mistake to assume that adding testimonials will always boost conversions without testing them. Even elements as trusted as testimonials need to be tested to ensure they’re effective for your specific audience and context.
Approach testimonials with the same scrutiny as any other content. Test different formats, placements, and styles to find out what resonates best with your audience.
In the pursuit of higher conversions, it’s easy to lose sight of your brand’s unique voice and personality. Over-optimizing for conversion can sometimes dilute what’s special about your brand, leading to a disconnect with your loyal customers.
Maintain a balance between optimizing for conversions and preserving your brand’s voice. Use A/B testing to find the sweet spot where your brand’s personality shines through while also driving conversions.
We get it—waiting is hard. But cutting your test short is like leaving a cake half-baked. Without enough time, your results won’t reach statistical significance, and you’re just gambling with your data.
Stay disciplined. Don’t stop your test before hitting the 95% significance mark. Let your A/B testing tool declare a winner, or better yet, wait until you’ve reached your pre-calculated minimum sample size. Patience is a virtue, especially in A/B testing.
Some A/B testing tools can slow your site by up to a second. It may not sound like much but it’s actually a big deal since, according to Google, 53% of users abandon mobile sites that take more than three seconds to load. If your site slows down, your conversion rate will likely drop, skewing your test results.
Run an A/A test first—test your tool without any changes to see if it impacts your site’s performance. This will help you identify any performance issues before your real test starts.
Tools with server-side loading (just like our own A/B testing tool) can help avoid delays and flickers, so you’ll get the cleanest, most accurate results.
Simplicity is key. Running multiple tests simultaneously can muddle your results. While it’s okay to test different versions of a single element, running too many tests at once demands a larger sample size and complicates your analysis.
Limit yourself to just the number of tests that you and your team can handle without getting overwhelmed, and focus on significant elements like your CTA button or headline. By keeping things streamlined, you’ll gather clearer, more actionable insights.
Traffic fluctuates, and comparing results from different periods can mislead you. Comparing a high-traffic Wednesday to a low-traffic Tuesday is like comparing apples to oranges. Seasonal events or external factors can further distort your results.
Run your tests over similar and comparable time periods to get consistent data. For instance, if you’re an ecommerce retailer, don’t compare holiday season traffic with post-holiday slumps—instead, try comparing similar holiday seasons across different years. Consistency is crucial for reliable insights.
Tweaking your test mid-way is the quickest route to invalid results. Whether it’s adjusting traffic allocation or altering variables, mid-test changes can skew your data and lead to false conclusions.
Set your parameters and stick to them. If you absolutely need to make changes, start a new test. Consistency ensures that your results are valid and actionable.
You’ve run your A/B test, collected your data, and declared a winner. But hold your confetti, because the end of the test doesn’t mean the end of your work. There are several common mistakes that can mess with your results after the test is over, if you don’t know how to avoid them.
Between waiting for statistical significance and making incremental changes, A/B tests demand high maintenance. That’s why thorough documentation is crucial to squeeze every drop of learning from your experiments. Without proper records, you miss out on valuable insights, waste resources, and lack direction for future tests.
Create a template for documenting internal A/B tests and ensure everyone sticks to it. Your documentation should include:
Recommended resource: Paid media experiment brief template
It’s easy to shrug off a failed hypothesis and move on, especially if you’ve been waiting weeks for the results. But giving up too soon means you’re not fully digesting your learnings.
If your hypothesis was grounded in data but the test didn’t achieve the desired result, tweak your approach and try again. Here are your options:
Convincing A/B test results can be persuasive, but overestimating their implications can lead to trouble. For example, if adding a sign-up pop-up increases your mailing list on one page, it doesn’t mean you should plaster pop-ups everywhere. Overdoing it might annoy users and increase your bounce rate.
Go slow and steady with your changes. Remember, an A/B test answers a specific question. Implement changes gradually and monitor their impact carefully before rolling them out site-wide.
Accurate measurement is as crucial as accurate testing. If you don’t measure results properly, your data becomes unreliable, making it impossible to make informed decisions.
Ensure your A/B testing solution integrates with Google Analytics for better control and insights. This way, you can track your test results accurately and gain actionable insights.
It’s tempting to copy what worked for others, but what works for one company might not work for yours. Every business is unique, and blindly following case studies can lead you astray.
Use case studies as a reference point to generate ideas, but develop your own A/B testing strategy tailored to your audience. This approach ensures that your tests are relevant and effective for your specific needs.
Recommended reading: How a three-word A/B test led to triple-digit conversion growth
A 2% or 5% increase in conversion might seem insignificant, but small gains add up over time. Ignoring them is one of the biggest A/B testing mistakes you can make.
Embrace small wins. Look at them from a 12-month perspective—even a small percentage of steady, continuous growth can lead to huge returns over a full year.
Without a clear plan, A/B tests can become a random guessing game. It’s challenging to draw significant conclusions without a strategic approach, leading to wasted resources and fleeting wins.
Even with a decent sample size, confidence level, and test duration, your test results can be invalid due to several threats like the instrumentation effect (when a flawed instrument skews the data), the selection effect (when you incorrectly assume that a small portion of the traffic represents all of the traffic), and the broken code effect (when the page didn’t display properly on certain devices or browsers).
A winning variation for one segment might not work for another. It’s crucial to segment your audience and understand different user behaviors.
Like we mentioned before, it’s crucial to segment your users by demographics, behavior, and source when analyzing data. This approach helps you understand how different groups interact with your changes and ensures you’re optimizing for the right audience.
Changes that improve one metric might negatively impact another. It’s essential to consider the overall effect on your site’s performance.
Monitor downstream impacts carefully. Ensure that improvements in one area don’t lead to declines in another. This holistic approach helps maintain a balanced and effective optimization strategy.
Innovator and inventor Thomas Edison once said, “I have not failed. I’ve just found 10,000 ways that won’t work.” The same principle applies to A/B testing results. It’s not about “pass” or “fail”—when you run testing experiments, you’ll get results that are either impactful or inconclusive, but don’t just throw the non-impactful ones away.
Look at your inconclusive results through a different lens: They show you what not to do. These results will reveal the factors that don’t have a strong impact on your conversion, so you can instead focus on what matters.
A/B testing is a powerful tool in your conversion optimization arsenal, but it’s not foolproof. From planning your hypotheses to analyzing your results, every step of the testing process requires careful attention to avoid common pitfalls. By steering clear of these mistakes—whether they occur before, during, or after your tests—and following A/B testing best practices, you can ensure your experiments yield meaningful, actionable insights.
Ready to start your own A/B testing journey (with fewer mistakes along the way)? Check out our A/B testing tool, which is built into the Unbounce builder. It’s super easy to build your own pages, test them, and analyze the results—optimization and higher conversion rates are just a few clicks away.
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