A/B testing (also called split testing) is an important part of conversion optimization. While it can be tempting to trust your intuition when it comes to creating landing pages, email copies, call-to-action banners, if you simply make decisions based on “feelings”, you might be losing a lot of conversions that you might otherwise be able to get.
By running A/B tests, you can test out your hypotheses and use real data to guide your actions. This template helps you plan for an experiment in a more structured way. This ensures an experiment is well thought out and it also helps you tos communicate it more effectively with designers, developers and others who will be involved in implementing the test.
The key part of a A/B test is formulating your hypothesis as this basically guides the whole A/B test plan.
In formulating the hypothesis, first you need to define the problem you want to solve. For example, you are an SaaS that offers free trial and you want to improve the traffic-to-lead conversion ratio (i.e. attracting more website visitors to actually sign up for a free trial). But that problem might be too broad to form an A/B test as you can simply test one variable in an A/B test to be effective (otherwise you won’t know which variable is causing the change).
So to narrow down the problem you want to solve, you need to find out the bottle-neck in the conversion funnel – where do people drop off the most? Are there any key information or call-to-action buttons that you expect people to read/click but they didn’t? You can use heatmaps and session recording tools like Hotjar and Fullstory to help you identify bottlenecks more easily.
After narrowing down the problem you want to solve, you then need to make a hypothesis as in what causes those bottlenecks and what you can do to improve.
For example, you noticed most of the visitors will visit your “Features” page but very few of them will actually scroll past even half of the page so many features that you think are important are not actually viewed by the visitors. To improve this, one hypothesis might be using tab or toggle list design to make your page shorter and visitors can select to dig deeper into content that they are interested in by expanding the content.
Remember when formulating your hypothesis, change only one variable so that you will know it’s really that variable that is causing the change in conversion.
Now you have your hypothesis, the next is to plan how you are going to measure your results. Defining your success metrics carefully beforehand is important. Otherwise, if there is not enough tracking done during the experiment, it might be hard to draw conclusions and next steps at the end of the experiment.
To communicate clearly to the implementation team, detail out the experiment setup that you will use to test your hypothesis. This include
In this section, describe what variations you would like to test.
Layout the design work related and add diagrams, mockups and designs related to the confirmed variation that you’d like to test. Gathering all these in one place helps your development team understand the context much better.
So at the end of the planned experiment period, you get all the stats but does a better conversion rate for one variation really conclude that variation is really better? You need to run a test of statistical significance to see whether your results are really statistically significant. You can use this A/B Testing Calculator by Neil Patel to check the results easily by inputting the sample size and conversion numbers of the variations.
If one variation is statistically better than the other, then you have the winner and can then complete the test by disabling the losing variation.
But if neither variation is statistically better or the original version is still better, then you might have to run another test.
Document any learnings you got from this experiment so that it can help you better plan your future ones.
From the results and learnings section, list out the action items that you would need to do after the experiment. Is that you would need to disable the losing variation? Is there more elements on that page that you want to test to further improve conversion rate?
A/B testing is a continuous process. Hope this template can help guide you in executing better split tests.
Most A/B tests should run for at least 1-2 weeks to account for day-of-week variations in user behavior, but the duration depends on your traffic volume and conversion rates. You need enough data to reach statistical significance - typically at least 100-300 conversions per variation. Low-traffic sites may need to run tests for 4-8 weeks, while high-traffic sites might get results in days. Never stop a test early just because one variation appears to be winning.
You need at least 100-300 conversions per variation to detect meaningful differences, but this varies based on your baseline conversion rate and the effect size you want to detect. For detecting a 20% improvement with 95% confidence, you typically need 1,000-5,000 visitors per variation. Use a sample size calculator before starting your test to determine how long you'll need to run it based on your traffic.
Generally, you should test only one element per A/B test to clearly identify what's causing any changes in performance. Testing multiple elements simultaneously (called multivariate testing) requires significantly more traffic and can make results harder to interpret. If you want to test multiple elements, run sequential A/B tests or use a multivariate testing approach only if you have very high traffic volumes.
Any statistically significant improvement is worth implementing, but focus on tests that can detect at least a 10-20% relative improvement in your key metrics. Smaller improvements might not be practically significant given the effort required to implement and maintain changes. Consider the lifetime value impact - even a 5% improvement in conversion rate could be worth thousands of dollars annually.
Prioritize tests based on potential impact and ease of implementation. Start with high-traffic pages and elements that directly affect conversions, such as headlines, call-to-action buttons, and form fields. Use analytics data, heatmaps, and user feedback to identify the biggest bottlenecks in your conversion funnel. Test the most impactful elements first before moving to smaller optimizations.
Inconclusive results (no statistical significance) are common and valuable learning opportunities. This might mean your hypothesis was wrong, the change wasn't significant enough to detect, or you need more data. Document what you learned, consider testing a more dramatic variation, or move on to test a different element. Not every test will be a winner, and that's normal in optimization.
Yes, segmenting results can provide valuable insights, but plan this analysis before starting the test. Common segments include traffic source, device type, new vs. returning visitors, and geographic location. However, remember that segmenting reduces your sample size for each group, so you may not achieve statistical significance for smaller segments. Focus on your most important user segments.
Seasonal variations can significantly impact test results, especially for e-commerce and B2B businesses. Avoid running tests during major holidays, sales events, or known seasonal peaks unless that's specifically what you want to test. If you must test during these periods, account for seasonal effects in your analysis and consider extending the test duration to capture normal behavior patterns.
Statistical significance means you can be confident the difference between variations isn't due to random chance (typically 95% confidence). Practical significance means the difference is large enough to matter for your business. A test might show statistical significance with only a 2% improvement, but if implementing the change costs more than the revenue gain, it's not practically significant.
Common mistakes include stopping tests too early, testing too many elements at once, not having enough traffic for reliable results, ignoring external factors that might affect results, and not documenting hypotheses clearly. Always plan your test completely before starting, including success metrics, sample size requirements, and analysis methods. Use proper statistical methods and don't let emotions or preferences influence when you stop a test.
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