How can you handle inconclusive A/B test results?

1 Check your data quality

The first step is to make sure that your data is accurate and reliable. You should verify that your tracking and measurement tools are working properly, that your sample size is large enough to detect meaningful differences, and that your test duration is long enough to account for seasonal and external factors. You should also check for any errors or biases in your data collection, such as selection, attrition, or novelty effects, that might skew the results.

2 Review your hypothesis and metrics

The second step is to review your hypothesis and metrics and see if they are relevant and aligned with your goal. You should ask yourself if your hypothesis is specific, measurable, achievable, realistic, and time-bound, and if your metrics are valid, reliable, and sensitive to the changes you are testing. You should also consider if you are measuring the right outcomes, such as user behavior, engagement, retention, or revenue, rather than superficial indicators, such as clicks, views, or downloads.

3 Explore your segments and subgroups

The third step is to explore your segments and subgroups and see if there are any patterns or trends that might reveal hidden insights. You should analyze your data by different dimensions, such as demographics, geographics, psychographics, or behavioral characteristics, and see if there are any significant differences between the groups. You should also look for any interactions or correlations between the variables that might affect the results. However, you should be careful not to overfit or cherry-pick the data and avoid drawing conclusions based on small or noisy samples.

4 Experiment with different variations

The fourth step is to experiment with different variations and see if you can find a better alternative or a combination of elements that works better than the original. You should try to optimize your design, copy, layout, or functionality and test different versions that might have a bigger impact on your goal. You should also use a multivariate testing approach, which allows you to test multiple variables at once and see how they interact with each other. However, you should be aware that this method requires more resources and time and might increase the complexity and uncertainty of the results.

5 Learn from your failures

The fifth step is to learn from your failures and see what you can improve for your next experiment. You should document your findings and insights and share them with your team and stakeholders. You should also identify the gaps and limitations in your knowledge and assumptions and formulate new questions and hypotheses. You should also evaluate your testing process and see if you can optimize your methods, tools, or practices and increase your efficiency and effectiveness.