Problem: A/B testing provides a lot of quantitative data, but may not reveal the reasons why your users behave the way they do.
Solution: get more from your A/B test results with qualitative insights.
Use an A/B testing tool with a rich reporting feature like Convert to analyze your data and compare KPIs like CTR, AOV, RPV, and ROI. Once your experiment is done, you can easily slice and dice your test data by identifying audience segments like new vs returning users, browsers and devices used, campaigns clicked, and resident countries.
Next, use a tool like Hotjar’s Incoming Feedback widget to combine those metrics with qualitative feedback, so you can connect the dots and understand why your users behave the way they do.
Ask users about their experience with the winning variation: what did they like about it? What would they want to improve? This removes the guesswork, helps you better understand why one variation outperformed another, and will help with further iterations of your product.
Keep in mind that on your A/B testing journey, you’ll see both favorable and less favorable results, which applies to both successful and failed tests:
- Successful tests: say more than one of your tests gets statistically significant positive results, and you decide to deploy them. What now? Interpreting test results after they conclude is crucial to understanding why the test succeeded. A fundamental question to be asked is why? Why did users behave the way they did? Why did they react a certain way with one version and not with the others? What user insights did you gather, and how can you use them?
- Failed tests: most people have a hard time dealing with failure. But just because a test failed doesn’t mean you should ignore it—in fact, it’s the exact opposite. Study the data gathered during the test to understand what made this particular A/B test fail and how you can avoid that during future ones. No failed test is unsuccessful unless you fail to learn from it.
Benefit: poor interpretation of results can lead to bad decisions and negatively affect other developments you integrate this data into. Combining quantitative data with qualitative feedback lets you get more out of your A/B tests and build a better product.