Data-driven A/B Testing: The Value-Added Ingredient

Why cast a wide net when you can use a laser to pin-point your A/B testing targets? While A/B Testing allows you to zoom in on a given page element and answer questions like which of several elements works best in a spot or which version of a page converts more, it generally can’t answer “why”. Why should I test this specific element as opposed to another? Why are users dropping off more frequently than before? Why do more customers convert from this image than from another?

Casting a wide net, and then serial testing may be helpful, but it’s sure not efficient. The challenge then is making A/B testing focused, faster and more informative by improving the selection of test targets, expediting the testing process, and raising the value of results by applying deep customer experience-based understanding.

Here’s where data-driven A/B testing steps in. Data-driven issue identification tests what matters by eliminating testing based on hunches. It can anticipate where the test is heading at early stages of testing, so you can stop tests that are unproductive. This speeds up testing cycles with quick, accurate discovery of unforeseen issues. It offers actionable conclusions and enhances buy-in.

Data-driven A/B testing focuses on data from actual customer behavior and in-page actions at every step of the testing process – before, during and after.

Before testing

The most effective test is a well-researched one, but it’s challenging to define exactly what to test, why it’s important, and how results can impact page performance. Before the test, heatmaps and form analytics help identify webpage elements that are most engaging, most unexpected, or most significantly affect customer behavior - and should therefore be tested. This Data-Driven A/B Testing approach helps concentrate testing on where it will have the greatest impact and assess hypotheses based on hard data.

  • Actionable aggregated insights: Visually examining aggregated heatmap data from in-page customer experience metrics can help you discover which page areas require improvement. For example, regarding on-site activity, how many visitors scroll down past the fold? In cross-segment and cross-version comparison, do men and women respond differently to the same page or page element? 



  • Individual insights: Understand actual customer intent by watching real customer journeys, for example, watch replays of user browsing sessions to visualize exactly what they’re seeing and doing on your website.

Form testing: Leverage advanced form analytics and session replays to focus testing on form-specific issues that can dramatically impact conversions.

During testing

During testing the Data Driven A/B Testing approach helps speed time to results and increase return on time and resources invested during the test planning stage. You can, for example, validate that test variations are working as intended by watching session replays immediately after the first visitors experience your tests for quick corrective action and quick and immediate results.

After testing

  • Prioritize changes: Data-Driven A/B Testing empowers testers to better understand why certain experiences won and others lost during testing and how to best prioritize implementation of necessary changes. For example, by comparing test variations side-by-side using heatmaps, you can see exactly which changes most successfully drove visitor interactions.



  • Get buy-in for change: By integrating customer intent with A/B Testing data, you can change the way your organization looks at web analytics. A/B Testing platforms show test result data and indicate which version of a given page or page element worked best, adding the ‘why’ to the ‘which’ by demonstrating the reasons one version performed better than another. This is what decision-makers and website stakeholders need to hear when evaluating findings and suggested changes.

  • Quantify change: The data-driven A/B testing approach enables testers to quantify the impact of changes. Intelligent use of user experience data enables accurate prediction of how revenues will be impacted when recommended changes are implemented.

Overall, Data-driven A/B Testing:

  • Focuses tests on what customers actually experience
  • Raises testing efficacy and relevance
  • Provides accessible and visually compelling validation of testing results
  • Enhances overall business impact
  • Helps move decision makers to action

For more information on how data-driven A/B testing can improve the speed, accuracy and ROI of your testing program, download our “Test Like a Hawk” e-book now.

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