How to Interpret and Communicate A-B Test Results

Interpreting and communicating the results of an A/B test is a critical step in the experimentation process. In this section, we'll explore how to draw conclusions from your test results, avoid common pitfalls, and effectively communicate findings to different stakeholders.

Interpreting Results

Example 1: Highlighted Form Field Performing Better

Let’s say you ran an A/B test where a variation with a more prominent blue form field background performed better than the original. You decide to apply this across your entire user base. What conclusions can you draw from this test?

A possible conclusion might be: "Forms with a highlighted background are likely to perform better, so we should standardize this design across all forms."

Is this conclusion valid? Yes, but with caution. You’re making a probabilistic statement, not an absolute one. The key here is to understand that, based on this test, there is a high probability that highlighting form fields will yield better results, but it's not guaranteed in every context. This is a reasonable assumption, and using test insights to inform broader design decisions is a valid approach.

Example 2: Testing Button Placement for Color Selection

In another test, you altered the placement of the color selection buttons on a product page and observed improved performance. A follow-up assumption might be: "If the color selection button performs better, the same layout will work for size selection."

Is this conclusion valid? No. The user intent when choosing colors is different from when selecting sizes. When selecting a color, users are likely deciding on the visual aspect of the product, while selecting a size involves more technical decision-making (e.g., fit, measurements). Therefore, assuming similar performance for both elements might not be accurate, as they fulfill different user needs.

Example 3: Multivariable Test on Layout

Imagine you ran a multivariable A/B test comparing several layouts. Someone concludes: "The layout with the photo on the left and the form below will always perform better."

This conclusion is likely false because you didn’t test the individual elements separately. It’s possible that the combination of a photo on the left and the form below performed well in this specific test, but you can't isolate which element caused the improvement. Testing elements separately (isolated variables) would allow you to draw more definitive conclusions.

Communicating Results

The next step is to effectively communicate your findings to different audiences. Each group may require a different level of detail, so it’s essential to tailor your communication accordingly.

For Executives and Senior Stakeholders

Keep the presentation brief, focusing on high-level insights:

Results: What were the outcomes of the test? Which variation performed better?
Conclusion: What recommendation are you making based on the results? For example, "We recommend rolling out Variation A to the entire user base as it increased conversions by 10%."
Key Metrics: Highlight the main performance metrics (e.g., conversion rate, click-through rate) that led to your conclusion.
Visuals: Provide clear visual comparisons between variations to facilitate understanding.

For Your Team and Close Stakeholders

Provide a more detailed analysis, including:

Hypothesis: Clearly state the hypothesis being tested.
Objective and Results: Include all metrics (objective, auxiliary, and guardrail) and compare the results for each variation against the control group.
Conclusion: Explain how the results support your decision and include any trade-offs or anomalies observed during the test.
Detailed Data: Provide raw and percentage data for each metric to support your conclusions.

Example of a Presentation Template

In a presentation, you might include:

Context: "We tested the hypothesis that increasing the prominence of the form field would lead to a 10% increase in conversion."
Results Summary: "Variation A increased conversion by 10%, with no significant change in the guardrail metrics."
Recommendation: "Based on the results, we recommend rolling out Variation A across the site."
Visuals: Show the winning variation alongside key metrics (e.g., conversion rate and engagement rates).

Example of a Detailed Report Template

In your detailed documentation, include:

Experiment Name: A/B Test on Form Field Highlighting
Team: List all members involved.
Date: Test start and end dates.
Summary: Provide an overview of the test, including the hypothesis and key metrics.
Design: Describe the design of the test, including the control and variations, the user segments involved, and any configurations.
Results: Provide the full data, including raw values and percentage changes, for the objective, auxiliary, and guardrail metrics.
Conclusion: Clearly explain why the winning variation was selected, addressing any notable metrics and trade-offs observed. If necessary, discuss why the results may not warrant immediate rollout.

Conclusion

Effectively interpreting and communicating A/B test results involves making sound, probabilistic conclusions and sharing insights in a way that resonates with different audiences. By ensuring your results are backed by data and focusing on clear communication, you can ensure that your A/B testing efforts lead to meaningful improvements in product performance and decision-making.