AB Testing for a "Recommended Products" Feature. One of the common strategy in CRO optimization.

Let's create a hypothetical scenario where you are conducting A/B testing for a new feature on a supermarket chain's website. In this scenario, imagine the supermarket chain wants to test a new feature: a "Recommended Products" section based on user purchase history.

Step 1: Setting Objectives for your AB testing

• Objective: To determine if the "Recommended Products" section increases the average order value and customer engagement (measured by click-through rate on recommended items).

Step 2: Creating Hypotheses for the AB test

• Primary Hypothesis: Displaying personalized product recommendations will increase the average order value as it encourages customers to add more items to their cart.

• Secondary Hypothesis: The "Recommended Products" section will have a higher click-through rate compared to other non-personalized sections of the site.

Step 3: Segmenting the Audience for your AB text

• Audience Selection: Divide your website visitors into two groups. Group A (the control group) will see the current website without the "Recommended Products" section. Group B (the test group) will see the new feature.

• Randomization: Ensure that visitors are randomly assigned to each group to eliminate selection bias.

Step 4: Executing the AB test

• Implementation: Integrate the "Recommended Products" section for Group B. Ensure that the rest of the website experience remains consistent between both groups.

• Duration: Run the test for a sufficient duration to gather meaningful data, typically a few weeks or until you reach statistical significance.

Step 5: Analyzing Results of your AB text

• Data Collection: Gather data on average order value, click-through rates on recommended products, and overall user engagement.

• Statistical Analysis: Use appropriate statistical methods to determine if the differences between Group A and Group B are significant.

• Learning from the Test: Regardless of the outcome, analyze why the feature performed the way it did. Did it meet the objectives? Were there any unexpected user behaviors?

Step 6: Making Data-Driven Decisions with AB test is the foundation for CRO optimization

• Positive Outcome: If the test is successful (i.e., statistically significant improvement in average order value and click-through rates), consider implementing the feature across the site.

• Negative or Inconclusive Outcome: If the results are negative or inconclusive, analyze why. Was the algorithm not effective enough in predicting user preferences? Was the section not prominently displayed? Use these insights to refine the feature or test different hypotheses.