Next Best Action - Model

How A/B Testing Trains Our Machine Learning Model to Recommend the Next Best Action

Introduction

A/B testing is a powerful method to measure the impact of specific actions on customer behavior & Segments. By systematically testing different approaches and analyzing the results, we can train our machine learning model to provide data-driven recommendations for the next best action (NBA). This allows businesses to move away from intuition-based decisions and instead rely on predictive insights to optimize customer engagement.

How A/B Testing Enhances Machine Learning Predictions

Machine learning models thrive on high-quality, structured data. A/B testing provides precisely this by offering a controlled environment where the impact of an action can be isolated and analyzed. When a business conducts an A/B test—such as sending an email to one group and withholding it from another—our model learns from the resulting behavioral patterns. Over time, the model refines its understanding of what actions lead to churn, engagement, or upsells, improving its ability to recommend optimal next steps for each customer.

The A/B Testing Process in Churned

Our platform simplifies the execution of A/B tests and ensures that insights gained from these experiments directly feed into our predictive models. Below is a step-by-step outline of the process:

1. Selection of Participants

   • Using Churned, we select the target customer segment for the A/B test. This can be based on churn risk, engagement level, or other relevant factors.

2. Random Split into Test Groups

   • The selected customers are randomly divided into two equal groups:

     • Group A (Control): No action is taken (e.g., no email sent).

     • Group B (Treatment): The intended action is executed (e.g., email about performance metrics sent).

3. Syncing the Groups with you system

   • The two groups are pushed to your system as separate lists (e.g., campaignX_groupA and campaignX_groupB).

   • Group B’s list is used to trigger the email campaign in your system.

4. Executing the A/B Test

   • The campaign is run, and interactions such as email opens, clicks, responses, and eventual customer actions (e.g., renewal or churn) are tracked.

5. Analyzing the Results in Churned

   • The impact of the action is measured using key metrics such as engagement rates and churn rates.

   • If customers who received the email showed a higher churn rate, it suggests the email content or timing might be suboptimal. Conversely, if engagement increased, the email strategy could be expanded.

6. Training the Machine Learning Model

   • The results of the A/B test are fed into our machine learning model, which learns how specific actions influence customer behavior.

   • Over time, as more A/B tests are conducted, the model becomes more precise in predicting customer responses to various actions.

7. Syncing Next Best Actions to your system

   • Based on the learned insights, next best actions (NBA) are generated by Churned.

   • These actions are synced to your system via lists or customer properties, enabling automated and personalized follow-ups.

Conclusion

A/B testing provides critical data that enhances our machine learning model’s ability to make actionable recommendations. By continuously testing different customer engagement strategies and analyzing the results, businesses can ensure that every customer interaction is optimized for maximum impact. This data-driven approach not only improves retention but also ensures that customer success strategies are always evolving based on real-world results.