A Simplified and Numerically Stable Approach to the BG/NBD Churn Prediction model
This provides a more practical and robust churn prediction method for industries with irregular purchase patterns, but it is incremental as it builds on an existing model.
The study tackled the limitations of the BG/NBD churn prediction model in industries with seasonal events and high purchase counts by redefining churn as no purchases within M days, resulting in a simplified equation and a numerically stable alternative expression to prevent overflow or underflow issues.
This study extends the BG/NBD churn probability model, addressing its limitations in industries where customer behaviour is often influenced by seasonal events and possibly high purchase counts. We propose a modified definition of churn, considering a customer to have churned if they make no purchases within M days. Our contribution is twofold: First, we simplify the general equation for the specific case of zero purchases within M days. Second, we derive an alternative expression using numerical techniques to mitigate numerical overflow or underflow issues. This approach provides a more practical and robust method for predicting customer churn in industries with irregular purchase patterns.