Retention Science has developed processes to evaluate the accuracy of Customer Churn Prediction and Customer Future value models, which are two important predictive metrics. The processes developed by Retention Science help to identify the best performing models as well as to determine the accuracy of the models. Retention Science has also developed email alerts for notification when any of the model strays from it mark.
- Retention Science’s process of evaluating the accuracy of two crucial predictive models, Customer Churn Prediction and Customer Future Value.
- Churn probabilities–the likelihood that a potential customer will shop–are divided into low, medium, and high churn groups and then those groups are analyzed and compared to each other.
- Similar to churn probabilities, customer future value–the value of a given customer to a company–is divided into low, medium, and high value groups for analysis.
“We define customer lifetime value (CLV) as sum of the past value (observed component) and the future value (predictive component) of a user.”