CHURN PREDICTION AND MANAGEMENT SYSTEM

摘要

A system and method for managing churn among the customers of a business is provided. The system and method provide for an analysis of the causes of customer churn and identifies customers who are most likely to churn in the future. Identifying likely churners allows appropriate steps to be taken to prevent customers who are likely to churn from actually churning. The system included a dedicated data mart, a population architecture, a data manipulation module, a data mining tool and an end user access module for accessing results and preparing preconfigured reports. The method includes adopting an appropriate definition of churn, analyzing historical customer to identify significant trends and variables, preparing data for data mining, training a prediction model, verifying the results, deploying the model, defining retention targets, and identifying the most responsive targets.

主权项

1. A method of designing an efficient customer retention program for managing customer churn among customers of a business having a statistically large customer base, the customer retention program including an analysis of the causes of customer churn and identifying customers who are most likely to churn in the future, so that appropriate steps may be taken to prevent customers who are likely to churn in the future from churning, the method comprising:
adopting a definition of churn sufficient to encompass all customers in the customer base and which relies on objective factors to determine whether individual customers have churned or remain active; analyzing historical customer data to identify significant trends and variables that provide insight into causes of churn and to identify classes of customers who are more likely to churn than others; preparing customer data, including data corresponding to the identified trends and variables, for data mining and predictive modeling; training at least one predictive model on historical customer data; verifying the accuracy of the at least one predictive model based on historical data; deploying the at least one trained model on current customer data to generate a propensity to churn score for individual customers indicating the relative likelihood that the individual customer will churn within a specified time period in the future; defining characteristics of the target customers to be contacted during the course of the customer retention program; and compiling a list of targeted customers having the defined characteristics.