Customer Churn Analysis Based on the Data-mining Approach: Hybrid Algorithm ‎Incorporates Decision Tree and Bayesian Network

Today, companies and organizations are aware of the fact that customer retention leads to greater profitability. Increasing competition causes the rate of customer churn to grow. Therefore, studying the features influencing the tendency of customer churn is important. In the present study, a hybrid model based on the data mining approach is presented to analyze the features of churn customers. In the first step, the feature selection node has been used to identify the features with higher importance and remove redundant items. Then, the C5.0 Decision Tree and Bayesian network were used to classify the customers into two groups, turning customers, and non-turning customers. These are data mining techniques and terms that can help in forecasting. Finally, the proposed model has been implemented in the chain store industry as a case study. Key findings indicate that both the decision tree model and the Bayesian network can predict churn customers with different accuracies, the area under the receiver operating curve in the decision tree model is greater than the Bayesian network model and it has better performance. The results indicate the optimal efficiency of the proposed method. In addition, the results show that three features of gender, marital status, and age from the set of demographic characteristics and five factors of average monthly income level, number of purchases per month, the share of online shopping, how to get to know the store, type of market-related to customer transaction records are among the most effective factors.