Abstarct: Customer Churn Analysis baxsed on The Datamining Approach: Hybrid Algorithm Incorporates Support Vector Machine and Logistic Regression (case of study: chain stores) Abstract Customer churn is one of the challenges of business management in today's competitive environment. For this purpose, it is necessary for the organization to have an efficient system to detect and analyze the factors of churning or not churning customers during their lifetime. This is despite the fact that many different factors will influence this analysis, which will increase the complexity of the analysis process. In this research, a hybrid model baxsed on the data mining approach has been presented to analyze the factors of customer churn. In the first step, the logistic regression technique has been used to identify features with higher importance. In the second step, the support vector machine technique was used to classify the customers into two categories: churning customers and non-churning customers. Finally, the proposed model has been implemented in the chain stores industry as a case study. The results indicate the optimal efficiency of the proposed analysis method. Also, the results show that the three factors of age, marital status, and monthly salary from the set of demographic characteristics and four factors of the number of purchases per month, how to get to know the store, the amount of online shopping and Special time sale from the set of features related to customer transaction records are the most important factors affecting customer churn. In the end, the practical suggestions presented can be used for micro and macro planning of chain stores in the field of attracting and retaining customers.