Sequential Feature Selection in Customer Churn Prediction Based on Naive Bayes

Abstract

Abstract The customer churn prediction is an important business strategy for the company. The ease of switching operators is one of the serious challenges that must be faced by the telecommunications industry. To get new customers requires a much higher cost than maintaining existing customers. Customer churn refers to the periodic loss of customers in an organization. To retain existing customers, organizations must improve customer service, improve product quality, and must be able to know in advance which customers have the possibility of leaving the organization. By predicting customer churn, companies can immediately take action to retain customers. Prediction can be done by analysing customer data using data mining techniques. This study proposes to implement feature selection to select relevant features and can provide improved performance in customer churn prediction models. Some proposed feature selection techniques are Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), Sequential Forward Floating Selection (SFFS), Sequential Forward Floating Selection (SBFS), Sequential Backward Floating Selection (SBFS). The classification algorithm used to classify is Naive Bayes. The model that provides the best performance value is the model that implements Sequential Backward Selection (SBS) and Sequential Backward Floating Selection (SBFS) feature selection technique with feature number 19.