A Survey on Customer Churn Prediction using Machine Learning Techniques
نویسنده
چکیده
The fast expansion of the market in every sector is leading to superior subscriber base for service providers. Added competitors, novel and innovative business models and enhanced services are increasing the cost of customer acquisition. In such a fast set up, service providers have realized the importance of retaining the on-hand customers. It is therefore essential for the service providers to prevent churna phenomenon which states that customer wishes to quit the service of the company. This paper reviews the most popular machine learning algorithms used by researchers for churn predicting, not only in banking sector but also other sectors which highly depends on customer participation.
منابع مشابه
Customers Churn Prediction and Attribute Selection in Telecom Industry Using Kernelized Extreme Learning Machine and Bat Algorithms
With the fast development of digital systems and concomitant information technologies, there is certainly an incipient spirit in the extensive overall economy to put together digital Customer Relationship Management (CRM) systems. This slanting is further more palpable in the telecommunications industry, in which businesses turn out to be increasingly digitalized. Customer churn prediction is a...
متن کاملAnalysis of Customer Churn prediction in Logistic Industry using Machine Learning
Customer churn prediction in logistics industry is one of the most prominent research topics in recent years. It consists of detecting customers who are likely to cancel a subscription to a service. Recently, logistics market has changed from a rapidly growing market into a state of saturation and fierce competition. The focus of the logistic companies has therefore shifted from building a larg...
متن کاملHigh Accuracy Predictive Modelling for Customer Churn Prediction in Telecom Industry
Churn prediction is an important factor to consider for Customer Relationship Management (CRM). In this study, statistical and data mining techniques were used for churn prediction. We use linear (logistic regression) and non-linear techniques of Random Forest and Deep Learning architectures including Deep Neural Network, Deep Belief Networks and Recurrent Neural Networks for prediction. This i...
متن کاملHierarchical Alpha-cut Fuzzy C-means, Fuzzy ARTMAP and Cox Regression Model for Customer Churn Prediction
As customers are the main asset of any organization, customer churn management is becoming a major task for organizations to retain their valuable customers. In the previous studies, the applicability and efficiency of hierarchical data mining techniques for churn prediction by combining two or more techniques have been proved to provide better performances than many single techniques over a nu...
متن کاملA Fuzzy Rule-Based Learning Algorithm for Customer Churn Prediction
Customer churn has emerged as a critical issue for Customer Relationship Management and customer retention in the telecommunications industry, thus churn prediction is necessary and valuable to retain the customers and reduce the losses. Recently rule-based classification methods designed transparently interpreting the classification results are preferable in customer churn prediction. However ...
متن کامل