CUSTOMER STRESS PREDICTION IN TELECOM INDUSTRY

Retaining the most valuable customers is a major problem companies face in this information age. Especially, the field of telecommunication faces complex challenges due to a number of vibrant competitive service providers. Therefore, it has become very difficult for them to retain existing customers. Since the cost of acquiring new customers is much higher than the cost of retaining the existing customers, it is the time for the telecom industries to take necessary steps to retain the customers to stabilize their market value. CRM uses data mining (one of the elements of CRM) techniques to interact with customers. This study investigates the use of a technique, supervised learning, for the management and analysis of customer-related data warehouse and information. Data mining technologies extract hidden information and knowledge from large data stored in databases or data warehouses, which supports the corporate in decision making process. Several data mining techniques have been proposed in the literature for predicting the happy and stressed customer using heterogeneous customer records. Probably, the stressed customers are in the urge of moving out to competitive service providers. This project analysis the telecom customer data available in open dataset and predict the customer stress by applying supervised machine-learning algorithms mainly using Deep Neural Network , K Nearest Neighbour , Support Vector Machine and Random Forest .