Nowadays, the telecom industry faces fierce competition in satisfying its customers. With the advent of newer technology, the services offered by telecom companies have increased from being only calls to calls, data and web services. This means a constant struggle to strike a perfect balance among services and pricing of these services. In order to survive this market, telecom companies need to innovate, offer better services and increase its customer base. With newer companies entering the market and increasing freedom of customers to switch telecom companies, it’s now becoming increasingly important to focus resources in retaining existing customers. According to an article in Harvard Business Review (Gallo,2014), it was determined that the cost of acquiring a customer is five to twenty-five times more than retaining an existing one. Furthermore, increasing retention by five percent can lead to an increase in profits by twenty-five to ninety-five percent. This paper aims to segment customers and find the factors contributing to churn in each customer segment. Customer churn rate was defined as the percentage of customers who end their relationship with a company in a particular period. Additionally, this paper discusses a churn prediction model developed to identify those customers who are likely to churn. The records available for analysis is around 71 thousand records. For the analysis, SAS Enterprise Miner was used. Using the insights from the customer segmentation and prediction models, an action plan was developed for each segment.