Machine learning and its application to predict churn in the Telecommunications Industry
The global market for telecom services is expecte... [Show More] d to grow at an incredible rate as everyday life becomes more dependent on it. Due to the constant innovations and rapid pace of development, the global telecommunications market continues to change (Bryman, 1988). The loyalty of customers is key to the success of the telecommunications market. Telecom operators can maintain their value in a highly competitive market by building trust with customers and establishing a culture of trust that drives loyalty. In today's competitive market, it is important to retain existing customers and acquire new customers (Braun & Schweidel, 2011, pp. 902).There are many churn customers within the telecommunications industry. For predicting churn customers, a domain expert is necessary. We use a variety of supervised algorithms to predict churn.
Telecommunications has been one of the most important industries in developed nations. These service companies suffer from losing valuable customers to rivals known as customer churn (Bryman & Bell, 2011).The opposition has increased due to scientific advances and an increase in operators. Complicated strategies are required by companies to survive in such a competitive market. Customer churn is a serious problem as it causes significant losses in telecom services. To increase profits, new customers can be acquired, existing customers can be up-sold, and customers' holding periods are extended. It proves that keeping an existing customer costs less than getting one new and that it is easier to hold a customer than upselling. Companies must reduce customer churn by offering aggressive services to help them implement the third strategy. If it is done in the early stages, the ability to foretell the future customers that are likely to leave the company could be a huge revenue source (DeMuro, 2018). This prediction is possible thanks to machine learning technology, which has been proven by many types of research. This is called applied learning from historical data. [Show Less]