Enhancing customer retention through data mining techniques

Abstract

A top priority in any business is a constant need to increase revenue and profitability. One of the causes for a decrease in profits is when current customers stop transacting. When a customer leaves or churns from a business, the business loses the opportunity for potential sales or cross selling. When a customer leaves the business without any form of advice, the company may find it hard to respond and take corrective action. Ideally companies should be proactive and identify potential churners prior to them leaving. Customer retention has been noted to be less costly than attracting new customers. By analysing the data analytics, companies may analyse customer behavioural patters and gather insight on their customers. These insights will help to identify profitable customers and improvements in their business process thereby increasing customer retention. This paper demonstrates the power and value of data, companies may attain through data analysis and data mining. Two techniques have been implemented Random Forest and Logistic Regression attaining 94% and 76% respectively. Through data analyses and data mining, retail businesses may adopt a proactive approach in identifying possible churners. The novelty of this paper is the concept of implementing deep learning algorithms in addition to data mining techniques. Through this, marketing campaigns may be targeted to specific profitable customers who might leave, therefore increasing profitability and reducing marketing campaign costs.