Customer Segmentation in IT Sector using Datamining Techniques

Abstract

Due to its large client base, the IT industry generates enormous amounts of data every day. Business experts and decision-makers stressed that keeping current clients is less expensive than acquiring new ones. Business analysts and customer relationship management (CRM) analysts must understand the causes of customer attrition as well as the patterns of behavior found in the data of these clients. This research is a comprehensive study about churn prediction in IT industry it also suggests a churn prediction model to identify consumer churn and provide the reasons behind customer churn in the IT industry by employing clustering and classification algorithms. Information gain and correlation attribute ranking filters are used in feature selection. One of the CRM's most important tasks is to create retention strategies that work to keep customers from leaving. The study intends to create a model that can effectively identify the primary reasons behind customer churn. This model is likely to use various techniques, such as analyzing customer behavior, preferences, or other relevant data, to identify patterns or characteristics associated with churn and segment the churning customers based on groups in order to retain the customers based on the specific characteristics or behaviors they share within their respective groups. The main aspect of the study is to identify the challenges in the existing methods that are used in churn prediction and suggest a model to improve churn prediction and elude churn effectively.