Open AccessCustomer Churn Prediction using Logistic Regression with Regularization and Optimization Technique
Author(s) -
Bharathi Arivazhagan,
Devika Subramanian
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.i7219.079920
Subject(s) - computer science , customer relationship management , data mining , database transaction , profitability index , transaction data , logistic regression , customer retention , customer intelligence , loyalty business model , machine learning , service (business) , marketing , business , service quality , database , finance
Customer Relationship Management (CRM) is a challenging issue in marketing to better understand the customers and maintaining long-term relationships with them to increase the profitability. It plays a vital role in customer centered marketing domain which provides a better service and satisfies the customer requirements based on their characteristics in consuming patterns and smoothes the relationship where various representatives communicate and collaborate. Customer Churn prediction is one of the area in CRM that explores the transaction and communication process and analyze the customer loyalty. Data mining ease this process with classification techniques to explore pattern from large datasets. It provides a good technical support to analyze large amounts of complex customer data. This research paper applies data mining classification technique to predict churn customers in three variant sectors Banking, E-commerce and Telecom. For Classification, enhanced logistic regression with regularization and optimization technique is applied. The work is implemented in Rapid miner tool and the performance of the prediction algorithm is assessed for three variant sectors with suitable evaluation metrics.