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, Ecommerce 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.
Experimental Parameter Tuning of Artificial Neural Network in Customer Churn Prediction
