Customer Churn Prediction Using Machine learning technique

Sunita D. Patil

ISBN 10: 6139443326 / ISBN 13: 9786139443321

Published by LAP LAMBERT Academic Publishing, 2019

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - A predictive model which predicts the future churners seems to be vital. Author identifies different predictive models to recognize the customers which tend to churn in close future of insurance companies. With customer churn as main objective the study would thereby allow information gathering that can be used to take actions to avoid the customer desertion. In this book conceptual variables will be used to compare behavioral and environmental factors of customer history and to analyze the transaction record and behavior pattern to know the possibility of customer attrition to facilitate insurance companies to apply different recovery actions to customer's base on their churn tendency and contribution to the company. Author develop predictive model for Life Insurance to Predict of customer behavior, customer value, customer satisfaction and customer loyalty in Insurance Sector.

Seller Inventory # 9786139443321

Bibliographic Details

• Title: Customer Churn Prediction Using Machine learning technique
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2019
• Language: English
• ISBN 10: 6139443326
• ISBN 13: 9786139443321
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 17D millimeters 220 millimeters width by 150 millimeters height by 17 millimeters depth
• Weight: 417 grams

About this title

Synopsis

A predictive model which predicts the future churners seems to be vital. Author identifies different predictive models to recognize the customers which tend to churn in close future of insurance companies. With customer churn as main objective the study would thereby allow information gathering that can be used to take actions to avoid the customer desertion. In this book conceptual variables will be used to compare behavioral and environmental factors of customer history and to analyze the transaction record and behavior pattern to know the possibility of customer attrition to facilitate insurance companies to apply different recovery actions to customer’s base on their churn tendency and contribution to the company. Author develop predictive model for Life Insurance to Predict of customer behavior, customer value, customer satisfaction and customer loyalty in Insurance Sector.