Developing Churn Models Using Data Mining Techniques and Social Network Analysis

Nonfiction, Computers, Advanced Computing, Programming, Data Modeling & Design, Database Management, General Computing
Cover of the book Developing Churn Models Using Data Mining Techniques and Social Network Analysis by Goran Klepac, Robert Kopal, Leo Mršić, IGI Global
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Author: Goran Klepac, Robert Kopal, Leo Mršić ISBN: 9781466662902
Publisher: IGI Global Publication: July 31, 2014
Imprint: Information Science Reference Language: English
Author: Goran Klepac, Robert Kopal, Leo Mršić
ISBN: 9781466662902
Publisher: IGI Global
Publication: July 31, 2014
Imprint: Information Science Reference
Language: English
Churn prediction, recognition, and mitigation have become essential topics in various industries. As a means for forecasting and managing risk, further research in this field can greatly assist companies in making informed decisions based on future possible scenarios. Developing Churn Models Using Data Mining Techniques and Social Network Analysis provides an in-depth analysis of attrition modeling relevant to business planning and management. Through its insightful and detailed explanation of best practices, tools, and theory surrounding churn prediction and the integration of analytics tools, this publication is especially relevant to managers, data specialists, business analysts, academicians, and upper-level students.
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Churn prediction, recognition, and mitigation have become essential topics in various industries. As a means for forecasting and managing risk, further research in this field can greatly assist companies in making informed decisions based on future possible scenarios. Developing Churn Models Using Data Mining Techniques and Social Network Analysis provides an in-depth analysis of attrition modeling relevant to business planning and management. Through its insightful and detailed explanation of best practices, tools, and theory surrounding churn prediction and the integration of analytics tools, this publication is especially relevant to managers, data specialists, business analysts, academicians, and upper-level students.

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