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Data warehousing and mining: Customer churn analysis in the wireless industry

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Date Issued:
2003
Summary:
This study looks at the database technique of data warehousing and data mining to analyze the business problems related to customer churn in the wireless industry. The customer churn due to new industry regulations has hit the wireless industry hard. The study uses data warehousing and data mining to model the customer database to predict churn rates and suggest timely recommendations to increase customer retention and thereby increase overall profitability. The Naive Bayes algorithm for supervised learning was the prediction algorithm used for data modeling in the study. The data set used in the study consists of one hundred thousand real wireless customers. The study uses database tools such as Oracle database with data mining options and JDeveloper for implementing the models. The data model developed with the calibration data was used to predict the churn for the wireless customers along with the predictive accuracy and probabilities of the results.
Title: Data warehousing and mining: Customer churn analysis in the wireless industry.
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Name(s): Nath, Shyam Varan.
Florida Atlantic University, Degree grantor
Behara, Ravi, Thesis advisor
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Date Issued: 2003
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 77 p.
Language(s): English
Summary: This study looks at the database technique of data warehousing and data mining to analyze the business problems related to customer churn in the wireless industry. The customer churn due to new industry regulations has hit the wireless industry hard. The study uses data warehousing and data mining to model the customer database to predict churn rates and suggest timely recommendations to increase customer retention and thereby increase overall profitability. The Naive Bayes algorithm for supervised learning was the prediction algorithm used for data modeling in the study. The data set used in the study consists of one hundred thousand real wireless customers. The study uses database tools such as Oracle database with data mining options and JDeveloper for implementing the models. The data model developed with the calibration data was used to predict the churn for the wireless customers along with the predictive accuracy and probabilities of the results.
Identifier: 9780496179299 (isbn), 12992 (digitool), FADT12992 (IID), fau:9859 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Business
Thesis (M.B.A.)--Florida Atlantic University, 2003.
Subject(s): Data warehousing
Data mining
Telecommunication--Customer services
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/12992
Sublocation: Digital Library
Use and Reproduction: Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.