Publication Date
Spring 2016
Document Type
Project Summary
Degree Name
Master of Science
Department
Computer Science
First Advisor
Soon-Ok Park, Ph.D.
Second Advisor
(Clare) Xueqing Tang, Ph.D.
Third Advisor
Neng-Shin Chen, M.S.
Abstract
The interest for data mining techniques has increased tremendously during the past decades, and numerous classification techniques have been applied in a wide range of business applications. Hence, the need for adequate performance measures has become more important than ever. In this application, a cost-benefit analysis framework is formalized in order to define performance measures which are aligned with the main objectives of the end users, i.e., profit maximization.
A new performance measure is defined, the expected maximum profit criterion. This general framework is then applied to the customer churn problem with its particular cost-benefit structure. The advantage of this approach is that it assists companies with selecting the classifier which maximizes the profit. Moreover, it aids with the practical implementation in the sense that it provides guidance about the fraction of the customer base to be included in the retention campaign.
Recommended Citation
Kambhampati, Divyasri; Kommidi, Madhurika Reddy; and Metla, Prathyusha, "A Metric for Measuring Customer Turnover Prediction Models" (2016). All Capstone Projects. 210.
https://opus.govst.edu/capstones/210
Comments
Authors listed in alphabetical order by OPUS staff.