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Activity Number: 435
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2009 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #304661
Title: Detecting Unsatisfied Customers: A Case Study
Author(s): Kristen Schuck*+
Companies: The College of New Jersey
Address: , , ,
Keywords: binary classification ; profit analysis ; customer retention ; logistic regression ; neural network ; bootstrapping
Abstract:

This study evaluates a program that is designed to identify and retain the unsatisfied customers. The cost of the program is $15 per customer, the benefit of retaining an unsatisfied customer is $278.52, and the rate of retention of those exposed to program is 25%. The data has 19,991 cases, 24 predictors, and a binary target (satisfied vs. unsatisfied customers). The data is known as the Capital-One Data. A detailed description of the data can be found in a 2001 paper "Predicting Dissatisfied Credit Card Customers" by Zachary Arens and Dr. Wegman (http://www.galaxy.gmu.edu/stats/syllabi/inft979/ArensPaper.pdf). Our approach doubles the profit as reported in the Arens-Wegman paper. In addition, our study goes further to identify customers that would be most profitable for the company. The same techniques can be applied to many other situations for Customer Relationship Management.

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