CEC Theses and Dissertations

Campus Access Only

All rights reserved. This publication is intended for use solely by faculty, students, and staff of Nova Southeastern University. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, now known or later developed, including but not limited to photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the author or the publisher.

Date of Award

2013

Document Type

Dissertation - NSU Access Only

Degree Name

Doctor of Philosophy in Computer Information Systems (DCIS)

Department

Graduate School of Computer and Information Sciences

Advisor

Sumitra Mukherjee

Committee Member

Francisco Mitropoulos

Committee Member

Junping Sun

Abstract

Customer lifetime value models (CLTV) are a critical component of customer relationship management strategies. Over time, numerous approaches have been used to estimate the lifetime value (LTV) of a customer or segment of customers to make appropriate decisions on how to distribute marketing dollars and make other customer- related business decisions. In recent years, the development of lower cost data warehousing strategies and the ease with which customer data is captured has increased the volume of data available to firms to utilize in such models. This is, in part, a result of the rise in use of the Internet to interact with customers. Even with the additional data available from Internet interactions, much of the current research in this field relies on membership, subscription based, or contract term data, with little, if any research addressing today's multi-channel retail environment.

The robustness of data available for use in application to customer lifetime value models is another result coming from the combination of increased volume of data available, along with advances in the fields of data warehousing and data mining techniques. Existing statistical models for predicting LTV have limitations. Recent advances in machine learning techniques have allowed researchers to apply these techniques to problems similar to customer lifetime value estimation. These techniques can be applied to LTV models.

This dissertation develops and evaluates methods for estimating LTV in a multi-channel retail environment. It builds on existing models and introduces supervised learning methods, specifically feed-forward neural networks and regression trees into the prediction models to develop and evaluate new methods for LTV modeling in multi-channel retail environments.

The new models proposed by this dissertation present an easier-to-implement solution to predicting churn and the future purchase value of a customer, which are the two key elements of LTV models. These elements provide the multi-channel retail firm with data comparable in customer relationship management utility to LTV data used by organizations whose customer value is rooted in membership, subscription, or contract term data.

To access this thesis/dissertation you must have a valid nova.edu OR mynsu.nova.edu email address and create an account for NSUWorks.

  Contact Author

Share

COinS