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Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers

Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers
Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers
Undoubtedly, customer relationship management has gained its importance through the statement that acquiring a new customer is several times more costly than retaining and selling additional products to existing customers. Consequently, marketing practitioners are currently often focusing on retaining customers for as long as possible. However, recent findings in relationship marketing literature have shown that large differences exist within the group of long-life customers in terms of spending and spending evolution. Therefore, this paper focuses on introducing a measure of a customer’s future spending evolution that might improve relationship marketing decision making. In this study, from a marketing point of view, we focus on predicting whether a newly acquired customer will increase or decrease his/her future spending from initial purchase information. This is essentially a classification task. The main contribution of this study lies in comparing and evaluating several Bayesian network classifiers with statistical and other artificial intelligence techniques for the purpose of classifying customers in the binary classification problem at hand. Certain Bayesian network classifiers have been recently proposed in the artificial intelligence literature as probabilistic white-box classifiers which allow to give a clear insight into the relationships between the variables of the domain under study. We discuss and evaluate several types of Bayesian network classifiers and their corresponding structure learning algorithms. We contribute to the literature by providing experimental evidence that: (1) Bayesian network classifiers offer an interesting and viable alternative for our customer lifecycle slope estimation problem; (2) the Markov Blanket concept allows for a natural form of attribute selection that was very effective for the application at hand; (3) the sign of the slope can be predicted with a powerful and parsimonious general, unrestricted Bayesian network classifier; (4) a set of three variables measuring the volume of initial purchases and the degree to which customers originally buy in different categories, are powerful predictors for estimating the sign of the slope, and might therefore provide desirable additional information for relationship marketing decision making.
artificial intelligence, bayesian network classifiers, marketing, CRM, customer loyalty
0377-2217
508-523
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Verstraeten, Geert
d717ae4b-6c22-4d41-beab-f8e43b6e1e0a
Van den Poel, Dirk
642866fa-0713-4137-8207-3a929aab98a8
Egmont-Petersen, Michael
ceb0aa2a-b31f-45ab-8bcf-b79dd32a7fec
Van Kenhove, Patrick
87791952-7b84-4b61-8305-b53eb38f10d7
Vanthienen, Jan
6f3d818f-0fce-46fa-966b-160e645caf6d
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Verstraeten, Geert
d717ae4b-6c22-4d41-beab-f8e43b6e1e0a
Van den Poel, Dirk
642866fa-0713-4137-8207-3a929aab98a8
Egmont-Petersen, Michael
ceb0aa2a-b31f-45ab-8bcf-b79dd32a7fec
Van Kenhove, Patrick
87791952-7b84-4b61-8305-b53eb38f10d7
Vanthienen, Jan
6f3d818f-0fce-46fa-966b-160e645caf6d

Baesens, Bart, Verstraeten, Geert, Van den Poel, Dirk, Egmont-Petersen, Michael, Van Kenhove, Patrick and Vanthienen, Jan (2004) Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers. European Journal of Operational Research, 156 (2), 508-523. (doi:10.1016/S0377-2217(03)00043-2).

Record type: Article

Abstract

Undoubtedly, customer relationship management has gained its importance through the statement that acquiring a new customer is several times more costly than retaining and selling additional products to existing customers. Consequently, marketing practitioners are currently often focusing on retaining customers for as long as possible. However, recent findings in relationship marketing literature have shown that large differences exist within the group of long-life customers in terms of spending and spending evolution. Therefore, this paper focuses on introducing a measure of a customer’s future spending evolution that might improve relationship marketing decision making. In this study, from a marketing point of view, we focus on predicting whether a newly acquired customer will increase or decrease his/her future spending from initial purchase information. This is essentially a classification task. The main contribution of this study lies in comparing and evaluating several Bayesian network classifiers with statistical and other artificial intelligence techniques for the purpose of classifying customers in the binary classification problem at hand. Certain Bayesian network classifiers have been recently proposed in the artificial intelligence literature as probabilistic white-box classifiers which allow to give a clear insight into the relationships between the variables of the domain under study. We discuss and evaluate several types of Bayesian network classifiers and their corresponding structure learning algorithms. We contribute to the literature by providing experimental evidence that: (1) Bayesian network classifiers offer an interesting and viable alternative for our customer lifecycle slope estimation problem; (2) the Markov Blanket concept allows for a natural form of attribute selection that was very effective for the application at hand; (3) the sign of the slope can be predicted with a powerful and parsimonious general, unrestricted Bayesian network classifier; (4) a set of three variables measuring the volume of initial purchases and the degree to which customers originally buy in different categories, are powerful predictors for estimating the sign of the slope, and might therefore provide desirable additional information for relationship marketing decision making.

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More information

Published date: 2004
Keywords: artificial intelligence, bayesian network classifiers, marketing, CRM, customer loyalty

Identifiers

Local EPrints ID: 36770
URI: https://eprints.soton.ac.uk/id/eprint/36770
ISSN: 0377-2217
PURE UUID: 9ec4cf66-8eca-441c-b4ec-8119716226fa

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Date deposited: 23 May 2006
Last modified: 17 Jul 2017 15:43

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