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Optimal customer relationship management using Bayesian decision theory: an application for customer selection

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This study addresses significant challenges that practitioners face when using customer lifetime value (CLV) for customer selection. First, the authors propose a Bayesian decision theory-based customer selection framework that accommodates the uncertainty inherent in predicting customer behavior. They develop a joint model of purchase timing and quantity that is amenable for selecting customers using CLV. Second, the authors compare performance of the proposed customer selection framework (1) with the current customer selection procedure in the collaborating firm and (2) with different customer-level cost allocation rules that are necessary for computing CLV. The study finds that given a budget constraint, customers selected by means of a Bayesian decision theory-based framework (i.e., using the maximized expected CLV of a customer and the corresponding optimal marketing costs as an estimate of future costs) provide the highest profits. The study provides guidelines for implementation and illustrates how the proposed customer selection framework can aid managers in enhancing marketing productivity and estimating return on marketing actions..Printed Journal

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Ketersediaan

Call Number	Location	Available
	PSB lt.dasar - Pascasarjana	1

Penerbit	American Marketing Association.,
Edisi	-
Subjek	Consumer behavior Decision theory Models Customer relationship management studies Bayesian analysis
ISBN/ISSN	222437
Klasifikasi	-
Deskripsi Fisik	-
Info Detail Spesifik	-
Other Version/Related	Tidak tersedia versi lain
Lampiran Berkas	Tidak Ada Data