A Hierarchical Bayesian Approach to Predicting Retail Customers' Share-of-Wallet Loyalty

36 Pages Posted: 12 Apr 2007

See all articles by Edward Fox

Edward Fox

Southern Methodist University (SMU) - Marketing Department

Jacquelyn Thomas

Northwestern University - Integrated Marketing Communications Program

Date Written: November 2006

Abstract

As supermarkets have developed the capability to gather customer data, their focus on customer loyalty has increased; however, retailers cannot actually measure loyalty. In this paper, we propose and test a hierarchical Bayesian approach to predicting customers' share-of-wallet loyalty that offers a high degree of predictive accuracy and discriminates well between loyal and non-loyal customers. Among the types of information that retailers may gather, we find that geographic information (travel time to the store and retail concentration around that store) makes the greatest predictive contribution. Finally, we show how retailers can use the posterior distribution of customer share-of-wallet to more profitably select customers to receive targeted marketing offers.

Keywords: retail, shopping behavior, customer relationship management, share-of-wallet, loyalty

JEL Classification: M3

Suggested Citation

Fox, Edward and Thomas, Jacquelyn, A Hierarchical Bayesian Approach to Predicting Retail Customers' Share-of-Wallet Loyalty (November 2006). SMU Cox School of Business Research Paper No. 07-003. Available at SSRN: https://ssrn.com/abstract=980047 or http://dx.doi.org/10.2139/ssrn.980047

Edward Fox (Contact Author)

Southern Methodist University (SMU) - Marketing Department ( email )

United States

Jacquelyn Thomas

Northwestern University - Integrated Marketing Communications Program ( email )

Evanston, IL
United States

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