CRM Targeting with reference-dependent sensitivities: Evidence from the casino industry

Wayne Taylor () and Jonathan Zhang ()
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Wayne Taylor: Southern Methodist University
Jonathan Zhang: Colorado State University

Quantitative Marketing and Economics (QME), 2025, vol. 23, issue 2, No 4, 319-345

Abstract: Abstract This research explores heterogeneity in customers’ reference-dependent sensitivities using rich, individual level CRM data from a large casino in the U.S. and discusses implications for targeting decisions. We use a unique panel dataset of over 12,000 slot machine gamblers over 14 years and model heterogeneity in reference-dependent sensitivities at the individual level using a hierarchical Bayesian model. This analysis focuses on gains and losses relative to three reference points unique to the casino industry but conceptually extends to many other settings such as the financial services industry and hospital- ity: gambling outcomes relative to 1) zero, 2) prior trip outcomes, and 3) expected losses based on the house advantage of the slot machines. Firms can use heterogeneous reference-dependent sensitivities to improve their targeting decisions by considering the sequences of gambler outcomes in tandem with gamblers’ individual sensitivities to marketing promotions. In our empirical application, we estimate that incorporating individual-level reference-dependent sensitivities improves targeted offer profitability by at least 19.8% relative to a comparable RFM model, depending on the offer type.

Keywords: Casino; Customer relationship management; Targeting; Reference-dependent sensitivities (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11129-025-09293-8

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