Customer Lifetime Value: A Data Science Approach for Hospitality Applications
Keywords:hospitality, casinos, customer lifetime value, Markov-chains, customer relationship marketing
Segmentation of databases based on Customer Lifetime Value (CLV) is the cornerstone of Customer Relationship Management (CRM). To implement CRM strategies, the hospitality industry relies heavily on loyalty programs to track customer behavior. Despite the prevalence of loyalty programs, little attention has been given to CLV model formulation in hospitality. This paper reviews the extant literature discussing CLV modeling and formulates a model with hospitality-specific considerations. Based on the literature, a phased approach is proposed using cluster and Markov chain analyses, while incorporating a new metric based on a customers’ expected trip cycle to identify lost customers in the non-contractual setting. The model is empirically tested on casino loyalty data to demonstrate the viability and robustness of the approach for hospitality sectors.
Copyright (c) 2022 Timothy Webb, Ray Cho, Mark Legg
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