Using Machine Learning To Cocreate Value Through Dynamic Customer Engagement In A Brand Loyalty Program

TLDR

Hospitality venues traditionally rely on historical customer data for CRM, but now can also collect real‑time data and use automated processes such as machine learning to dynamically predict customer behavior and create value through engagement. The study implements machine learning at a major hospitality venue to demonstrate its merits by comparing it with traditional methods for identifying customer value in a loyalty program. Machine learning was implemented at a major hospitality venue and compared with traditional methods to identify what customers value in a loyalty program. Machine learning outperforms traditional methods in identifying customers who value specific promotions, deepening practical and theoretical understanding of dynamic customer engagement in loyalty programs.

Abstract

Hospitality venues traditionally use historical data from customers for their customer relationship management systems, but now they can also collect real-time data and automated procedures to make dynamic decisions and predictions about customer behavior. Machine learning is an example of automated processes that create insights into cocreation of value through dynamic customer engagement. To show the merits of automation, machine learning was implemented at a major hospitality venue and compared with traditional methods to identify what customers value in a loyalty program. The results show that machine learning processes are superior in identifying customers who find value in specific promotions. This research deepens practical and theoretical understanding of machine learning in the customer engagement-to-value loyalty chain and in the customer engagement construct that uses a dynamic customer engagement model.