Using Machine Learning To Cocreate Value Through Dynamic Customer Engagement In A Brand Loyalty Program. (January 2019)

• Record Type: Journal Article
• Authors: Aluri, Ajay; Price, Bradley S.; McIntyre, Nancy H.
• Is Part Of: Journal of hospitality & tourism research. Volume 43:Number 1(2019)
• Page Start: 78
• Page End: 100
• Publication Date: 2019-01
• Subjects: cocreation of value -- customer engagement -- machine learning -- predictive data analytics -- customer loyalty; Hotel management -- Study and teaching -- Periodicals; Restaurant management -- Study and teaching -- Periodicals; Food service management -- Study and teaching -- Periodicals; 647.068
• DOI: 10.1177/1096348017753521
• Languages: English
• ISSNs: 1096-3480

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.