Using Machine Learning to Cocreate Value through Dynamic Customer Engagement in a Brand Loyalty Program

Ajay Aluri, Bradley Price, Nancy H. McIntyre

Research output: Contribution to journalArticle

14 Scopus citations


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.

Original languageEnglish (US)
JournalJournal of Hospitality and Tourism Research
StateAccepted/In press - Jan 1 2018
Externally publishedYes



  • cocreation of value
  • customer engagement
  • customer loyalty
  • machine learning
  • predictive data analytics

ASJC Scopus subject areas

  • Education
  • Tourism, Leisure and Hospitality Management

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