How Machine Learning Impacts Organizations

server-2160321_1920 (1)[We’re pleased to welcome authors Dr. Ajay Aluri, Dr. Bradley S. Price, Dr. Nancy H. McIntyre of West Virginia University. They recently published an article in Journal of Hospitality & Tourism Research entitled “Using Machine Learning to Cocreate Value through Dynamic Customer Engagement in a Brand Loyalty Program,” which is currently free to read for a limited time. Below, Dr. Aluri reveals the inspiration for conducting this research :]

2JHTR07_Covers.pdfWhat motivated you to pursue this research?

The ability to see how machine learning impacts organizations has always been an interest of the authors. We know that customer engagement is dynamic and behaviors change rapidly, and we know that making managerial decisions inside of this dynamic model is extremely difficult. This made us believe that machine learning could be used to identify these changes in behaviors and more importantly help organizations identify what customers value. Machine learning models can behave in a dynamic way as well, which we thought may be able to match the dynamic behavior of engagement. Many researchers look at the use of social networking information or other text information, such as survey data when making decisions, but there can be issues with each of these. It was our goal to see if we could use the information in a CRM and update predictions for customer behavior as new information entered the system, with the final goal of understanding and influencing behavior that co-creates value for organizations and customers. We hoped that this would be of interest to a broad range of industry practitioners and academic researchers.

In what ways is your research innovative, and how do you think it will impact the field?

Hospitality and tourism businesses traditionally use historical customer data to make dynamic decisions and predictions about customer behavior. This study used an innovative machine learning approach to co-create value through dynamic customer engagement. It was implemented at a major hospitality venue to successfully identify what customers value in a loyalty program. This research study offers methodology for adjusting offers, promotions, and rewards to influence customer engagement behaviors in real time, which bridges research gaps on the application of machine learning in the hospitality and tourism industry. This research method offers an innovative approach to solving the challenges with big data, and in the future, hospitality and tourism professionals will be able to use algorithms to develop highly targeted marketing strategies that are needed in the industry, particularly businesses that utilize the massive amount of customer information in loyalty programs.

What advice would you give to new scholars and incoming researchers in this particular field of study?

Our advice for researchers entering this field is to understand that machine learning methods can behave in a dynamic manner as well. That means different input variables may matter at different times in the system. Variables that may be meaningless as your system starts could gain importance or play a role at later times. Understanding the methods of machine learning and how variable impact is decided is extremely important as well. As we stated in the paper, this study changes the way hospitality and tourism venues and businesses—hotels, resorts, theme parks, cruise ships, convention centers, etc.—view customer engagement and use machine learning to predict the behavior of a customer at different engagement opportunities. Furthermore, managers can now use this information to publish offers or promotions via different mediums in real-time to engage with the customers in ways that would directly or indirectly influence their loyalty. Taking time to make sure the right methods are used is a key component, and may times that means developing new machine learning methods that exploit the specifics of the problem.

 

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