Customer segmentation on hotel loyalty programs: leveraging loyalty with data mining

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceAlthough topics of segmentation and loyalty programs are of central importance in the hospitality industry, research about alternative ways to segment loyalty program members is limited and managers tend to rely on traditional segmentation techniques. This study aims to provide a new customer segmentation solution for hotels’ loyalty programs using a data mining approach for identifying and classifying the customers into segments through clustering processes. Guests profiles were assessed with data about 498.655 loyalty members’ of Pestana Hotel Group. The K-means algorithm was applied in order to group the similar guests based on the monetary value of clients and consumption behavior. The goal is to compare the data-driven segments which are based on the customer’s monetary value, brand preferences and demographic data with the loyalty program tiers. The results demonstrate that the widely used tier-based loyalty programs are not optimal and are hiding important features that could be used to improve clients’ segmentation. Findings suggest that some high tier members generate comparatively less revenue for the hotel than lower tier ones. Hence, more efforts should be focused on truly valuable clients. Loyalty programs are not equally suitable for all guests neither for all brands within a hotel group, therefore additional levels of segmentation would be appropriate to match the distinct guests’ behavior. Data mining technologies can be extremely useful in order to support hotel managers in designing a more efficient and valuable loyalty program with tailored strategies and rewards