Data-Driven Market Segmentation in Hospitality Using Unsupervised Machine Learning
This work addresses the need for understandable, adaptable segmentation in hospitality marketing, though it is incremental as it applies existing methods to this domain.
The study tackled the problem of market segmentation in hospitality by applying hierarchical clustering to guest profiles, resulting in a process that provides actionable insights for marketing departments to make data-driven decisions.
Within hospitality, marketing departments use segmentation to create tailored strategies to ensure personalized marketing. This study provides a data-driven approach by segmenting guest profiles via hierarchical clustering, based on an extensive set of features. The industry requires understandable outcomes that contribute to adaptability for marketing departments to make data-driven decisions and ultimately driving profit. A marketing department specified a business question that guides the unsupervised machine learning algorithm. Features of guests change over time; therefore, there is a probability that guests transition from one segment to another. The purpose of the study is to provide steps in the process from raw data to actionable insights, which serve as a guideline for how hospitality companies can adopt an algorithmic approach.