Clustering and Segmentation of Ticketing Data
Client: Sabre Airline Solutions
Team: Hunter Ross, Mary Liz Tuttle, Ramon Trespalacios
Faculty Advisor: Dr. Barr Year: 2014
Documents: Presentation, Report, Video
Sabre Airline Solutions offers data solutions and software to aid airlines sell products, market themselves, and operate efficiently. The company would like to provide traveler segmentation services for their customer reservation system to support various marketing programs. (Segmentation involves classifying prospective buyers into groups, or segments, to create products specifically for each segment.) This project required creating segmentation rules that classify ticket purchase data in this manner.
The senior design team replicated the data to create pre-booking and post-booking results. Pre-booking segmentation will show clusters that do not include variables such as fare and travel time, because these can’t be known until after booking. On the other hand, post‐booking data will provide segments that include purchases made. Pre-booking clusters could be used to make promotions for customers while booking, and post-booking clusters could be used to make promotions after booking.
The team used k-means clustering method and the R software to find the optimal number of clusters in the data and assist Sabre with the design of good fare products. For example, if an airline has created a ticket fare product for a specific market like business‐travelers, the team’s segmentation rules can confirm whether the product is well‐defined and well-targeted.