Camacho, Pedrode Almeida, AnaAntónio, Nuno2020-12-152023-03-11202197898133425522190-3018PURE: 26862323PURE UUID: 0b96336a-0949-4034-be1e-43cc8db986aeScopus: 85097245362WOS: 000759163000027http://hdl.handle.net/10362/108683Camacho, P., de Almeida, A., & António, N. (2021). Using Customer Segmentation to Build a Hybrid Recommendation Model. In J. V. de Carvalho, P. Liberato, Á. Rocha, & A. Peña (Eds.), Advances in Tourism, Technology and Systems - Selected Papers from ICOTTS20 (pp. 299-308). (Smart Innovation, Systems and Technologies; Vol. 208). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-33-4256-9_27The growing trend in leisure tourism has been closely followed by the number of hospitality services. Nowadays, customers are more sophisticated and demand a personalized and simplified experience, which is commonly achieved through the use of technological means for anticipating customer behavior. Thus, the ability to predict a customer’s willingness to buy is also a growing trend in hospitality businesses to reach more customers and consolidate existing ones. The acquisition of a transfer service through website reservation generates data that can be used to perform customer segmentation and enable recommendations for other products or services to a customer, like recreation experiences. This work uses data from a Portuguese private transfer company to understand how its private transfer business customers can be segmented and how to predict their behavior to enhance services cross-selling. Information extracted from the data acquired with the private transfer reservations is used to train a model to predict customer willingness to buy, and based on it, offer leisure services to customers. For that, a hybrid classifier was trained to offer recommendations to a customer when he/she is booking a transfer. The model employs a two-phase process: first, a binary classifier asserts if the customer who’s buying the transfer would eventually buy a service experience. In that case, a multi-class model decides what should be the most likely experience to be recommended.10656591engCustomer segmentationHospitalityRecommendation systemTransfersGeneral Decision SciencesGeneral Computer Scienceconference object10.1007/978-981-33-4256-9_27https://www.scopus.com/pages/publications/85097245362https://www.webofscience.com/wos/woscc/full-record/WOS:000759163000027