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Orientador(es)
Resumo(s)
Competitor identification is crucial in the hospitality industry. However, identifying the correct
competitors is a complicated and often biased task. This study investigates the problem of
defining competitive sets in the hospitality industry, particularly in the five-star hotel sector,
where traditional supply-side methods are often insufficient to capture the complexity of
modern market dynamics. Motivated by the increasing importance of customer perception
and the availability of online data, the study proposes a data-driven methodology that
leverages clustering techniques to identify meaningful groupings of competing hotels. The
dataset comprises 43 five-star hotels in Lisbon, with features extracted from online travel
agency and from the official record of tourism properties, including, among others, guest
satisfaction ratings and geographic coordinates. Following exploratory data analysis and
feature preprocessing, three clustering algorithms were tested. Based on DBSCAN combined
with K-Means clustering with five clusters, the final model utilized ratings and location data.
Results indicate that location and experiential attributes, particularly staff, cleanliness, and
comfort ratings, are more influential than price in shaping competitive structures among fivestar hotels. This finding aligns with recent literature emphasizing the limited influence of price
on competition among five-star hotels. The study offers a replicable, data-driven method for
defining competitive sets based on customer experience and location data. The findings
highlight the significance of customer experience variables in distinguishing five-star hotel
offerings.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Digital Marketing and Analytics
Palavras-chave
Competitive Sets Clustering Hospitality Hospitality Analytics SDG 8 - Decent work and economic growth
