CLUSTERING FAMILY-FRIENDLY HOTELS’ GUESTS TO DEVELOP TOURISM MARKETING STRATEGIES

Rizka Miladiah Ervianty; Nasa Zata Dina; Sidarta Prassetyo

doi:10.2298/IJGI2402213M

Authors

Rizka Miladiah Ervianty Airlangga University, Faculty of Vocational Studies, Department of Business, Surabaya https://orcid.org/0000-0003-0582-3017
Nasa Zata Dina Airlangga University, Faculty of Vocational Studies, Department of Engineering, Surabaya https://orcid.org/0000-0002-8560-8626
Sidarta Prassetyo Airlangga University, Faculty of Vocational Studies, Department of Business, Surabaya https://orcid.org/0000-0001-5715-0091

DOI:

https://doi.org/10.2298/IJGI2402213M

Keywords:

customer satisfaction, hotels, sustainable tourism development, tourism and economic growth, tourism economics

Abstract

An increasing number of guests in hotels evaluate the quality by reading online reviews. A deeper analysis of the attitude and behavior of the visitors is conducted to understand the experiences of guests, considering the diverse backgrounds and needs. This study aims to analyze the selection process of family-friendly hotels by guests, using available TripAdvisor online reviews, as well as for hotel management to better understand the comments left by guests and create more organized plans and policies. A model is devised that integrates clustering and Multi-Criteria Decision-Making-VIKOR (MCDM-VIKOR) method to prioritize the attributes of hotels based on the significance within each cluster of guests. Data is collected from online reviews of guests in family-friendly hotels in Indonesia. The features used for ranking preferences are the numerical ratings assigned to four attributes on the platform. These four features included “location”, “cleanliness”, “service”, and “value”. The results showed that “cleanliness” evolved as the most critical factor in the majority of segments for selecting family-friendly hotels. To further comprehend the behavioral trends of guests and assist in decision-making, this study proposed a model capable of analyzing online reviews and ratings provided by customers.

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