Benefit segmentation of a summer destination in Uruguay: a clustering and classification approach

Abstract

Purpose This study aims to perform a benefit segmentation and then a classification of visitors that travel to the Rocha Department in Uruguay from the capital city of Montevideo during the summer months. Design/methodology/approach A convenience sample was obtained with an online survey. A total of 290 cases were usable for subsequent data analysis. The following statistical techniques were used: hierarchical cluster analysis, K-means cluster analysis, machine learning, support vector machines, random forest and logistic regression. Findings Visitors that travel to the Rocha Department from Montevideo can be classified into four distinct clusters. Clusters are labelled as “entertainment seekers”, “Rocha followers”, “relax and activities seekers” and “active tourists”. The support vector machine model achieved the best classification results. Research limitations/implications Implications for destination marketers who cater to young visitors are discussed. Destination marketers should determine an optimal level of resource allocation and destination management activities that compare both present costs and discounted potential future income of the different target markets. Surveying non-residents was not possible. Future work should sample tourists from abroad. Originality/value The combination of market segmentation of Rocha Department’s visitors from the city of Montevideo and classification of sampled individuals training various machine learning classifiers would allow Rocha’s destination marketers determine the belonging of an unsampled individual into one of the already obtained four clusters, enhancing marketing promotion for targeted offers.

Tourism village clusters: Potential for development at Jepara, Indonesia

“…The research of Maulida (2018) on the application of data mining in classifying tourist visits to top tourist attractions in Daerah Khusus Ibu Kota (DKI) Jakarta province with k-means, produces three clusters of superior tourist attractions in DKI Jakarta province in iteration 2 in which cluster 3 is a record for DKI Jakarta province. In addition, the research of Perera et al (2020) using k-means cluster analysis on the grouping of visitors who travel to Rocha results in four cluster groups.…”

Benefit Segmentation of Tourists to Geosites and Its Implications for Sustainable Development of Geotourism in the Southern Lake Tana Region, Ethiopia

“…Since its introduction by Haley in 1968, benefit segmentation has been applied in many study fields [32], one of which is tourism. Benefit segmentation was conducted on different types and forms of tourism, such as ecotourism (e.g., Palacio & Mc Cool [13]), rural tourism (e.g., Frochot [34]), rural community-based festivals (e.g., Li et al [35]), spa goers (e.g., Koh et al [36]), wellbeing tourism (e.g., Pesonen et al [37]), health tourism (e.g., Dryglas & Salamaga [38]), outbound summer package tourism (e.g., Zečević & Kovačević [39]), naturebased tourism (e.g., Nduna & van Zyl [40]), summer destination (e.g., Perera et al [41]), and pleasure boating (e.g., Benevolo & Spinelli [42]).…”

“…Convenience sampling was used in this study because it was difficult to control who the respondents were (they were continuously moving and had a short stay at the destination). This sampling technique was used by many tourist-segmentation studies (e.g., [30,41,[50][51][52]. To make the sample size representative of the population, tourists with the largest presence in the destination (based on the major tourist origin countries) were considered.…”

Editorial - How the world is changing: The role of micro trends

“…They are important to observe as they are signals and sign posts for the future of tourism (Robertson and Yeoman, 2014). Certain micro trends are important to follow as they become the basis of big data and segmentation models for destination decision-making (Fuchs et al, 2014;Perera et al, 2020).…”