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

• Perera, Gonzalo ^[1] ; Sprechmann, Martin ^[2] ; Bourel, Mathias ^[3]
  1. [1] Centro Universitario Regional Este, Universidad de la República,Rocha, Uruguay
  2. [2] Facultad de Ciencias Económicas y de Administración,Universidad de la República, Montevideo, Uruguay
  3. [3] Facultad de Ingeniería, Instituto de Matemática y Estadística Prof. Rafael Laguardia,Universidad de la República, Montevideo, Uruguay

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• Localización: Journal of Tourism Analysis = Revista de Análisis Turístico, ISSN 1885-2564, Vol. 27, Nº. 2, 2020, págs. 185-206
• Idioma: inglés
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• Resumen

  • 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.