Mining excursion tourist profile through classification algorithms

Abdullah Akgün (), Beykan Çizel () and Edina Ajanovic ()
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Abdullah Akgün: Akdeniz University
Beykan Çizel: Akdeniz University
Edina Ajanovic: Talya Software Ltd. Technocity Akdeniz University

Quality & Quantity: International Journal of Methodology, 2022, vol. 56, issue 4, No 34, 2567-2588

Abstract: Abstract Digitalization of all processes inside tourism value chain, databases for tracing the operational activities and digital environments with user-generated-content created significant data stacks that can be used in increasing performance of one tourism business. In order for one company to obtain competitive advantage, it is not only important to possess the data, but to derive the useful information from these. Goal of this study is to extract information about the profile of travel agency customers who buy or do not buy daily tours, by using classification technique of data mining method. In accordance with this goal, data regarding reservations and daily tours’ operations from one incoming travel agency operating in Antalya in Turkey were used. Total data set consisted of 9972 hotel reservations, represented by 30 different variables. After evaluation of 11 different algorithms, the C4.5 algorithm was applied, since it showed the best performance in discovering customer profiles. Results showed that the groups that purchase daily tours differ according to the region where the tour is sold and that age group and tourist type (family, group, single, etc.) are related to daily tour-purchasing decisions. In general, it was detected that young guests tend to participate in daily tours, while middle-aged and elderly ones mostly prefer to attend shopping and entertainment tours. Study results are evaluated within the scope of the current literature, while theoretical and practical implications were discussed.

Keywords: Data mining; Classification algorithms; Incoming tour operators; Excursion tourist profiles (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11135-021-01234-3

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