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Mining excursion tourist profile through classification algorithms

Author

Listed:
  • Abdullah Akgün

    (Akdeniz University)

  • Beykan Çizel

    (Akdeniz University)

  • Edina Ajanovic

    (Talya Software Ltd. Technocity Akdeniz University)

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.

Suggested Citation

  • Abdullah Akgün & Beykan Çizel & Edina Ajanovic, 2022. "Mining excursion tourist profile through classification algorithms," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(4), pages 2567-2588, August.
    • Handle: RePEc:spr:qualqt:v:56:y:2022:i:4:d:10.1007_s11135-021-01234-3
    DOI: 10.1007/s11135-021-01234-3
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    References listed on IDEAS

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    1. Alexander, K.A. & Ramotadima, M. & Sanderson, Claire E., 2018. "The power of consensus: Developing a community voice in land use planning and tourism development in biodiversity hotspots," Ecosystem Services, Elsevier, vol. 30(PC), pages 350-361.
    2. Li, Gang & Law, Rob & Vu, Huy Quan & Rong, Jia, 2013. "Discovering the hotel selection preferences of Hong Kong inbound travelers using the Choquet Integral," Tourism Management, Elsevier, vol. 36(C), pages 321-330.
    3. Guo, Yue & Barnes, Stuart J. & Jia, Qiong, 2017. "Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation," Tourism Management, Elsevier, vol. 59(C), pages 467-483.
    4. Park, Sangwon & Xu, Yang & Jiang, Liu & Chen, Zhelin & Huang, Shuyi, 2020. "Spatial structures of tourism destinations: A trajectory data mining approach leveraging mobile big data," Annals of Tourism Research, Elsevier, vol. 84(C).
    5. Law, Rob & Li, Gang & Fong, Davis Ka Chio & Han, Xin, 2019. "Tourism demand forecasting: A deep learning approach," Annals of Tourism Research, Elsevier, vol. 75(C), pages 410-423.
    6. David L. Olson & Dursun Delen, 2008. "Advanced Data Mining Techniques," Springer Books, Springer, number 978-3-540-76917-0, October.
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