Top

Quality & Quantity

Published in:

14-09-2021

Mining excursion tourist profile through classification algorithms

Authors: Abdullah Akgün, Beykan Çizel, Edina Ajanovic

Published in: Quality & Quantity | Issue 4/2022

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

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.

previous article New insights on respondents’ recall ability and memory effects when repeatedly measuring political efficacy

next article College students’ and Mechanical Turk workers’ environmental factors while completing online surveys

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Akgün, A., Çizel, B.: Günlük tur programları oluşturmada veri madenciliği: a grubu seyahat acentası örneği. Turar Tur. Araştırma Derg. 6(1), 73–87 (2017)

Alaei, A.R., Becken, S., Stantic, B.: Sentiment analysis in tourism: capitalizing on big data. J. Travel Res. 58(2), 175–191 (2019)CrossRef

Alexander, K.A., Ramotadima, M., Sanderson, C.E.: The power of consensus: developing a community voice in land use planning and tourism development in biodiversity hotspots. Ecosyst. Serv. 30, 350–361 (2018)CrossRef

Bach, M.P., Schatten, M., Marušić, Z.: Data mining applications in tourism: a keyword analysis. In: Central European conference on information and intelligent systems. 18–20 September 2013, Varazdin, Croatia, pp. 26–32 (2013)

Berry, M.J., Linoff, G.S.: Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Wiley Publishing Inc., Indianapolis (2004)

Bloom, J.Z.: Market segmentation: a neural network application. Ann. Tour. Res. 32(1), 93–111 (2005)CrossRef

Byrd, E.T., Gustke, L.: Using decision trees to identify tourism stakeholders: the case of two Eastern North Carolina counties. Tour. Hosp. Res. 7(3/4), 176–193 (2007)CrossRef

Chang, J.R., Chen, M.Y., Chen, L.S., Tseng, S.C.: Why customers don’t revisit in tourism and hospitality industry? IEEE Access 7, 146588–146606 (2019)CrossRef

Cho, V., Leung, P.: Towards using knowledge discovery techniques in database marketing for the tourism industry. J. Qual. Assur. Hosp. Tour. 3, 109–131 (2002)CrossRef

Çuhadar, M., Güngör, İ, Göksu, A.: Turizm talebinin yapay sinir ağları ile tahmini ve zaman serisi yöntemleri ile karşılaştırmalı analizi: antalya iline yönelik bir uygulama. Süleyman Demirel Üniversitesi, İktis. Ve İdari Bilim. Fakültesi Derg. 14(1), 99–114 (2009)

Delen, D., Sirakaya, E.: Determining the efficacy of data-mining methods in predicting gaming ballot outcomes. J. Hosp. Tour. Res. 30(3), 313–332 (2006)CrossRef

Dietz, L.W., Herzog, D., Wörndl, W.: Deriving tourist mobility patterns from check-in data. In: Proceedings of the WSDM 2018 workshop on learning from user interactions. Learn-IR’18, 9 Feb. 2018, Los Angeles, California, USA (2018)

Grinberger, A.Y., Shoval, N.: Spatiotemporal contingencies in tourists’ intradiurnal mobility patterns. J. Travel Res. 58(3), 512–530 (2019)CrossRef

Guo, Y., Barnes, S.J., Jia, Q.: Mining meaning from online ratings and reviews: tourist satisfaction analysis using latent dirichlet allocation. Tour. Manag. 59, 467–483 (2017)CrossRef

Ha, S.H., Park, S.C.: Application of data mining tools to hotel data mart on the Intranet for database marketing. Expert Syst. Appl. 15(1), 1–31 (1998)CrossRef

Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco (2006)

Kesorn, K., Juraphanthong, W., Salaiwarakul, A.: Personalized attraction recommendation system for tourists through check-in data. IEEE Access 5, 26703–26721 (2017)CrossRef

Law, R., Li, G., Fong, D.K.C., Han, X.: Tourism demand forecasting: a deep learning approach. Ann. Tour. Res. 75, 410–423 (2019)CrossRef

Li, G., Law, R., Wang, J.: Analyzing international travelers’ profile with self-organizing maps. J. Travel Tour. Mark. 27(2), 113–131 (2010)CrossRef

Li, G., Law, R., Vu, H.Q., Rong, J.: Discovering the hotel selection preferences of Hong Kong inbound travelers using the Choquet Integral. Tour. Manag. 36, 321–330 (2013)CrossRef

Liao, S., Chen, Y., Deng, M.: Mining customer knowledge for tourism new product development and customer relationship management. Expert Syst. Appl. 37(6), 4212–4223 (2010)CrossRef

Lin, C.T., Huang, Y.L.: Mining tourist imagery to construct destination image position model. Expert Syst. Appl. 36(2), 2513–2524 (2009)CrossRef

López, A.L.S., Chaglla, S.A.F., Figueroa, F.E.E., Tenesaca, D.S.A., Pucha, M.E.V.: Modeling of tourist profiles with decision trees in a world heritage city: the case of Cuenca (Ecuador). Tour. Plan. Dev. 16(5), 473–493 (2019)CrossRef

Majewska, J., Truskolaski, S.: Cluster-mapping procedure for tourism regions based on geostatistics and fuzzy clustering: example of Polish districts. Curr. Issue Tour. 22(19), 2365–2385 (2019)CrossRef

Min, H., Min, H., Emam, A.: A data mining approach to developing the profiles of hotel customers. Int. J. Contemp. Hosp. Manag. 14(6), 274–285 (2002)CrossRef

Nagao, M., Kawamura, H., Yamamoto, M., Ohuchi, A.: Acquisition of personal tourism activity information based on GPS log mining method. In: Fifth Asia Pacific industrial engineering and management systems conference vol. 37, pp. 1–37 (2004)

Nave, M., Rita, P., Guerreiro, J.: A decision support system framework to track consumer sentiments in social media. J. Hosp. Market. Manag. 27(6), 693–710 (2018)

Nilashi, M., Esfahani, M.D., Roudbaraki, M.Z., Ramayah, T., Ibrahim, O.: A multi-criteria collaborative filtering recommender system using clustering and regression techniques. J. Soft Comput. Decis. Support Syst. 3(5), 24–30 (2016)

Nuankaew, W., Nuankaew, P., Phanniphong, K., Bussaman, S.: For discovery: significant factors for the promotion of tourist attractions based on individual behaviour through data- mining techniques. Chophayom J. 28(3), 88–102 (2017)

Olmeda, I., Sheldon, P.J.: Data mining techniques and applications for tourism internet marketing. J. Travel Tour. Mark. 11(2–3), 1–20 (2001)

Olson, D.L., Delen, D.: Advances Data Mining Techniques. Springer, Berlin (2008)

Park, S., Xu, Y., Jiang, L., Chen, Z., Huang, S.: Spatial structures of tourism destinations: a trajectory data mining approach leveraging mobile big data. Ann. Tour. Res. 84, 102973 (2020)CrossRef

Pyo, S., Uysal, M., Chang, H.: Knowledge discovery in database for tourist destinations. J. Travel Res. 40(4), 374–384 (2002)CrossRef

Samara, D., Magnisalis, I., Peristeras, V.: Artificial intelligence and big data in tourism: a systematic literature review. J. Hosp. Tour. Technol. 11(2), 343–367 (2020)

Sathishkumar, V.E., Park, J., Cho, Y.: Seoul bike trip duration prediction using data mining techniques. IET Intel. Transp. Syst. 14(11), 1465–1474 (2020)CrossRef

Septiadi, H., Ariandika, M.C., Alamsyah, A.: Prediction models based on flight tickets and hotel rooms data sales for recommendation system in online travel agent business. In: Paper presented at the sustainable collaboration in business, technology, information and innovation (SCBTII) conference. West Java, Indonesia, pp. 15–16 August 2016. (2018)

Shao, H., Zhang, Y., Li, W.: Extraction and analysis of city’s tourism districts based on social media data. Comput. Environ. Urban Syst. 65, 66–78 (2017)CrossRef

Shapoval, V., Wang, M.C., Hara, T., Shioya, H.: Data Mining in tourism data analysis: inbound visitors to Japan. J. Travel Res. 57(3), 310–323 (2018)CrossRef

Shi, X.: Tourism culture and demand forecasting based on BP neural network mining algorithms. Pers. Ubiquit. Comput. 24, 299–308 (2020)CrossRef

Siering, M., Deokar, A.V., Janze, C.: Disentangling consumer recommendations: explaining and predicting airline recommendations based on online reviews. Decis. Support Syst. 107, 52–63 (2018)CrossRef

Srivastava, S.K., Chandra, B., Srivastava, P.: The impact of knowledge management and data mining on CRM in the service industry. In: Nath, V., Mandal, J.K. (eds.) Nanoelectronics, Circuits and Communication Systems, pp. 37–52. Springer, Singapore (2019)CrossRef

Sun, X., Huang, Z., Peng, X., Chen, Y., Liu, Y.: Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data. Int. J. Digit. Earth 12(6), 661–678 (2018)CrossRef

Vajirakachorn, T., Chongwatpol, J.: Application of business intelligence in the tourism industry: a case study of a local food festival in Thailand. Tour. Manag. Perspect. 23, 75–86 (2017)CrossRef

Wan, L., Hong, Y., Huang, Z., Peng, X., Li, R.: A hybrid ensemble learning method for tourist route recommendations based on geo-tagged social networks. Int. J. Geogr. Inf. Sci. 32(11), 2225–2246 (2018)CrossRef

Wong, J., Chen, H., Chung, P., Kao, N.: Identifying valuable travelers and their next foreign destination by the application of data mining techniques. Asia Pac. J. Tour. Res. 11(4), 355–373 (2006)CrossRef

Xie, C., Lu, J., Parkany, E.: Work travel mode choice modeling with data mining: decision trees and neural networks. Transp. Res. Rec. 1854, 50–61 (2003)CrossRef

Zhang, K., Chen, Y., Li, C.: Discovering the tourists’ behaviors and perceptions in a tourism destination by analyzing photos’ visual content with a computer deep learning model: the case of Beijing. Tour. Manag. 75, 595–608 (2019)CrossRef

Zheng, X., Luo, Y., Sun, L., Zhang, J., Chen, F.: A tourism destination recommender system using users’ sentiment and temporal dynamics. J. Intell. Inf. Syst. 51(3), 557–578 (2018)CrossRef

Zhou, X., Su, M., Liu, Z., Hu, Y., Sun, B., Feng, G.: Smart tour route planning algorithm based on naïve bayes interest data mining machine learning. ISPRS Int. J. Geo Inf. 9(2), 112 (2020)CrossRef

Title: Mining excursion tourist profile through classification algorithms
Authors: Abdullah Akgün
Beykan Çizel
Edina Ajanovic
Publication date: 14-09-2021
Publisher: Springer Netherlands
Published in: Quality & Quantity / Issue 4/2022
Print ISSN: 0033-5177
Electronic ISSN: 1573-7845
DOI: https://doi.org/10.1007/s11135-021-01234-3

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 4/2022

Methodological approach to the evolution of a terrorist organisation: ETA, 1959–2018

The role of farmers’ attitude towards their resources to alleviate rural household poverty

Implementation of the SutteARIMA method to predict short-term cases of stock market and COVID-19 pandemic in USA

New insights on respondents’ recall ability and memory effects when repeatedly measuring political efficacy

The status quo of omani female entrepreneurs: a story of multidimensional success factors

Ensuring survey research data integrity in the era of internet bots

Premium Partner