Data-Driven Management Using Business Analytics: The Case Study of Data Sets for New Business in Tourism
Aarish Sachdeva
Suncity School, Suncity Township, Sector 54, Gurugram, India
Download PDF
http://doi.org/10.37648/ijtbm.v14i03.005
Abstract
Tourism entrepreneurship has become increasingly data-intensive. This paper synthesizes peer-reviewed work from 2013-2023 to propose a practical, data-driven management playbook for a new tourism venture. We review major data sources (user-generated content, search and social signals, mobile positioning, OTA/airbnb supply & price data, and official statistics) and the analytics they enable (demand forecasting, location and product design, pricing, and reputation management). We then construct a case study for a hypothetical startup TourStart launching curated neighbourhood experiences in a beach destination. Using a portfolio of feasible, real-world data sets and methods (e.g., Google Trends for demand signals, TripAdvisor review mining for product features, Airbnb/OTA data for capacity & pricing, and where available mobile positioning for flows), we outline end-to-end decisions: market selection, seasonality calibration, micro-location choice, itinerary design, price setting, and marketing mix. Comparative analysis shows complementary strengths and biases across data sets: search data is timely but volatile; reviews are rich but biased toward vocal users; mobile data is granular but regulated; OTA/supply data captures competition but can contain systematic errors if not validated. We close with implementation guidance (data pipelines, KPIs, and experimentation), limitations (privacy, survivorship bias, platform shocks), and a research agenda. The contribution is a reproducible blueprint that aligns business analytics capability with entrepreneurial decisions in tourism.
Keywords: Tourism entrepreneurship; OTA/airbnb supply; hypothetical; Airbnb/OTA data
References
- Alaei, A. R., Becken, S., & Stantic, B. (2019). Sentiment Analysis in Tourism: Capitalizing on Big Data. Journal of Travel Research, *58*(2), 175–191. https://doi.org/10.1177/0047287517747753
- Alsudais, A. (2021). Incorrect data in the widely used Inside Airbnb dataset. Decision Support Systems, *141*, 113453. https://doi.org/10.1016/j.dss.2020.113453
- Batista e Silva, F., MarÃn Herrera, M. A., Rosina, K., Ribeiro Barranco, R., Freire, S., & Schiavina, M. (2018). Analysing spatiotemporal patterns of tourism in Europe at high-resolution with conventional and big data sources. Tourism Management, *68*, 101–115. https://doi.org/10.1016/j.tourman.2018.02.020
- Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, *53*(8), 1049–1064. https://doi.org/10.1016/j.im.2016.05.002
- Höpken, W., Eberle, T., Fuchs, M., & Lexhagen, M. (2019). Business intelligence for cross-process knowledge extraction at tourism destinations. Information Technology & Tourism, *21*, 479– 505. https://doi.org/10.1007/s40558-018-0129-4
- Li, J., Xu, L., Tang, L., Wang, S., & Li, L. (2018). Big data in tourism research: A literature review. Tourism Management, *68*, 301–323. https://doi.org/10.1016/j.tourman.2018.03.009
- Li, Y., Hu, C., Huang, C., & Duan, L. (2017). The concept of smart tourism in the context of tourism information services. Tourism Management, *58*, 293–300. https://doi.org/10.1016/j.tourman.2016.03.014
- Önder, I. (2017). Classifying multi-destination trips in Austria with big data. Tourism Management Perspectives, *21*, 54–58. https://doi.org/10.1016/j.tmp.2016.11.002
- Raun, J., Ahas, R., & Tiru, M. (2016). Measuring tourism destinations using mobile tracking data. Tourism Management, *57*, 202–212. https://doi.org/10.1016/j.tourman.2016.06.006
- Sun, S., Wei, Y., Tsui, K.-L., & Wang, S. (2019). Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, *70*, 1–10. https://doi.org/10.1016/j.tourman.2018.07.010
- Taecharungroj, V., & Mathayomchan, B. (2019). Analysing TripAdvisor reviews of tourist attractions in Phuket, Thailand. Tourism Management, *75*, 550–568. https://doi.org/10.1016/j.tourman.2019.06.020
- Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J.-F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, *70*, 356– 365. https://doi.org/10.1016/j.jbusres.2016.08.009
- Zervas, G., Proserpio, D., & Byers, J. W. (2017). The Rise of the Sharing Economy: Estimating the Impact of Airbnb on the Hotel Industry. Journal of Marketing Research, *54*(5), 687– 705. https://doi.org/10.1509/jmr.15.0204<a href='https://uhrenelite.net/'>replica uhren</a>