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Understanding and predicting passenger behaviours through data analytics

Author

Listed:
  • Papagiannopoulos, Nikolaos

    (Senior Project Manager, IT&T Data Services, Information Technology & Telecommunications, Athens International Airport, Greece)

  • Lopez, Juan Francisco Garcia

Abstract

Passenger satisfaction is currently one of the most significant key performance indicators (KPIs) for the benchmarking of airports and airlines. It is also a source of valuable insights on the visibility of the process taking place and its effectiveness and efficiency. Therefore, it can be considered as a tool that can provide — depending on the data sources incorporated — not only understanding and awareness of the airport, passenger and airline processes, but also detailed analysis, trends, pattern recognition and actionable initiatives in areas such as retail revenue development and marketing initiatives based on personal preferences. The aim of this paper is to try to break down and provide a list of how Athens International Airport together with Indra, Spain, in the context of the biggest Horizon 2020 European Union (EU) funded data analytics research project for the transport sector, the Transforming Transport project, approaches airport data analytics and the methodology employed and provide a description of some of the use cases that will be considered. This paper can serve as a starting point for organisations in their quest for airport data analytics.

Suggested Citation

  • Papagiannopoulos, Nikolaos & Lopez, Juan Francisco Garcia, 2018. "Understanding and predicting passenger behaviours through data analytics," Journal of Airport Management, Henry Stewart Publications, vol. 13(1), pages 39-56, December.
    • Handle: RePEc:aza:jam000:y:2018:v:13:i:1:p:39-56
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    Cited by:

    1. Marco Cioppi & Ilaria Curina & Barbara Francioni & Elisabetta Savelli, 2023. "Digital transformation and marketing: a systematic and thematic literature review," Italian Journal of Marketing, Springer, vol. 2023(2), pages 207-288, June.

    More about this item

    Keywords

    data analytics; big data predictive analytics; airport data analytics; digitalisation; airport digital transformation;
    All these keywords.

    JEL classification:

    • R4 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics
    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General
    • M1 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration
    • M10 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - General

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