Journal of Revenue and Pricing Management

, Volume 15, Issue 5, pp 334–351 | Cite as

Discovering patterns in traveler behaviour using segmentation

  • Aditya Kothari
  • Manini Madireddy
  • Ramasubramanian Sundararajan
Practice Article


We consider the problem of finding common behavioral patterns among travelers in an airline network through the process of clustering. Travelers can be characterized at relational or transactional level. In this article, we focus on the transactional level characterization; our unit of analysis is a single trip, rather than a customer relationship comprising multiple trips. We begin by characterizing a trip in terms of a number of features that pertain to the booking and travel behavior. Trips thus characterized are then grouped using an ensemble clustering algorithm that aims to find stable clusters as well as discover subgroup structures within groups. A multidimensional analysis of trips based on these groupings leads us to discover non-trivial patterns in traveler behaviour that can then be exploited for better revenue management.


segmentation data mining clustering 



This work presented in this article was done when Aditya Kothari was employed with Sabre Airline Solutions.


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Copyright information

© Macmillan Publishers Ltd 2016

Authors and Affiliations

  • Aditya Kothari
    • 1
  • Manini Madireddy
    • 2
  • Ramasubramanian Sundararajan
    • 3
  1. 1.Ather EnergyBangaloreIndia
  2. 2.Sabre Airline SolutionsSouthlakeUSA
  3. 3.Sabre Airline SolutionsBangaloreIndia

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