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

Abstract

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.

Keywords

segmentation data mining clustering 

Notes

Acknowledgements

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

References

  1. Bodea, T. and Ferguson, M. (2014) Segmentation, Revenue Management and Pricing Analytics. New York, USA: Routledge.Google Scholar
  2. Bottou, L. and Bengio, Y. (1995) Convergence properties of the k-means algorithm. In: G. Tesauro and D.S. Touretzky (eds.) Advances in Neural Information Processing Systems. Denver, Colorado, USA: MIT Press.Google Scholar
  3. Fred, A. and Jain, A.K. (2002) Evidence accumulation clustering based on the K-means algorithm. In: T. Caelli, A. Amin, R.P.W. Duin, M. Kamel and D. de Ridder (eds.) Proceedings of the International Workshops on Structural and Syntactic Pattern Recognition. Windsor, Canada: Springer-Verlag.Google Scholar
  4. Huang, Z. (1998) Extensions to the k-means algorithm for clustering large datasets with categorical values. Data Mining and Knowledge Discovery 2(3): 283–304.CrossRefGoogle Scholar
  5. Jain, A.K., Murthy, M.N. and Flynn, P.N. (1999) Data clustering: A review. ACM Computing Surveys 31(3): 264–323.CrossRefGoogle Scholar
  6. Khan, S.S. and Kant, S. (2007) Computation of initial modes for K-modes clustering algorithm using evidence accumulation. In: R. Sangal, H. Mehta and R.K. Bagga (eds.) Proceedings of the 20th international joint conference on Artifical intelligence. Hyderabad, India: Morgan Kaufmann Publishers Inc.Google Scholar
  7. Leick, R. (2007) Building Airline Passenger Loyalty Through an Understanding of Customer Value: A Relationship Segmentation of Airline Passengers. Ph.D. thesis, Cranfield, UK: Cranfield University.Google Scholar
  8. Liu, B., Xia, Y. and Yu, P.S. (2000) Clustering via decision tree construction. In: A. Agah, J. Callan, E. Rundensteiner and S. Gauch (eds.) Conference on Information & Knowledge Management. McLean, VA, USA: ACM.Google Scholar
  9. Maulik, U. and Bandyopadhyay, S. (2002) Performance evaluation of some clustering algorithms and validity indices. IEEE Transactions on Pattern Analysis And Machine Intelligence 24(12): 1650–1654.CrossRefGoogle Scholar
  10. Ramakrishnan, J., Sundararajan, R. and Singh, P. (2009) Behavioural segmentation of credit card customers. In: 1st IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence. Ahmedabad, India: IIM.Google Scholar
  11. Ratliff, R. and Gallego, G. (2013) Estimating sales and profitability impacts of airline branded-fares product design and pricing decisions using customer choice models. Journal of Revenue and Pricing Management 12(6): 509–523.CrossRefGoogle Scholar
  12. Sculley, D. (2010) Web-scale k-means clustering. In: M. Rappa and P. Jones (eds.) Proceedings of the World Wide Web Conference (WWW). Raleigh, NC, USA: ACM.Google Scholar
  13. Shebalov, S. (2014) Customer segmentation: Revisiting customer centricity for better analysis. Ascend (4).Google Scholar
  14. Teichert, T., Shehu, E. and von Wartburg, I. (2008) Customer segmentation revisited: The case of the airline industry. Transportation Research Part A: Policy and Practice 42(1): 227–242.Google Scholar
  15. Vinod, B. (2008) The continuing evolution: Customer-centric revenue management. Journal of Revenue and Pricing Management 7(1): 27–39.CrossRefGoogle Scholar
  16. Westermann, D. (2006) (Realtime) dynamic pricing in an integrated revenue management and pricing environment: An approach to handling undifferentiated fare structures in low-fare markets. Journal of Revenue & Pricing Management 4(4): 389–405.CrossRefGoogle Scholar
  17. Westermann, D. (2013) The potential impact of IATAs new distribution capability (NDC) on revenue management and pricing. Journal of Revenue and Pricing Management 12(6): 565–568.CrossRefGoogle Scholar
  18. Yankelovich, D. and Meer, D. (2006) Rediscovering market segmentation. Harvard Business Review 84(2): 122–131.Google Scholar

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

Personalised recommendations