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Customer Experience Analytics in Insurance: Trajectory, Service Interaction and Contextual Data

  • Gilles BeaudonEmail author
  • Eddie Soulier
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 918)

Abstract

The insurance market and particularly French Health-Insurers are affected by changes. That require from traditional actors to transform their Customer Experience management. Our paper, which relies on the third French mutual health-insurer, explores Customer Experience Analytics issues. We observed these issues throughout workshops focused on designing Customer Experience. Our observations show analytics based on Customer Journey Application which make use of decontextualized interactions. We approach the fact these analytics must apply the concept of trajectory as primary focus for user engagement. In order to articulate information system and human activity trajectories, we develop the “Contextualizing Artifact”. It is grounded on the Context-System-Trajectory theory (CST). That new theory is mandatory to grasp Customer Experience beyond its marketing dimension. As first step of our artifact development we explain how to improve Customer Journey application with a combination of contextual dataset and classification techniques. This proposal relies on Service Interaction pattern (NISPARO) and provides new qualitative analytics.

Keywords

Customer experience management Customer journey application Information system trajectory Context theory Service interactions modeling Classification techniques Failure mode and effects analysis 

References

  1. 1.
    Perrin, G., Mutuelles: La concentration du secteur se poursuit, L’Argus de l’assurance, April 2017Google Scholar
  2. 2.
    Holbrook, M.B., Hirschman, E.C.: The experiential aspects of consumption: consumer fantasies, feelings and fun. J. Consum. Res. 9(2), 132–140 (1982)CrossRefGoogle Scholar
  3. 3.
    Pine II, B.J., Gilmore, J.H.: Welcome to the experience economy. Harvard Bus. Rev. 76, 97–105 (1998)Google Scholar
  4. 4.
    Flacandji, M.: Du souvenir de l’expérience à la relation à l’enseigne: une exploration théorique et méthodologique dans le domaine du commerce de détail, Thesis, Gestion et management, Université de Bourgogne, Dijon (2015)Google Scholar
  5. 5.
    Barwitz, N., Maas, P.: Understanding the Omnichannel customer journey: determinants of interaction choice. J. Interact. Mark. 43, 116–133 (2018)CrossRefGoogle Scholar
  6. 6.
    Huang, Y., Zhang, L., Zhang, P.: A framework for mining sequential patterns from spatio-temporal event data sets. IEEE Trans. Knowl. Data Eng. 20(4), 433–448 (2008)CrossRefGoogle Scholar
  7. 7.
    Gotz, D., Stavropoulos, H.: DecisionFlow: visual analytics for high-dimensional temporal event sequence data. IEEE Trans. Vis. Comput. Graph. 20(12), 1783–1792 (2014)CrossRefGoogle Scholar
  8. 8.
    Li, H., Sheopuri, A., Yi, J., Yu, Q.: Feature learning on customer journey using categorical sequence data. US20170293919A1, October 2017Google Scholar
  9. 9.
    Mathisen, A., Grønbæk, K.: Clear visual separation of temporal event sequences. In: IEEE Symposium on Visualization in Data Science, October 2017Google Scholar
  10. 10.
    Wongsuphasawat, K., Guerra Gómez, J.A., Plaisant, C., Wang, T.D., Taieb-Maimon, M., Shneiderman, B.: LifeFlow: visualizing an overview of event sequence. In: SIGCHI Conference on Human Factors in Computing Systems, New York, pp. 1747–1756 (2011)Google Scholar
  11. 11.
    Spaulding, T.J., Furukawa, M.F., Raghu, T.S., Vinze, A.: Event sequence modeling of IT adoption in healthcare. Decis. Support Syst. 55(2), 428–437 (2013)CrossRefGoogle Scholar
  12. 12.
    Winters, G., Elshof, J., Shmelev, A.: Process and system to categorize, evaluate and optimize a customer experience. US20170308917A1, 26 October 2017Google Scholar
  13. 13.
    Cordewener, M.H.H.: Customer journey identification through temporal patterns and Markov clustering—Eindhoven University of Technology research portal, Eindhoven University of Technology (2016)Google Scholar
  14. 14.
    Moschetti-Jacob, F.: Création d’un artefact modulaire d’aide à la conception de parcours client cross-canal visant à développer les capacités des managers des entreprises du secteur du commerce. Thesis, Paris-Dauphine (2016)Google Scholar
  15. 15.
    Hevner, A.R., March, S.T., Park, J., Ram, S.: Design science in information systems research. MIS Q. 28(1), 75–105 (2004)CrossRefGoogle Scholar
  16. 16.
    Becker, H.S.: Notes on the concept of commitment. Am. J. Soc. 66(1), 32–40 (1960)CrossRefGoogle Scholar
  17. 17.
    Thévenot, L.: Pragmatic regimes governing the engagement with the world. In: Knorr-Cetina, K., Schatzki, T., Savigny Eike, V. (eds.) The Practice Turn in Contemporary Theory (2001)Google Scholar
  18. 18.
    Strauss, A.L.: La trame de la négociation: sociologie qualitative et interactionnisme. Éditions L’Harmattan, Paris (1992)Google Scholar
  19. 19.
    Leonardi, P.M., Nardi, B.A., Kallinikos, J.: Materiality and Organizing: Social Interaction in a Technological World, 1st edn. Oxford University Press, Oxford (2012)CrossRefGoogle Scholar
  20. 20.
    Barad, K.M.: Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning. Duke University Press, Durham (2007)CrossRefGoogle Scholar
  21. 21.
    Orlikowski, W.J.: Sociomaterial practices: exploring technology at work. Organ. Stud. 28(9), 1435–1448 (2007)CrossRefGoogle Scholar
  22. 22.
    Orlikowski, W.J.: The sociomateriality of organisational life: considering technology in management research. Camb. J. Econ. 34(1), 125–141 (2010)CrossRefGoogle Scholar
  23. 23.
    Orlikowski, W.J., Scott, S.: The entangling of technology and work in organizations. Working Paper Series, Department of Management Information Systems and Innovation Group, London School of Economics, January 2008Google Scholar
  24. 24.
    Oiry, E., et al.: Propositions pour un cadre théorique unifié et une méthodologie d’analyse des trajectoires des projets dans les organisations. Manag. Avenir 36(6), 84 (2010)CrossRefGoogle Scholar
  25. 25.
    Pettigrew, A.M.: What is a processual analysis? Scand. J. Manag. 13(4), 337–348 (1997)CrossRefGoogle Scholar
  26. 26.
    Van de Ven, A.H., Poole, M.S.: Explaining development and change in organizations. Acad. Manag. Rev. 20(3), 510–540 (1995)CrossRefGoogle Scholar
  27. 27.
    Abbott, A.D.: Time Matters: On Theory and Method. University of Chicago Press, Chicago (2001)Google Scholar
  28. 28.
  29. 29.
    Schneider, A.: Handling the Clash Between Production & Consumption: A Situated View on Front-line Service Workers’ Competencies in Interactive Service, Empirische Personal- und Organisationsforschung, vol. 55. Rainer Hampp (2016)Google Scholar
  30. 30.
    Akman, V., Surav, M.: The use of situation theory in context modeling. Comput. Intell. 13(3), 427–438 (1997)CrossRefGoogle Scholar
  31. 31.
    Spohrer, J., Vargo, S.L., Caswell, N., Maglio, P.P.: The service system is the basic abstraction of service science. In: Proceedings of 41st Annual International Conference on System Sciences, Hawaii, p. 104 (2008)Google Scholar
  32. 32.
    Dey, A.K.: Understanding and using context. Pers. Ubiquit. Comput. 5(1), 4–7 (2001)CrossRefGoogle Scholar
  33. 33.
    Beckett, R.: Service ecosystems supporting high reliability assets. Systems 5(4), 32 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Tech-CICO Team, Université de Technologie de Troyes (UTT)TroyesFrance

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