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Sensing Distress – Towards a Blended Method for Detecting and Responding to Problematic Customer Experience Events

  • Sue HesseyEmail author
  • Will Venters
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9751)

Abstract

Excellent Customer Experience (CE) is a strategic priority for many large service organisations in a competitive marketplace. CE should be seamless, and in most cases it is, with customers ordering, paying for and receiving services that align with their expectations. However, in rare cases, an exceptional process event leads to service delivery delay or failure, and both the customer and organisation end up in complex recovery situations as a result. Unless this recovery is handled effectively inefficiency, avoidable costs and brand damage can result. So how can organisations sense when these problems are occurring and how can they respond to avoid these negative consequences? Our paper proposes a blended methodology where process mining and qualitative user research combine to give a holistic picture of customer experience issues, derived from a particular customer case study. We propose a theoretical model for detecting and responding to customer issues, and discuss the challenges and opportunities of such a model when applied in practice in large service organisations.

Keywords

Customer experience Process mining HCI 

Notes

Acknowledgements

Thanks to Florian Allwein of LSE for additional data from advisor interviews and observations. Thanks also to William Harmer of BT for his Process Mining expertise.

References

  1. 1.
    Richardson, H.J., Howcroft, D.: The contradictions of CRM – a critical lens on call centres. Inf. Organ. 16(2), 143–168 (2006)CrossRefGoogle Scholar
  2. 2.
    Reichheld, F.: The one number you need to grow. Harvard Bus. Rev. 81(12), 46–55 (2003)Google Scholar
  3. 3.
    Duncan, E., Rawson, A., Jones, C.: The truth about customer experience. Harvard Bus. Rev. 91(9), 90–98 (2013)Google Scholar
  4. 4.
    Holziger, A.: Human-Computer Interaction and Knowledge Discovery (HCI-KDD): What is the benefit of bringing those two fields to work together? (HCI-IR) In: IADIS Multiconference on Computer Science and Information Systems (MCCSIS), Interfaces and Human-Computer Interaction, pp. 13–17 (2011)Google Scholar
  5. 5.
    Shneiderman, B.: Inventing discovery tools: combining information visualization with data mining. In: Jantke, K.P., Shinohara, A. (eds.) DS 2001. LNCS (LNAI), vol. 2226, pp. 17–28. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  6. 6.
    Seibt, J.: Process Philosophy. The Stanford Encyclopedia of Philosophy (2013)Google Scholar
  7. 7.
    Langley, A.: Strategies for theorizing from process data. Academy of management. Acad. Manage. Rev. 24(4), 691–710 (1999)MathSciNetGoogle Scholar
  8. 8.
    Corbin, J., Strauss, A.: Basics of Qualitative Research 3e, Chap. 8, pp. 163-164 (2008)Google Scholar
  9. 9.
    Nielsen, J., Molich, R.: Heuristics evaluation of user interfaces. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, p. 249 (1990)Google Scholar
  10. 10.
    Aalst, W.: Process Mining: Discovery, Conformance and Enhancement of Business Processes (preface). Springer, Heidelberg (2011)CrossRefzbMATHGoogle Scholar
  11. 11.
    Burattin, A.: Process mining. In: Burattin, A. (ed.) Process Mining Techniques in Business Environments. LNBIP, vol. 207, pp. 33–47. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  12. 12.
    Taylor, P., Leida, M., Majeed, B.: Case study in process mining in a multinational enterprise. In: Aberer, K., Damiani, E., Dillon, T. (eds.) SIMPDA 2011. LNBIP, vol. 116, pp. 134–153. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  13. 13.
    Reddy, M.C., Dourish, P., Pratt, W.: Temporality in medical work: time also matters. Comput. Support. Coop. Work (CSCW) 15(1), 29–53 (2006)CrossRefGoogle Scholar
  14. 14.
    Langley, A., et al.: Process studies of change in organization and management: unveiling temporality, activity, and flow. Acad. Manage. J. 56(1), 1–13 (2013)CrossRefGoogle Scholar
  15. 15.
    Lee, H.: Time and information technology: monochronicity, polychronicity and temporal symmetry. Eur. J. Inf. Syst. 8, 16–26 (1999)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.BT Plc Research and InnovationIpswichUK
  2. 2.London School of Economics and Political SciencesLondonUK

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