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Predictive Systems for Customer Interactions

  • Ravi Vijayaraghavan
  • Sam Albert
  • Vinod Kumar Singh
  • Pallipuram V. Kannan
Chapter
Part of the Service Science: Research and Innovations in the Service Economy book series (SSRI)

Abstract

With the coming of age of web as a mainstream customer service channel, B2C companies have invested substantial resources in enhancing their web presence. Today customers can interact with a company, not only through the traditional phone channel but also through chat, email, SMS or web self-service. Each of these channels is best suited for some services and ill-matched for others. Customer service organizations today struggle with the challenge of delivering seamlessly integrated services through these different channels. This paper will evaluate some of the key challenges in multi-channel customer service. It will address the challenge of creating the right channel mix i.e. providing the right choice of channels for a given customer/behavior/issue profile. It will also provide strategies for optimizing the performance of a given channel in creating the right customer experience.

Keywords

Service systems predictive systems customer interactions call center contact center self-service text mining channel optimization customer experience customer satisfaction sentiment analysis online customer service customer care 

Notes

Acknowledgments

The authors would like to thank members of the Innovation Labs team at 24/7 Customer for various insightful discussions.

References

  1. Albert and Vijayaraghavan (2011). Net Experience Score – a measure of customer experience, paper in progress.Google Scholar
  2. Bernoff and Li (2008). Harnessing the power of the Oh-So-Social Web, MIT Sloan Management Review.Google Scholar
  3. Cheng, Boyette and Krishna (2006). Towards a low-cost high-quality service call architecture, Services Computing, SCC ’06. IEEE International Conference, Chicago, IL, pp 261–264.Google Scholar
  4. Chuang and Chien (2003). Enriching web taxonomies through subject categorization of query terms from search engine logs, Decision Support Systems, 35(1), 113–127.CrossRefGoogle Scholar
  5. Cronin, Brady and Hult (2000). Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments, Journal of Retailing, 76(2), 193–218.CrossRefGoogle Scholar
  6. Cross, Liedtka and Weiss (2005). A practical guide to social networks, Harvard Business Review, 83(3), 124–132.Google Scholar
  7. Domingos, P., and Pazzani, M. (1997). Beyond independence: Conditions for the optimality of the simple Bayesian classifier. Machine Learning 29:103–130.zbMATHCrossRefGoogle Scholar
  8. Fellbaum (1998) (Ed) WordNet: An Electronic Lexical Database, MIT Press, Cambridge, MA.zbMATHGoogle Scholar
  9. IfM and IBM (2008). Succeeding through service innovation: A service perspective for education, research, business and government. Cambridge: University of Cambridge, Institute for Manufacturing. ISBN: 978-1-902546-65-0.Google Scholar
  10. Lebow, J. L. (1982). Consumer satisfaction with mental health treatment. Psychological Bulletin 91, 244–259.CrossRefGoogle Scholar
  11. Lee and Lin (2005). Customer perceptions of e-service quality in online shopping, International Journal of Retail & Distribution Management, 33(2), 161–176.CrossRefGoogle Scholar
  12. Lennon and Harris (2002). Customer service on the Web: A cross-industry investigation, Journal of Targeting, Measurement and Analysis for Marketing, 10(4), 325–338.CrossRefGoogle Scholar
  13. LeVois, M., Nguyen, T. D. and Attkisson, C. (1981). Artifact in client satisfaction assessment: Experience in community mental health settings. Evaluation and Program Planning 4 (April): 139–I50.CrossRefGoogle Scholar
  14. Liu and Singh (2004). ConceptNet – a practical commonsense reasoning tool-kit, BT Technology Journal, 22(4).CrossRefGoogle Scholar
  15. Liu, Lieberman, and Selker (2003). A model of textual affect sensing using real-world knowledge. In Proceedings of IUI’03, Miami, FL, pp 125–132.Google Scholar
  16. Maglio, Srinivasan, Kreulen, and Sphorer (2006). Service Systems, Service Scientists, SSME, and Innovation, Communications of the ACM, 49(7).CrossRefGoogle Scholar
  17. Meuter, Ostrom, Roundtree, and Bitner (2000). Self-service technologies: Understanding customer satisfaction with technology-based service encounters, Journal of Marketing, 64, 50–64.CrossRefGoogle Scholar
  18. Pang and Lee (2008). Opinion mining and sentiment analysis, Foundations and Trends in Information Retrieval, 2(1–2), 1–135.CrossRefGoogle Scholar
  19. Peterson and Wilson (1992). Measuring customer satisfaction: Fact and artifact, Journal of the Academy of Marketing Science, 20(1), 61–71.CrossRefGoogle Scholar
  20. Piccoli, Brohman, Watson and Parasuraman (2004). Net-based customer service systems: Evolution and revolution in web site functionalities, Decision Sciences, 35(3), 423–455.CrossRefGoogle Scholar
  21. Previsor (2008). The US Contact Center Operational Review (2008). Whitepaper, 2nd Edition.Google Scholar
  22. Reichheld (2006). The ultimate question – Driving good profits and true growth, Harvard Business School Press, Boston, MA.Google Scholar
  23. Rust, Zahorik and Keiningham (1995). Return on quality (ROQ): Making service quality financially accountable, Journal of Marketing, 59, 58–70.CrossRefGoogle Scholar
  24. Rust, R., Zeithaml, V. and Lemon, K. (2000). Driving customer equity: How customer lifetime value is reshaping corporate strategy. Free Press, New York.Google Scholar
  25. Salomann, Kolbe, and Brenner (2005). Self-Services in customer relationships: Balancing high-tech and high-touch today and tomorrow, e-Service Journal 4(2), 65–84.CrossRefGoogle Scholar
  26. Sousa and Voss (May 2006). Service quality in multichannel services employing virtual channels, Journal of Services Research, 8(4), 356–371.CrossRefGoogle Scholar
  27. Sphorer, Maglio, Bailey and Gruhl (2006). Steps toward a science of service systems, Communications of the ACM, 49(7).Google Scholar
  28. Szymanski and Henard (2001). Customer satisfaction: A meta-analysis of the empirical evidence, Journal of the Academy of Marketing Science, 29(1).Google Scholar
  29. Taboada and Grieve (2004). In Proc. of AAAI Spring Symposium on Exploring Attitude and Affect in Text, Stanford, CA, pp. 158–161.Google Scholar
  30. Witten and Frank (2005), Data mining – Practical machine learning tools and techniques, 2nd Edition. Elsevier, Amsterdam.zbMATHGoogle Scholar

Copyright information

© Springer US 2011

Authors and Affiliations

  • Ravi Vijayaraghavan
    • 1
  • Sam Albert
    • 1
  • Vinod Kumar Singh
    • 1
  • Pallipuram V. Kannan
    • 2
  1. 1.24/7 Customer Innovation LabsBangaloreIndia
  2. 2.24/7 Customer IncBangaloreIndia

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