Predictive Systems for Customer Interactions

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


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.


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 



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


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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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