Service Analytics

Part of the Service Science: Research and Innovations in the Service Economy book series (SSRI)


Service analytics describes the process of capturing, processing, and analyzing the data generated from the execution of a service system to improve, extend, and personalize a service to create value for both providers and customers. This chapter explains how services, especially electronic services, generate a wealth of data which can be used for their analysis. The main tasks and methods, from areas such as data mining and machine learning, which can be used for analysis are identified. To illustrate their application, the data generated from the execution of an IT service is analyzed to extract business insights.


Analytical Services Information Technology Infrastructure Library (ITIL) Service User Studies Study Customer Behavior Page Visits 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Van Bon J, de Jong A, Kolthof A (2007) Foundations of IT Service Management based on ITIL. Van Haren Publishing, Zaltbommel. ISBN 9789087530570Google Scholar
  2. 2.
    Fromm H, Bloehdorn S (2014) Big data - technologies and potential. In: Enterprise integration, Chap 9. Springer, Berlin, pp 107–124CrossRefGoogle Scholar
  3. 3.
    Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Mag 17(3):37Google Scholar
  4. 4.
    Terry K (2013) Analytics: the nervous system of IT-enabled healthcare. Institute for Health Technology Transformation. Report
  5. 5.
    Groves P et al (2013) The Big Data revolution in healthcare. McKinsey & CompanyGoogle Scholar
  6. 6.
    Fromm H, Habryn F, Satzger G (2012) Service analytics: leveraging data across enterprise boundaries for competitive advantage. In: Globalization of professional services. Springer, Berlin, pp 139–149CrossRefGoogle Scholar
  7. 7.
    Han J, Kamber M, Pei J (2011) Data mining: concepts and techniques. The Morgan Kaufmann series in data management systems. Morgan Kaufmann, Los Altos, CAGoogle Scholar
  8. 8.
    Srivastava J et al (2000) Web usage mining: discovery and applications of usage patterns from web data. ACM SIGKDD Explor Newsl 1(2):12–23CrossRefGoogle Scholar
  9. 9.
    Kohavi R, Rothleder N, Simoudis E (2002) Emerging trends in business analytics. Comm ACM 45(8):45–48CrossRefGoogle Scholar
  10. 10.
    Davenport T (2006) Competing on analytics. Harv Bus Rev 84(1):98MathSciNetGoogle Scholar
  11. 11.
    Davenport T, Harris J (2007) Competing on analytics: the new science of winning. Harvard Business Press, Watertown, MAGoogle Scholar
  12. 12.
    Kobielus J (2010) The Forrester wave predictive analytics and data mining solutions, Q1 2010. Forrester Research, Cambridge, MAGoogle Scholar
  13. 13.
    Chaudhuri S, Dayal U (1997) An overview of data warehousing and OLAP technology. ACM SIGMOD Rec 26(1):65–74CrossRefGoogle Scholar
  14. 14.
    Witten I, Frank E, Hall M (2011) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Los Altos, CAGoogle Scholar
  15. 15.
    Wilson H, Keating B (2008) Business forecasting with business ForecastX, 6th edn. McGraw-Hill/Irwin, New York, 513 ppGoogle Scholar
  16. 16.
    Hanke JE, Wichern D (2008) Business forecasting, 9th edn. Prentice Hall, Englewood Cliffs, 576 ppGoogle Scholar
  17. 17.
    Analytics (2015) INFORMS online. Accessed: 2015-10-17
  18. 18.
    Gerke K, Cardoso J, Claus A (2009) Measuring the compliance of processes with reference models. In: 17th international conference on cooperative information systems (CoopIS 2009). Springer, AlgarveGoogle Scholar
  19. 19.
    OGC (2007) ITIL service operation. ITIL Series. Stationery Office isbn: 978-0113310463Google Scholar
  20. 20.
    Paszkiewicz Z, Picard W (2013) Analysis of the Volvo IT incident and problem handling processes using process mining and social network analysis. In: van Dongen B et al (eds) CEUR online proceedings, 2013. Proceedings of the 3rd business process intelligence challenge co-located with 9th international business process intelligence workshop (BPI 2013)Google Scholar
  21. 21.
    Hall M (1998) Correlation-based feature subset selection for machine learning. Ph.D. thesis. University of Waikato, HamiltonGoogle Scholar
  22. 22.
    Cardoso J, Lopes R, Poels G (2014) Service systems: concepts, modeling, and programming. Springer, BerlinCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Informatics EngineeringUniversidade de CoimbraCoimbraPortugal
  2. 2.Huawei European Research Center (ERC)MunichGermany
  3. 3.Karlsruhe Service Research Institute (KSRI)Karlsruhe Institute of Technology (KIT)KarlsruheGermany

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