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Customer lifetime network value: customer valuation in the context of network effects

Author

Listed:
  • Miriam Däs

    (University of Regensburg)

  • Julia Klier

    (University of Regensburg)

  • Mathias Klier

    (University of Ulm)

  • Georg Lindner

    (University of Regensburg)

  • Lea Thiel

    (University of Regensburg)

Abstract

Nowadays customers are increasingly connected and extensively interact with each other using technology-enabled media like online social networks. Hence, customers are frequently exposed to social influence when making purchase decisions. However, established approaches for customer valuation mostly neglect network effects based on social influence. This leads to a misallocation of resources. Following a design-oriented approach, this paper develops a model for customer valuation referred to as the customer lifetime network value (CLNV) incorporating an integrated network perspective. By considering the customers’ net contribution to the network, the CLNV reallocates values between customers based on social influence. Inspired by common prestige- and eigenvector-related centrality measures it incorporates social influence among all degrees of separation acknowledging its viral spread. Using a real-world dataset, we demonstrate the practicable applicability of the CLNV to determine individual customers’ value.

Suggested Citation

  • Miriam Däs & Julia Klier & Mathias Klier & Georg Lindner & Lea Thiel, 2017. "Customer lifetime network value: customer valuation in the context of network effects," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(4), pages 307-328, November.
  • Handle: RePEc:spr:elmark:v:27:y:2017:i:4:d:10.1007_s12525-017-0255-4
    DOI: 10.1007/s12525-017-0255-4
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    1. V. Kumar & Rajkumar Venkatesan & Tim Bohling & Denise Beckmann, 2008. "—The Power of CLV: Managing Customer Lifetime Value at IBM," Marketing Science, INFORMS, vol. 27(4), pages 585-599, 07-08.
    2. Raghuram Iyengar & Christophe Van den Bulte & Thomas W. Valente, 2011. "Opinion Leadership and Social Contagion in New Product Diffusion," Marketing Science, INFORMS, vol. 30(2), pages 195-212, 03-04.
    3. Dipak C. Jain & Naufel J. Vilcassim, 1991. "Investigating Household Purchase Timing Decisions: A Conditional Hazard Function Approach," Marketing Science, INFORMS, vol. 10(1), pages 1-23.
    4. Hinz, Oliver & Skiera, Bernd & Barrot, Christian & Becker, Jan, 2011. "Seeding Strategies for Viral Marketing: An Empirical Comparison," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 56543, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    5. Anonymous, 2014. "Introduction to the Issue," Journal of Wine Economics, Cambridge University Press, vol. 9(1), pages 1-2, May.
    6. Bettina Lis & Christian Neßler, 2014. "Electronic Word of Mouth," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(1), pages 63-65, February.
    7. Hinz, Oliver & Schulze, Christian & Takac, Carsten, 2014. "New product adoption in social networks: Why direction matters," Journal of Business Research, Elsevier, vol. 67(1), pages 2836-2844.
    8. Kristine de Valck & Gerrit H. van Bruggen & Berendt Wierenga, 2009. "Virtual communities: A marketing perspective," Post-Print hal-00458421, HAL.
    9. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    10. Herr, Paul M & Kardes, Frank R & Kim, John, 1991. "Effects of Word-of-Mouth and Product-Attribute Information on Persuasion: An Accessibility-Diagnosticity Perspective," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 17(4), pages 454-462, March.
    11. Kristiaan Helsen & David C. Schmittlein, 1993. "Analyzing Duration Times in Marketing: Evidence for the Effectiveness of Hazard Rate Models," Marketing Science, INFORMS, vol. 12(4), pages 395-414.
    12. Michael L. Katz & Carl Shapiro, 1994. "Systems Competition and Network Effects," Journal of Economic Perspectives, American Economic Association, vol. 8(2), pages 93-115, Spring.
    13. Hogan, John E. & Lemon, Katherine N. & Libai, Barak, 2004. "Quantifying the Ripple: Word-of-Mouth and Advertising Effectiveness," Journal of Advertising Research, Cambridge University Press, vol. 44(3), pages 271-280, September.
    14. Anonymous, 2014. "Introduction to the Issue," Journal of Wine Economics, Cambridge University Press, vol. 9(2), pages 109-110, August.
    15. Scarpi, Daniele & Pizzi, Gabriele & Visentin, Marco, 2014. "Shopping for fun or shopping to buy: Is it different online and offline?," Journal of Retailing and Consumer Services, Elsevier, vol. 21(3), pages 258-267.
    16. Florian Probst & Laura Grosswiele & Regina Pfleger, 2013. "Who will lead and who will follow: Identifying Influential Users in Online Social Networks," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 5(3), pages 179-193, June.
    17. Stefan Stieglitz & Linh Dang-Xuan & Axel Bruns & Christoph Neuberger, 2014. "Social Media Analytics," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 6(2), pages 89-96, April.
    18. Verhoef, Peter C. & Lemon, Katherine N., 2013. "Successful customer value management: Key lessons and emerging trends," European Management Journal, Elsevier, vol. 31(1), pages 1-15.
    19. Mauro Bampo & Michael T. Ewing & Dineli R. Mather & David Stewart & Mark Wallace, 2008. "The Effects of the Social Structure of Digital Networks on Viral Marketing Performance," Information Systems Research, INFORMS, vol. 19(3), pages 273-290, September.
    20. Malthouse, Edward C. & Haenlein, Michael & Skiera, Bernd & Wege, Egbert & Zhang, Michael, 2013. "Managing Customer Relationships in the Social Media Era: Introducing the Social CRM House," Journal of Interactive Marketing, Elsevier, vol. 27(4), pages 270-280.
    21. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
    22. Robert M. Bond & Christopher J. Fariss & Jason J. Jones & Adam D. I. Kramer & Cameron Marlow & Jaime E. Settle & James H. Fowler, 2012. "A 61-million-person experiment in social influence and political mobilization," Nature, Nature, vol. 489(7415), pages 295-298, September.
    23. Duncan J. Watts & Peter Sheridan Dodds, 2007. "Influentials, Networks, and Public Opinion Formation," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 34(4), pages 441-458, May.
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    Cited by:

    1. Rainer Alt, 2017. "Electronic markets on transaction costs," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(4), pages 297-301, November.

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    More about this item

    Keywords

    Customer valuation; Customer lifetime value; Social influence; Network effects;
    All these keywords.

    JEL classification:

    • M10 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - General

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