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

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.

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Fig. 1
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Fig. 3

Notes

  1. 1.

    An overview of the mathematical notation is provided in Table 6 (cf. Appendix 1).

  2. 2.

    It is generally possible to define the share of cash flows tracing back to influence in the network as a customer and/or period specific parameter. To do so, the parameter α may for example be replaced by the parameter \( {\alpha}_t^j \) ∈ [0, 1[ representing the share of customer j’s cash flows in period t, which traces back to the influence of other customers in the network. By means of the parameter \( {\alpha}_t^j \) it can be considered that some customers in the network may be more susceptible to social influence than others and that this fact may vary over time. For reasons of simplicity, we refrain from this differentiation at this point.

  3. 3.

    Vgl. http://networkx.github.io/

  4. 4.

    Results for the CLNV below 0.01€ were rounded to zero.

  5. 5.

    Please note that, while the presented user segmentation seems suitable for a first hand classification of users in relation to other users, an in-depth analysis as well as a long-term application of the segmentation should also put a stronger focus on absolute values.

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Appendices

Appendix 1

Table 6

Table 6 Overview of the mathematical notations

Appendix 2

We additionally carried out the calculation of the CLNV for the time frame of 5 days (cf. Tables 7 and 8, Fig. 4) and the time frame of 7 days (cf. Tables 9 and 10, Fig. 5).

Table 7 Results of the application (time frame =5 days, n = 1,287 users)
Table 8 Comparison of top user groups for the CLNV and the CLV (time frame =5 days, n = 1,287 users)
Fig. 4
figure4

CLNV-based user segments (time frame =5 days, n = 1,287 users)

Table 9 Results of the application (time frame =7 days, n = 1,470 users)
Table 10 Comparison of top user groups for the CLNV and the CLV (time frame =7 days, n = 1,470 users)
Fig. 5
figure5

CLNV-based user segments (time frame =7 days, n = 1,470 users)

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Däs, M., Klier, J., Klier, M. et al. Customer lifetime network value: customer valuation in the context of network effects. Electron Markets 27, 307–328 (2017). https://doi.org/10.1007/s12525-017-0255-4

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Keywords

  • Customer valuation
  • Customer lifetime value
  • Social influence
  • Network effects

JEL classification

  • M10