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Enabling individualized recommendations and dynamic pricing of value-added services through willingness-to-pay data

Abstract

When managing their growing service portfolio, many manufacturers in B2B markets face two significant problems: They fail to communicate the value of their service offerings and they lack the capability to generate profits with value-added services. To tackle these two issues, we have built and evaluated a collaborative filtering recommender system which (a) makes individualized recommendations of potentially interesting value-added services when customers express interest in a particular physical product and also (b) leverages estimations of a customer’s willingness to pay to allow for a dynamic pricing of those services and the incorporation of profitability considerations into the recommendation process. The recommender system is based on an adapted conjoint analysis method combined with a stepwise componential segmentation algorithm to collect individualized preference and willingness-to-pay data. Compared to other state-of-the-art approaches, our system requires significantly less customer input before making a recommendation, does not suffer from the usual sparseness of data and cold-start problems of collaborative filtering systems, and, as is shown in an empirical evaluation with a sample of 428 customers in the machine tool market, does not diminish the predictive accuracy of the recommendations offered.

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Acknowledgement

This publication was written in the context of the research project ServPay. ServPay was funded by the German Federal Ministry of Education and Research (BMBF), funding number 02PG1010, and managed by the Project Management Agency Karlsruhe (PTKA). The authors are responsible for the content of this publication.

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Correspondence to Daniel Beverungen.

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Responsible editor: Martin Spann

Appendix

Appendix

Fig. 7
figure7

Sampling distributionturnover

Fig. 8
figure8

Sampling distributionnumber of employees

Table 3 Services and service levels assessed in the study
Table 4 Characteristics of the ServPay Conjoint Analysis
Table 5 Assumptions of the ServPay Conjoint Analysis
Fig. 9
figure9

Stepwise componential segmentation algorithm

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Backhaus, K., Becker, J., Beverungen, D. et al. Enabling individualized recommendations and dynamic pricing of value-added services through willingness-to-pay data. Electron Markets 20, 131–146 (2010). https://doi.org/10.1007/s12525-010-0032-0

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Keywords

  • Collaborative filtering
  • Dynamic pricing
  • Willingness-to-pay
  • Service science
  • Design science

JEL

  • M31–Marketing