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

Klaus Backhaus, Jörg Becker, Daniel Beverungen, Margarethe Frohs, Oliver Müller, Matthias Weddeling, Ralf Knackstedt, Michael Steiner

Research output: Journal Article or Conference Article in JournalJournal articleResearchpeer-review

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-ofthe-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.
Original languageEnglish
JournalElectronic Markets
Volume20
Issue number2
Pages (from-to)131-146
ISSN1019-6781
Publication statusPublished - 2010

Keywords

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

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