Incorporating Willingness-to-Pay Data into Online Recommendations for Value-Added Services

Backhaus Klaus, Becker Jörg, Beverungen Daniel, Frohs Margarete, Müller Oliver, Weddeling Matthias


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 to their customers, and they lack the capabilities to generate profits with value-added services. To tackle these two issues, we design and evaluate 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) obtains estimations of a customer's willingness-to-pay to allow for a dynamic, value-based pricing of those services. The recommender system is based on an adapted conjoint analysis method combined with a stepwise componential segmentation algorithm to collect preference and willingness-to-pay data for value-added services. Compared to other conjointbased recommendation approaches, our system requires significantly less customer input before making a recommendation and at the same time does not suffer from the usual cold-start problem of
recommender systems. And, as is shown in an empirical evaluation with a representative sample of 428 customers in the machine tool market, our approach does not diminish the predictive accuracy of the recommendations offered.

Keywords
Model-Based Recommendations; Service Science; Design Science; E-Commerce (B2B)



Publication type
Forschungsartikel in Sammelband (Konferenz)

Peer reviewed
Yes

Publication status
Published

Year
2010

Conference
18th European Conference on Information Systems

Venue
Pretoria, South Africa

Language
English

Full text