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

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.