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

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