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In B2B markets, value-based pricing and selling has become an importantalternative to discounting. This study outlines a modeling method that usescustomer data (product offers made to each current or potential customer,features, discounts, and customer purchase decisions) to estimate a mixed logitchoice model. The model is estimated via hierarchical Bayes and machinelearning, delivering customer-level parameter estimates. Customer-levelestimates are input into a nonlinear programming next-offer maximizationproblem to select optimal features and discount level for customer segments,where segments are based on loyalty and discount elasticity. The mixed logitmodel is integrated with economic theory (the random utility model), and itpredicts both customer perceived value for and response to alternative futuresales offers. The methodology can be implemented to support value-based pricingand selling efforts.  Contributions to the literature include: (a) the use of customer-levelparameter estimates from a mixed logit model, delivered via a hierarchicalBayes estimation procedure, to support value-based pricing decisions; (b)validation that mixed logit customer-level modeling can deliver strongpredictive accuracy, not as high as random forest but comparing favorably; and(c) a nonlinear programming problem that uses customer-level mixed logitestimates to select optimal features and discounts.

Optimizing B2B Product Offers with Machine Learning, Mixed Logit, and Nonlinear Programming