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