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

TL;DR

This paper presents a comprehensive method combining customer data, mixed logit modeling, and nonlinear programming to optimize B2B product offers, enhancing value-based pricing strategies with predictive accuracy and segment-specific customization.

Contribution

It introduces a novel integration of customer-level mixed logit estimates with nonlinear programming for offer optimization, supported by hierarchical Bayes estimation and validation of predictive performance.

Findings

Customer-level mixed logit models predict customer responses effectively.

The integrated approach improves offer customization and pricing accuracy.

Model validation shows competitive predictive accuracy compared to other methods.

Abstract

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.