Optimizing B2B product offers with machine learning, mixed logit, and nonlinear programming

作     者：Colias, John V. Park, Stella Horn, Elizabeth 

作者机构：Univ Dallas Irving TX 75062 USA AT&T Dallas TX USA Decis Analyst Inc 604 Ave H East Arlington TX 76011 USA 

出 版 物：《JOURNAL OF MARKETING ANALYTICS》 (J. Marketing Analytics)

年 卷 期：2021年第9卷第3期

页      面：157-172页

学科分类：12[管理学] 120202[管理学-企业管理（含：财务管理、市场营销、人力资源管理）] 0202[经济学-应用经济学] 02[经济学] 1202[管理学-工商管理] 020205[经济学-产业经济学] 

主　　题：B2B Sales data Machine learning Mixed logit Nonlinear programming 

摘      要：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.