Optimizing Product Offers with Machine Learning and the Mixed Logit Model
Summary
How does one create an efficient analytical pipeline that begins with data for responses (sale or no sale) to real offers made to customers and ends with an optimal offer of product and price to any current or potential customer visiting a retail site? This presentation will outline an analytical pipeline that uses machine learning to optimize a mixed logit model and then applies nonlinear programming to optimize the offer. The advantage of the mixed logit model vs. other popular machine learning models is that the mixed logit model is grounded in economic theory and thus produces sound pricing recommendations. The predictive accuracy of the mixed logit model will be compared to that of other types of models used for machine learning. Data requirements, estimation and optimization methods will be presented with empirical evidence using transactions data.
Presenters
John Colias, Ph.D.
Senior VP Research & Development
As a leader with both university teaching and business consulting experience, John focuses on predictive modeling, prescriptive analytics, and artificial intelligence. As Senior Vice President, Research & Development, at Decision Analyst, John combines academic and business interests to help analytics professionals by offering cutting-edge analytic solutions tempered by business realism. He holds a doctorate in economics from The University of Texas at Austin, with specializations in econometrics and mathematical modeling methods.
