Professional
Track: Machine Learning
Optimizing Product Offers with Machine Learning and the Mixed Logit Model
Tuesday, April 13, 1:45-2:25pm EDT
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.
John Colias
John Colias
Affiliate Assistant Professor of Business Analytics at the University of Dallas & Senior Vice President at Decision Analyst
As a thought leader in advanced analytics, John focuses on predictive modeling, forecasting, and marketing research. As Senior Vice President with Decision Analyst, he helps his clients integrate data and modeling methods, draw valid conclusions, make better business decisions, and improve marketing effectiveness.
John also teaches and conducts research as an Affiliate Assistant Professor of Business Analytics at the University of Dallas, where he is also Director of Master of Science in Business Analytics Program. His combination of academic and business interests helps analytics professionals to offer cutting-edge analytic solutions tempered by business realism.
John holds a doctorate in economics from The University of Texas at Austin, with specializations in econometrics and mathematical modeling methods. He has been a frequent conference presenter of advanced modeling methods over the past 30 years.
Beth Horn
Beth Horn
Senior Vice President, Advanced Analytics at Decision Analyst
Beth has provided expertise and high-end analytics for Decision Analyst for over 20 years. She leads the Advanced Analytics Group and is responsible for design, analyses and insights derived from discrete choice models, MaxDiff analyses, volumetric forecasting and other advanced analytics. She specializes in product and pricing optimization and market segmentation. Beth is consulted frequently by clients and internal account teams regarding best practices in research methodology.
Her analytic experience spans several industries, such as food and beverage, personal care, high tech, home improvement, appliances and other durables, pharmaceuticals, and travel/hospitality. She has published articles in marketing publications and refereed psychological and statistical journals.
Beth earned a Ph.D. and a Master of Science in Experimental Psychology with emphasis on psychological principles, research methods, and statistics from Texas Christian University, Fort Worth, TX.
