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John V. Colias

John V. Colias

John V. Colias

Affiliate Assistant Professor of Business Analytics at the University of Dallas and Senior Vice President with 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.

Track: Optimization

Tuesday, April 16, 10:30–11:20am

Combining Choice Modeling and Nonlinear Programming to Support Business Strategy Decisions

Using a case study with simulated data, we demonstrate how to integrate a choice model into a customer lifetime value (CLV) simulation and optimization tool. While the methodology is validated with AT&T data, due to the proprietary nature of the results, only results using simulated data will be presented.

Because the typical choice modeling study includes both nominal and numeric attributes as drivers of customer value, purchase probabilities, market share, and revenue, the nonlinear programming problem becomes non-trivial, requiring the use of state-of-the-art algorithms. Our solution would make use of several nonlinear programming algorithms using AMPL software.

From this presentation, industry experts will understand the features and benefits of choice modeling, required resources to implement and combine choice modeling and nonlinear programming, and the types of business strategy objectives that can be supported by combining Choice Modeling and Nonlinear Programming.