Optimization with Choice Modeling

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Why Choice Modeling?

Consumers can tell us what they will buy and what they will not buy. But rarely can they tell us what roles price, brand image, package color, brand name, promotional offers, and media advertising play in their purchase decisions. With choice modeling experiments, however, we can implicitly measure the influence of these marketing variables.

Consumers must make brand “choices” when they make purchases. Choice modeling simulates their shopping and decision-making process, with all of the important variables carefully controlled by rigorous experimental design, so that the role and importance of each marketing input can be determined.

“Choice modeling” refers to a family of statistical optimization techniques. Common applications are:

  • Pricing Optimization
  • Product Design Optimization
  • Product-Line Planning
  • Market Segmentation
  • Brand Strategy Optimization
  • Package Design Optimization
  • Advertising Optimization
  • New Product Concept Optimization
  • Promotion Offer Optimization


Jerry W. Thomas

Jerry W. Thomas

Chief Executive Officer

Jerry founded Decision Analyst in September 1978. The firm has grown over the years and is now one of the largest privately held, employee-owned research agencies in North America. The firm prides itself on mastery of advanced analytics, predictive modeling to maximize learning from research studies, and the development of leading-edge analytic software.

Jerry is deeply involved in the firm’s development of new research methods and techniques and in the design of new software systems. He plays a key role in the development of Decision Analyst’s proprietary research services and related mathematical models.

Jerry describes himself as a student of marketing strategy, new product development, mathematical modeling, business survival, and economic growth. In his spare time, he likes to work on his farm in East Texas where he grows grapes, apples, pears, pecans, plums, and peaches; a forest of native trees, grasses, and insects; and wild plants of many types.

He graduated from the University of Texas at Arlington, earned his MBA at the University of Texas at Austin, and studied graduate economics at SMU.

Elizabeth Horn

Elizabeth Horn

Senior VP, Advanced Analytics

Beth has provided expertise and high-end analytics for Decision Analyst for over 25 years. She is responsible for design, analyses, and insights derived from discrete choice models; MaxDiff analysis; volumetric forecasting; predictive modeling; GIS analysis; and market segmentation. She regularly consults with clients regarding best practices in research methodology. 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 in Fort Worth, TX.