December 3, 2020 | Article

Vega expert Professor David Gal from the University of Illinois at Chicago provides insight into the limits of artificial intelligence, machine learning, and big data, as well as those of traditional survey and experimental methods, in predicting consumer choices. The article, co-authored with Itamar Simonson, is forthcoming in Consumer Psychology Review. Professor Gal specializes in conjoint analysis. He is also an expert in survey design, trademark infringement, brand equity, deceptive advertising, and consumer behavior.


Recent technology advances (e.g., tracking and “AI”) have led to claims and concerns regarding the ability of marketers to anticipate and predict consumer preferences with great accuracy. Here, we consider the predictive capabilities of both traditional techniques (e.g., conjoint analysis) and more recent tools (e.g., advanced machine learning methods) for predicting consumer choices. Our main conclusion is that for most of the more interesting consumer decisions, those that are “new” and nonhabitual, prediction remains hard. In fact, in many cases, prediction has become harder due to the increasing influence of just-in-time information (user reviews, online recommendations, new options, etc.) at the point of decision that can neither be measured nor anticipated ex ante. Sophisticated methods and “big data” can in certain contexts improve predictions, but usually only slightly, and prediction remains very imprecise – so much so that attempting to improve it is often a waste of effort. We suggest marketers focus less on trying to predict consumer choices with great accuracy and more on how the information environment affects the choice of their products. We also discuss implications for consumers and policymakers.

The views expressed in this article are solely those of the authors, who are responsible for the content, and do not necessarily represent the views of Vega Economics. For more information about Professor Gal, please email