Active Machine Learning for Consideration Heuristics

We develop and test an active-machine-learning method to select questions adaptively when consumers use heuristic decision rules. The method tailors priors to each consumer based on a "configurator." Subsequent questions maximize information about the decision heuristics (minimize expected posterior entropy). To update posteriors after each question, we approximate the posterior with a variational distribution and use belief propagation (iterative loops of Bayes updating). The method runs sufficiently fast to select new queries in under a second and provides significantly and substantially more information per question than existing methods based on random, market-based, or orthogonal-design questions. Synthetic data experiments demonstrate that adaptive questions provide close-to-optimal information and outperform existing methods even when there are response errors or "bad" priors. The basic algorithm focuses on conjunctive or disjunctive rules, but we demonstrate generalizations to more complex heuristics and to the use of previous-respondent data to improve consumer-specific priors. We illustrate the algorithm empirically in a Web-based survey conducted by an American automotive manufacturer to study vehicle consideration (872 respondents, 53 feature levels). Adaptive questions outperform market-based questions when estimating heuristic decision rules. Heuristic decision rules predict validation decisions better than compensatory rules.