Market Research and Innovation Strategy in a Duopoly
Marketing Science
Efficient Choice Designs for a Consider-Then-Choose Model
Marketing Science
Active Machine Learning for Consideration Heuristics
Marketing Science
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In most marketing experiments, managerial decisions are not based directly on the estimates of the parameters but rather on functions of these estimates. For example, many managerial decisions are driven by whether or not a feature is valued more than the price the consumer will be asked to pay. In other cases, some managerial decisions are weighed more heavily than others. The standard measures used to evaluate experimental designs (e.g., A-efficiency or D-efficiency) do not accommodate these phenomena. We propose alternative “managerial efficiency” criteria (M-errors) that are relatively easy to implement. We explore their properties, suggest practical algorithms to decrease errors, and provide illustrative examples. Realistic examples suggest improvements of as much as 30% in managerial efficiency. We close by considering approximations for nonlinear criteria and extensions to choice-based experiments.