Preference Learning and Demand Forecast

Understanding consumer preferences is important for new product management, but is famously challenging in the absence of actual sales data. Stated-preference data are relatively cheap but less reliable, whereas revealed-preference data based on actual choices are reliable but expensive to obtain prior to product launch. We develop a cost-effective solution. We argue that people do not automatically know their preferences, but can make an effort to acquire such knowledge when given sufficient incentives. The method we develop regulates people’s preference-learning incentives using a single parameter, realization probability, meaning the probability with which an individual has to actually purchase the product she says she is willing to buy. We derive a theoretical relationship between realization probability and elicited preferences. This allows us to forecast demand in real purchase settings using inexpensive choice data with small to moderate realization probabilities. Data from a large-scale field experiment support the theory and demonstrate the predictive validity and cost-effectiveness of the proposed method.