Preference Learning and Demand Forecast
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Understanding consumer preferences is important for new product management, but isfamously challenging in the absence of actual sales data. Stated-preferences data are rel-atively cheap but less reliable, whereas revealed-preferences data based on actual choicesare reliable but expensive to obtain prior to product launch. We develop a cost-effectivesolution. We argue that people do not automatically know their preferences, but canmake an effort to acquire such knowledge when given sufficient incentives. The methodwe develop regulates people’s preference-learning incentives using a single parameter,re-alization probability, meaning the probability with which an individual has to actuallypurchase the product she says she is willing to buy. We derive a theoretical relation-ship between realization probability and elicited preferences. This allows us to forecastdemand in real purchase settings using inexpensive choice data with small to moderaterealization probabilities. Data from a large-scale field experiment support the theory, anddemonstrate the predictive validity and cost-effectiveness of the proposed method.
DepartmentSloan School of Management
Institute for Operations Research and the Management Sciences (INFORMS)
Cao, Xinyu and Juanjuan Zhang. “Preference Learning and Demand Forecast.” Marketing Science, 40, 1 (April 2020) © 2020 The Author(s)
Author's final manuscript