Predicting Behavior from Intention-to-Buy Measures: The Parametric Case

Abstract

The author develops a probabilistic model that converts stated purchase intents into purchase probabilities. The model allows heterogeneity between nonintenders and intenders with respect to their probability to switch to a new “true” purchase intent after the survey, thereby capturing the typical discrepancy between overall mean purchase intent and subsequent proportion of buyers (bias). When the probability to switch of intenders is larger (smaller) than that of nonintenders, the overall mean purchase intent overestimates (underestimates) the proportion of buyers. As special cases, the author derives upper and lower bounds on proportions of buyers from purchase intents data and shows the consistency of those bounds with observed behavior, except in predictable cases such as new products and business markets. However, a straightforward modification of the model deals with new product purchase forecasts.