Despite the enormous amounts of resources devoted to concept and product testing and the continued use of pretest market (PTM) modeling procedures, estimates of new product failures are still alarmingly high. The primary objectives of PTM modeling are to forecast the market share/sales volume of a new product and to determine the sources of new product share at the aggregate market level. The authors describe a new approach that is designed to provide a parsimonious description of competitive changes before and after a new product is introduced by identifying latent segments (i.e., groups of consumers) that vary in size and composition with respect to the relative preferences for a set of brands before and after a new product is introduced. Each latent segment represents a particular preference state characterized by a set of segment-level choice probabilities. The modeling framework is based on a class of dynamic latent class models that explicitly recognize two major types of preference heterogeneity: (1) heterogeneity caused by before-after changes in latent preferences for the brands (i.e., time-varying relative choice probabilities) and/or (2) heterogeneity caused by consumers changing their latent preference segment in response to a new product (i.e., time varying latent segment probabilities). As is demonstrated in the empirical application, the dynamic latent class models provide a comprehensive framework for understanding how a new product changes the competitive landscape.
Problems in detecting misfit of latent class models in diagnostic research without a gold standard were shown