The authors develop a dynamic factor model of brand satiation to explain longitudinal variation in consumer purchases. Factor loadings are associated with a brand's position along a satiation dimension, and factor scores are associated with a household's sensitivity to satiation effects. The authors introduce dynamics by allowing the factor scores to evolve over time, reflecting variation in household satiation sensitivity. They embed the factor model in a direct utility model that allows for both corner and interior solutions and show that it fits the data better than alternative specifications. Analysis of a panel data set of corn chips purchases indicates that respondent satiation is better explained by a low-dimensional factor structure, while baseline utility and preferences are not. The authors explore implications for product line assortment in the face of quickening satiation. Dynamic Brand SatiationConsumer tastes and preferences change over time for a variety of reasons; for example, preferences change with price, during promotional events, and after new product introductions. They also change with consumer learning when consumers grow tired of offerings, because of variety seeking and state-dependent behavior and because of changing sensitivities to product characteristics due to changing consumption contexts. A critical aspect in studying the dynamics of preference is determining whether changes are related to specific product attributes or combinations of attributes and which attributes lead to greater satiation in demand for offerings. If dynamic brand preferences are tied to product attributes, firms can consider physically reformulating brands to make them more attractive to consumers.Challenges arise in quantifying the dynamics of brand satiation because of the large number of brands and varieties in a category and an even larger number of attributes used to formulate the brands. Most brands comprise many * Shohei Hasegawa is a doctoral candidate (

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ustomer Satisfaction Index has been developed in many countries including North America, Europe and Asia last decades, which are based on Americal Customer Satisfaction Index (ACSI ) by the University of Michigan, where the latent factor "Customer Satisfaction" related to the customer loyalty is estimated by a covariance structural model with six factors generated from 17 question items and PLS method. They apply the identical structural model to all companies in order to measure the national and industrial indexes that are used to compare the services in different companies as well as industries.In this paper, by using the assumption that the same model must be applied to every company, we link the path coefficients of each company as the hierarchical regression model to estimate the structure for customer satisfaction across companies to show that, representing "communality" inside industry and "heterogeneity" outside industry, the hierarchical Bayes modeling produces more stable significant path coefficients. Moreover, our approach has the additional advantages. (i)The volume of information (number of survey data) can be augmented, (ii)The index can be constructed without additional surveys for new company (forecasting) and not-surveyed company (missing observations), (iii)When aggregating individual index of each company up to the industrial index and national index, the communality assumption could increase the stability of the macro index.