Essays on the dynamics of brand equity

Management of brand equity has come to be viewed as critical to the optimal long-term performance of a brand. In the first essay of this dissertation, we present a methodology for estimating, tracking, and managing brand equity using store-level scanner data. We estimate brand equity using a random coefficients logit demand model calibrated on store-level scanner data that accounts for unobserved consumer heterogeneity. We track brand equity estimates over time in two consumer packaged goods categories that experienced several new introductions of brands and product modifications during the time period of our empirical investigation. Using these tracked measures, we also study the impact of marketing actions such as advertising, sales promotions, and product innovations on brand equity. In the second essay of this dissertation, we develop and estimate a state-space model based on the Kalman filter that captures the dynamics in brand equity as influenced by its drivers. By integrating the Kalman filter with the random coefficients logit demand model, our estimation allows us to model consumer heterogeneity using store-level data as well as capture the dynamics in brand equity. Using these demand estimates, we infer both the short-term and long-term impact of changes in advertising and sales promotions on market share and profitability. We further investigate different strategies for reallocating the budgets between advertising and sales promotions without increasing the total marketing budget that can increase the long-term profitability of the brands. In the third essay of this dissertation, we develop a demand model for technology products that captures the effect of changes in the portfolio of models offered by a brand as well as the influence of the dynamics in its intrinsic preference on that brand\u27s performance. To accommodate the presence of multiple models at different points in time from a few (stable) brands, we use a nested logit model with the brand at the upper level and its various models at the lower level of the nest. The attractiveness of a brand\u27s product line, reflected in the inclusive value of the lower level of the nest, changes over time with entry and exit of new models and with changes in attribute and price levels. At the upper level of the nest, brand-level preferences are driven by the inclusive value as well as by the intrinsic preferences for each of the brands. To allow for time-varying intrinsic preferences at the brand level, we use a state-space model based on the Kalman filter which captures the influence of marketing actions such as brand-level advertising on the dynamics of intrinsic brand preferences. We estimate our model parameters on data for the U.S. digital camera market. Overall, we find that the effect of dynamics in the intrinsic brand preference relative to that of the dynamics in the inclusive values varies across brands. Assuming plausible profit margins, we evaluate the effect of increasing the advertising expenditures for each of the brands that respond to advertising and find that these brands can increase their profitability by increasing their advertising expenditures. We also analyze the impact of modifying a camera model\u27s attributes on its profits. Such an analysis could potentially be used to evaluate if product development efforts would be profitable