61 Journal of Marketing Vol. 71 (April 2007), 61–78 © 2007, American Marketing Association ISSN: 0022-2429 (print), 1547-7185 (electronic) S. Sriram, Subramanian Balachander, & Manohar U. Kalwani Monitoring the Dynamics of Brand Equity Using Store-Level Data Management of brand equity has come to be viewed as critical to a brand’s optimal long-term performance. The authors evaluate the usefulness of brand equity estimates obtained from store-level data for monitoring the health of a brand.They use a random coefficients logit demand model calibrated on store-level scanner data to track brand equity estimates over time in two consumer packaged goods categories that experienced several new product introductions during the period of the empirical investigation. Using these tracked measures, the authors also study the impact of marketing actions, such as advertising, sales promotions, and product innovations, on brand equity. They find that the brand equity estimates effectively capture the high equity of strongly positioned popular brands and brands that command a significant price premium in niche markets. Using an example, the authors illustrate how these brand equity estimates can be used to monitor changes in brand equity, which measures such as market share may fail to capture. The substantive results indicate that advertising has a positive effect on brand equity in both the product categories, whereas the effect of sales promotions is not significant in either category. Furthermore, the results reveal that new product innovations have a positive impact on brand equity and can explain a significant proportion of its variation. Overall, the analysis shows that a brand manager can track brand equity using store-level data, gain insights into the drivers of the brand’s equity, and manage these drivers to achieve brand equity targets. S. Sriram is Assistant Professor of Marketing, School of Business, Univer- sity of Connecticut (e-mail: ssriram@business.uconn.edu). Subramanian Balachander is Assistant Professor of Management (e-mail: sbalach@ mgmt.purdue.edu), and Manohar U. Kalwani is the OneAmerica Professor of Management (e-mail: kalwani@mgmt.purdue.edu), Krannert Graduate School of Management, Purdue University.This article is based on one of the essays in the first author’s doctoral dissertation. The authors thank Wilfred Amaldoss; Pradeep Chintagunta; Vrinda Kadiyali; Vishal Singh; V. Srinivasan; the seminar participants at Binghamton University, Emory University, Southern Methodist University, and University of Rochester; and the three anonymous JM reviewers for their comments and suggestions. To read and contribute to reader and author dialogue on JM, visit http://www.marketingpower.com/jmblog. I n contemporary marketing, brand equity has emerged as a key strategic asset that needs to be monitored and nur- tured for maximum long-term performance. A widely used definition of brand equity characterizes it as the value added by the brand name to a product (Farquhar 1989). Higher brand equity can help a brand become more prof- itable through higher brand loyalty, premium pricing, lower price elasticity, lower advertising-to-sales ratios, and trade leverage (Keller 1998). Given the advantages that accrue to a brand with high equity, effective brand management requires careful monitoring of its equity over the long run. Recently, many firms have appointed brand equity man- agers who are responsible for monitoring brand equity to both detect signals of erosion in a brand’s equity and approve programs to enhance it (Aaker 1991). Many commercially available methods for tracking brand equity, such as EquiTrend and Brand Asset Evaluator, employ consumer or expert surveys to measure brand equity (Aaker 1996). In the marketing literature, Park and Srinivasan (1994) offer such a survey-based measure of brand equity. Keller (1993) proposes a conceptual frame- work for measuring brand equity using customer surveys. Although survey-based measures can be useful for tracking brand equity and can provide useful diagnostic input to managers, Park and Srinivasan note (p. 286) that a limita- tion of their method is that “it depends on the ability of con- sumers to accurately report their relative brand prefer- ences.” Conversely, the availability of store-level scanner data offers an alternative way to measure and track brand equity that is free of self-report errors. Brand equity esti- mates based on store-level data in combination with tradi- tional survey-based measures can add to a manager’s confi- dence in formulating marketing programs designed to maintain and build a brand’s equity. Because marketing practitioners in consumer packaged goods categories often use store-level data to monitor brand performance by store type and geographical region and to assess the impact on sales of changes in advertising, price, and distribution (Bucklin and Gupta 1999; Shoemaker and Pringle 1980), such data with appropriate adjustments can also be used to generate brand equity estimates for tracking purposes. Using store-level data, as opposed to household-level data, to track brand equity has two advantages: First, store-level data are considered less vulnerable to sample selection bias than survey or individual-level scanner-panel data (Bucklin and Gupta 1999). Second, because store-level data can be obtained across several retailers in various geographic regions, managers can use these data to track the health of their brands across geographic regions and with respect to various store brands. In this article, we evaluate the usefulness of brand equity estimates obtained from store-level data for tracking