How well does consumer-based brand equity align with sales-based brand equity and marketing mix response?

Brand equity is the differential preference and response to marketing effort that a product obtains because of its brand identification. Brand equity can be measured based on either consumer perceptions or on sales. Consumer-based brand equity (CBBE) measures what consumers think and feel about the brand, whereas sales-based brand equity (SBBE) is the brand intercept in a choice or market share model. This paper studies the extent to which CBBE manifests itself in SBBE and marketing mix response using ten years of IRI scanner and Brand Asset Valuator (BAV) data for 290 brands spanning 25 packaged good categories. It uncovers a fairly strong positive association of SBBE with three dimensions of CBBE – Relevance, Esteem, and Knowledge – but a slight negative correspondence with the fourth dimension, Energized Differentiation. It also reveals new insights on the category characteristics that moderate the CBBE-SBBE relationship, and documents a more nuanced association of the CBBE dimensions with response to the major marketing mix variables than heretofore assumed. Implications are discussed for academic researchers who predict and test the impact of brand equity, for market researchers who measure it, and for marketers who want to translate their brand equity into marketplace success

Empirical Models of Manufacturer-Retailer Interaction: A Review and Agenda for Future Research

The nature of the interaction between manufacturers and retailers has received a great deal of empirical attention in the last 15 years. One major line of empirical research examines the balance of power between them and ranges from reduced form models quantifying aggregate profit and other related trends for manufacturers and retailers to structural models that test alternative forms of manufacturer-retailer pricing interaction. A second line of research addresses the sources of leverage for each party, e.g., trade promotions and their pass-through, customer information from loyalty programs, manufacturer advertising, productassortment in general, and private label assortment in particular. The purpose of this article is to synthesize what has been learnt about the nature of the interaction between manufacturers and retailers and the effectiveness of each party’s sources of leverage and to highlight gaps in our knowledge that future research should attempt to fill

Market Power and Performance: A Cross-Industry Analysis of Manufacturers and Retailers

Two recent studies of manufacturer and retailer profitability in the food industry have raised questions about whether the widely cited, but empirically untested, shift of power from manufacturers to retailers has really occurred. Has the marketing community been operating under a misconception or are these studies flawed? This paper uses more complete measures of exercised and potential market power and a broader sample of industries and retail classes to address this critical question. Not only do our measures have strong theoretical grounding in the industrial organization, finance and accounting literature, they incorporate in them the impact of actions that have been commonly cited as illustrations of a power shift. Our analysis of 14 consumer good industries shows that only a few of them exhibit a shift in market power towards retailers. Further this apparent shift is highly influenced by a small number of retailers within a single retail class

—Retailer Promotion Pass-Through: A Measure, Its Magnitude, and Its Determinants

We use data on all manufacturer funding and promotion activity by a major U.S. retailer during a two-year period to compute promotion pass-through and assess its magnitude. Then, we estimate a two-tiered probit and lognormal regression model to study drivers of the large variation we observe in pass-through rates. Although our analysis is based on data from a single retailer, it provides a much more complete picture of the magnitude and variability of pass-through than has been available to date. Some key insights from our work are as follows. First, the retailer passes through more than 100% of the total manufacturer funding it receives in aggregate, but the median pass-through rate for individual manufacturers is much lower than 100%. Second, some manufacturers are promoted even without funding. This is more likely for private label and high-share manufacturers in high-lift and high-margin categories. Third, a small number of manufacturer and category characteristics explain a significant amount of the variation in pass-through. In particular, pass-through is higher for private label. It increases with the share of the manufacturer in the focal category but also with the sales of that manufacturer in other categories. Categories with high sales, high promotion lift, low concentration, and low margin get more pass-through. We corroborate some recent conclusions in the literature, e.g., that some pass-through rates are much higher than 100% and that high-share manufacturers get more pass-through. We add several new insights, e.g., on the magnitude of aggregate pass-through, on cross-pass-through across and within categories, on pass-through for private label, and on the category drivers of pass-through.trade promotion, promotion pass-through, promotion response, retail promotions, private label promotions, cross-pass-through

Structural Analysis of Models with Composite Dependent Variables

Composite variables are those that may be mathematically decomposed into additive and/or multiplicative component variables. Several researchers have noted that the relationship between a composite variable and its components may be a mathematical artifact, but the effect of their inclusion as independent variables on the coefficients of the remaining variables in the model has not been recognized, nor has a formal expression for the resulting bias been presented. Structural analysis of composite dependent variables, as presented here, provides the key to understanding the nature and extent of this bias. It separates higher and lower level components from one another and also separates these components from other antecedent variables in the model. The advantages of this hierarchical decomposition are that (1) it reduces problems of misspecification and omitted variables, (2) by separately estimating antecedent effects on each component, it offers some insights into the underlying causal mechanisms that are not available from other techniques and (3) it ensures that regression coefficients can be interpreted in the standard way: the expected change in the dependent variable associated with a change in the independent variable, holding other independent variables in the equation constant. Moreover, hierarchical decomposition of the dependent variable can reproduce all information available from techniques that mix levels of analysis, but the converse is not true.marketing, structural analysis

Practice Prize Report—Quantifying and Improving Promotion Effectiveness at CVS

We quantified the net unit and profit impact of each promotion offered in 2003 by CVS, a leading U.S. drug retail chain, and analyzed the key drivers of variation in this net impact. We used this analysis to identify the least effective promotions and conducted a controlled field test to demonstrate the impact of eliminating them before chainwide implementation. Our key findings are as follows. First, approximately 45% of the gross lift from promotions is incremental for CVS. Further, for every unit of gross lift, 0.16 unit of some other product is purchased elsewhere in the store. Still, more than 50% of promotions are not profitable because the lower promotional margin is not sufficiently offset by incremental units. Second, there is substantial variation in net profit impact across categories. Our field test shows that eliminating promotions chainwide in 15 of the worst performing categories will decrease sales by about $7.8 million but will improve profit by approximately$52.6 million. This is very impressive given that CVS front store sales in 2003 were approximately $9 billion while the net profit impact of promotions was -$25.3 million.promotion profitability, retail promotions, retail promotion impact