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

Private-Label Use and Store Loyalty

The authors develop an econometric model of the relationship between a household's private-label (PL) share and its behavioral store loyalty. The model includes major drivers of these two behaviors and controls for simultaneity and nonlinearity in the relationship between them. The model is estimated with a unique data set that combines complete purchase records of a panel of Dutch households with demographic and psychographic data. The authors estimate the model for two retail chains in the Netherlands—the leading service chain with a well-differentiated high-share PL and the leading value chain with a lower-share PL. They find that PL share significantly affects all three measures of behavioral loyalty in the study: share of wallet, share of items purchased, and share of shopping trips. In addition, behavioral loyalty has a significant effect on PL share. For the service chain, the authors find that both effects are in the form of an inverted U. For the value chain, the effects are positive and nonlinear, but they do not exhibit nonmonotonicity, because PL share has not yet reached high enough levels. The managerial implications of this research are important. Retailers can reap the benefits of a virtuous cycle; greater PL share increases share of wallet, and greater share of wallet increases PL share. However, this virtuous cycle operates only to a point because heavy PL buyers tend to be loyal to price savings and PLs in general, not to the PL of any particular chain

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

Category characteristics for IRI Marketing Science Dataset used in: How well does consumer-based brand equity align with sales-based brand equity and marketing-mix response?, Journal of Marketing (2017)

Category characteristics survey for the IRI Marketing Science dataset (Bronnenberg, Kruger, and Mela 2008), collected by Datta, Ailawadi, and Van Heerde (2017) on Amazon Mechanical Turk (May 2016) to measure the following category characteristics: (1) Hedonic nature of category, (2) Functional / performance risk of category, (3) Social value / social demonstrance of category, (4) Category involvement, (5) Utilitarian nature of category. For details on data collection and constructs, see codebook (PDF/docx)

Category characteristics for IRI Marketing Science Dataset used in: How well does consumer-based brand equity align with sales-based brand equity and marketing-mix response?, Journal of Marketing (2017)

Category characteristics survey for the IRI Marketing Science dataset (Bronnenberg, Kruger, and Mela 2008), collected by Datta, Ailawadi, and Van Heerde (2017) on Amazon Mechanical Turk (May 2016) to measure the following category characteristics: (1) Hedonic nature of category, (2) Functional / performance risk of category, (3) Social value / social demonstrance of category, (4) Category involvement, (5) Utilitarian nature of category. For details on data collection and constructs, see codebook (PDF/docx)