Brand Equity as a Revenue Multiplier

Brand Equity as a Revenue Multiplier




Sudhir Voleti
Paul Nelson
Sanjog Misra*





*All three authors are at the William E. Simon Graduate School of Business
Administration, University of Rochester, Rochester, NY 14627
(Tel) 585-275-2550 or 585-275-8920
(Email) voletis1@simon.rochester.edu nelson@simon.rochester.edu
sanjog.misra@simon.rochester.edu

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Brand Equity as a Revenue Multiplier

Abstract

This paper develops and illustrates a revenue multiplier methodology to estimate brand
equity that addresses two major drawbacks in extant brand equity measurement methods: (i) Our
methodology requires data only from easily available secondary sources; and (ii) Marketing mix
impacts are explicitly modeled so as to allow a more accurate estimate of brand equity. For each
specific brand, brand equity is measured as a multiplier that quantifies the difference in market
response between that of the branded product and that estimated for an equivalent unbranded
product with exactly the same market mix actions. This meshes extremely well with the notion
that brand equity is the incremental effect of brand name on product value. In particular, we
utilize frontier estimation tools to estimate the revenue of each brand’s “unbranded equivalent”,
with each brand’s brand equity revenue multiplier reflecting the degree to which the brand’s
observed revenues exceed this amount. The methodology is illustrated using data for the top 25
US beer brands and the results agree with intuition, theory and financial data-based brand equity
valuations.

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“If this business were split up, I would give you the land and bricks and mortar, and I would
take the brands and trademarks, and I would fare better than you.”
- John Stuart, former CEO of Quaker Oats
Introduction
Brands are now widely recognized to be one of, if not the, most valuable assets that a
firm owns. It is not surprising then that the valuation of brand assets has taken on an increasingly
important role in recent years. The term that encompasses this notion of valuing such brand
assets is Brand Equity. While there are numerous definitions of the brand equity construct, most
researchers and practitioners today agree that brand equity is essentially the difference in the
values that accrue to a product with and without its brand identity.
Measuring brand equity is no trivial task. Consider the following scenario: Our task is to
evaluate the brand equity component of a branded automobile, say the Honda Accord. To answer
this, we need to know what the value of the Honda Accord would be if it were shorn of its brand
name. Clearly, no such generic entity readily exists, so constructing a simple comparative
valuation method is out of the question. We could compare the Honda Accord to another
(observed) automobile, but that wouldn’t quite work since we would be comparing two different
products (with different attribute sets) which have two different marketing effort allocations
(promotion and distribution levels, etc.). Such a comparison would ultimately give us a biased
picture of the Honda Accord’s brand equity. In addition to this conceptual difficulty in
measuring brand equity, there also are noteworthy effort and financial costs related to collecting
and processing the required data. Thus, ideally a good measure of brand equity would only use
data that are readily available to analysts and allow the construction of an unbranded mirror for
any branded product.

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This paper develops a methodology to estimate brand equity (hereafter, BE) that requires
data from only easily available secondary sources and explicitly models product attribute and
other marketing mix impacts so as to allow a more accurate estimate of brand equity. In
particular, BE is estimated as the brand-specific component of revenue shorn of the impact of
both observed and unobserved product attributes as well as other marketing mix and category
factors. That is, in adherence to the BE definition, the proposed methodology creates, for each
particular brand, a unique reference baseline that has identical marketing mix investments
including product features but no brand name and then compares the estimated product-market
outcome for this “unbranded equivalent” with that of the branded entity. The ratio of these values
is what we term the brand equity multiplier.
The paper proceeds as follows. The next section provides a brief literature review that
serves to position this study. We next develop the conceptual framework to estimate our revenue
multiplier measure of BE. Then the framework is empirically illustrated using beer data and the
results discussed. Finally, we conclude with a summary which forwards managerial implications
and directions for future research.
Brand Equity Measurement
The brand equity measurement literature is classified based on the level at which the
brand equity outcome is measured. In particular, the consumer-based perspective (Keller 1993)
proposes individual consumer level measures while the product-market perspective (Leuthesser
1988; Keller and Lehmann 2003; 2006) expounds market level measures.
The consumer-based perspective looks at consumer perception constructs such as
attitude, awareness and liking for a brand and translates these perceptual measures into brand
equity measures such as brand affect (Bousch et al. 1987) and brand-specific associations (Bhat

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and Reddy 2001). These studies require individual level data collected through surveys or
experiments and, as such, this information is costly and time-consuming to collect. Generally,
these studies also are subject to the above described confounding of marketing mix and brand
impacts on the measured outcome.1 Further, these measures are based on the stated preferences
of respondents and consequently may not reflect real world (revealed preference) outcomes.
The product-market perspective derives brand equity estimates from more accessible
market level outcome data routinely collected by the firm or syndicated data providers. One
stream of this literature uses firm level financial data to generate BE estimates based on
measures such as acquisition prices (Mahajan, Rao and Srivastava 1994) and residual market
values (Simon and Sullivan 1993). These BE estimates, however, are typically “firm equity”
measures since the financial measures used are at the firm level and most firms are multi-brand
firms (Aaker and Jacobson 1994). That is, for a particular brand not only are its marketing mix
and brand effects confounded with each other, they are also confounded with those of the firm’s
other brands.
A second stream of the product-market BE literature, which is most in line with the
approach forwarded in this paper, utilizes readily available brand level market results such as
sales, profits and prices or syndicated individual level scanner choice data. In particular,
measures such as the additional willingness-to-pay for a branded product compared to an
unbranded one (Aaker 1991, 1996; Sethuraman 2003), market-share and relative prices
(Chaudhari and Holbrook 2001), segment-wise brand preferences (Kamakura and Russell 1993),
revenue premiums (Ailawadi, Lehmann and Neslin 2003), and profit differentials (Dubin 1998;
Goldfarb, Lu and Moorthy 2007) are used to estimate BE.

1 Conjoint studies have explicitly estimated brand name impact on stated preference or choice while controlling for
product attribute differences (Srinivasan 1979; Park and Srinivasan 1994), but do not account for the impact of other
marketing mix variables. These studies also require significant primary data collection.

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While these product-market BE measures are brand specific, they still suffer from the
confounding of brand and marketing mix (product attributes, promotion, price and distribution)
effects. Four issues lie behind this: (i) The impact that marketing mix actions have on the
measured market outcome of a brand is typically not modeled. Thus, their impact on the outcome
may be improperly attributed to brand equity. (ii) A store brand, private label or low share brand
typically is taken as the “baseline brand” and it’s BE (along with any unmeasured marketing mix
effects) is assumed to be an a priori fixed value, typically zero. The difference in the chosen
outcome measure for this baseline brand and that for a particular “non-baseline” brand provides
the measure of that particular brand’s BE. Since the “true” BE of the baseline brand is almost
certainly positive, all of the BE estimates are biased downwards. (iii) Further, the baseline
brand’s marketing mix decisions and their impact are not the same as those of any particular non-
baseline brand. Consequently, the estimated BE of each non-baseline brand implicitly includes
these marketing mix impact differences in its BE estimate. This bias may be positive for some
brands and negative for others. (iv) Finally, the BE and marketing mix confound is exacerbated
by the fact that multiple items (SKUs or stock keeping units which identify distinct variants,
flavors, sizes, etc.) typically share a brand name, and the SKUs that make up the baseline brand
are very likely to differ in not only number but in their marketing mix from those of each
particular non-baseline brand. In addition to these issues, general market characteristics such as
market size and input costs also influence the observed market outcomes of all products. As a
result, a fifth BE measurement bias could manifest itself if these are not modeled.
Our proposed methodology, discussed below, utilizes readily available secondary data
but unlike earlier efforts it directly addresses the issues outlined above by explicitly modeling the
impact of product attribute, promotion and distribution actions as well as brand effects and does

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so without the use of an ad hoc baseline brand relative to which all other brand outcomes are
evaluated. The BE-category size confound also is addressed by explicitly modeling the impact of
category characteristics.
Brand Equity as a Revenue Multiplier
Product-market outcomes such as revenue for any product level in the category, be it a
brand or SKU, are tied to category-wide factors as well as the product’s marketing mix actions
and brand equity. Thus,
⎛
⎞
Category
Marketing Mix
Brand
⎜
⎟
Market Response=f
,
,
⎜
. (1)
Charateristics
Actions
Equity ⎟
⎜
⎟
⎝
⎠

Our aim is to identify the impacts that category characteristics and a product’s marketing
mix actions have on the market outcome of interest and, in effect, remove them from the
observed market outcome measure, thereby, leaving us with a more accurate estimate of brand
equity (the effect of brand name on the outcome). The combined impact of the first two factors
described in (1) provides an estimate of the outcome that would result for an unbranded product
that has identical components to those of the branded product. Once the estimated outcome for
this “unbranded equivalent” (UE) is removed from the branded product’s observed outcome,
what is left is the impact of the brand name itself – our measure of brand equity.
We choose revenue as our metric of market response for a variety of reasons. (i) Revenue
is recorded in scanner data at every level of product aggregation. (ii) Revenue has previously
been used as a BE metric (e.g., Ailawadi, Lehmann and Neslin 2003). (iii) The economic
rationale behind the parametric restrictions in our model applies readily to revenue. We also
utilize a general multiplicative formulation that accommodates various response shapes and rates
including both diminishing and increasing returns (Lilien, Kotler and Moorthy 1992).

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Product
⎡ Revenue of the
⎤ ⎡Brand Equity⎤
= ⎢
⎥ ⎢
⎥ . (2)
Revenue
Unbranded Equivalent
Multiplier
⎣
⎦ ⎣
⎦
This multiplier formulation implies that the Brand Equity Multiplier (BEM) for a
particular branded product is simply a multiple of the market outcome that would arise to its
unbranded equivalent UE (i.e., an unbranded product with exactly the same product attributes,
promotion, price and distribution). This formulation implies that BE scales up or down the value
of (or demand for) non-brand product characteristics. Equation (2) implies that the accuracy of
our brand equity measure BEM depends on how well we estimate the revenue that would accrue
to the UE. For each branded product analyzed, this requires a reasonably complete description of
its marketing mix (as well as general category conditions) so that their impacts on revenue can be
accurately estimated and used to derive the revenue particular to each branded product’s unique
UE. To spell this notion out further we identify the revenue of the UE of each brand as a
multiplicative function of the marketing mix decisions2 of the brand, general category level sales
drivers, and a random error term (non-systematic revenue shocks that can be interpreted as
measurement error). This results in a more detailed expression for product revenue.
⎡Category ⎤
⎢
⎥ ⎡Product⎤ ⎡Promotion⎤ ⎡Distribution⎤ ⎡Random ⎤ ⎡ Brand Equity ⎤
Revenue = Drivers
⎢
⎥ ⎢
⎥ ⎢
⎥ ⎢
⎥ ⎢
⎥ ⎢
⎥
Impact
Impact
Impact
Shocks
⎣
⎦ ⎣
⎦ ⎣
⎦ ⎣
⎦
⎢
Multiplier BEM
⎣
⎦ .(3)
Impact
⎣
⎥
⎦
1 4 4 4 4 4 4 4 4 4 4 4 2 4 4 4 4 4 4 4 4 4 4 43
Revenue due to Unbranded Equivalent
In equation (3), we decompose revenue into broad determinants of product demand
which we label “revenue components” and use observable measures (detailed in the data section)
to capture the effect of each revenue component. In addition, since a brand typically
encompasses numerous SKUs, each with slightly different marketing mix characteristics, an

2 Since our dependent variable revenue is constructed from price and quantity data, including any function of price
as an explanatory variable is not appropriate.

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analysis of SKU rather than brand level revenues allows for more accurate estimation. Each SKU
associated with each particular brand, thus, has its own unique UE. These considerations give
rise to a more explicitly defined revenue expression
β
A
⎛Category ⎞ a C
β
D
β
β
Revenue
0
= e ∏⎜
⎟ ∏(Product
c
jt
c j
∏ Promotion
d
, )
(
d , jt )
a =
Drivers
1 ⎝
a t ⎠
,
c=1
d =1
,
(4)
∏M(
βm η
Distribution
jt
e
BEM
m, jt )
b( j)
m 1
=
where j refers to a particular SKU, b to a particular brand, b(j) to the brand b that contains SKU j,
and t to each time period. The various measures outlined below for the UE revenue components
are referred to generically. η is the random error. Any systematic brand level revenue impact
jt
unexplained by the analysis variables is captured by the brand equity multiplier term BEM.
Data
Beer data collected from a variety of readily available sources are used to illustrate our
methodology. The US beer market is a well defined and mature product category with
characteristically little change in the total quantity of beer sold during our sample period 2002 -
2005. As with sales, distribution, promotion and price levels differ widely across brands and
SKUs. Distribution is a major determinant of sales, and promotions – especially advertising
($1.175 billion in 2005) and retail merchandising (features, displays, and temporary price
reductions) are utilized heavily. However, while the category is dominated by a handful of big
brands and manufacturers with extensive distribution and large promotional programs
(Anheuser-Busch, SAB Miller, Molson-Coors and Pabst account for over 81% of US sales),
smaller, more regionally distributed brands compete quite effectively. Indeed, the collective
share of the top 25 beer brands we analyze is slowly falling. Note also that even these major
brands have numerous SKUs that receive limited distribution and promotion.

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In order to achieve a reasonably accurate partitioning of SKU level revenues into those
related to brand equity and those that would accrue to the SKU’s unbranded equivalent, it is
necessary to identify and use a variety of measures to reflect the impact of the UE revenue
components outlined in equation (4). These data and their readily available sources are outlined
in Table 1 and detailed forthwith. Table 2 briefly profiles the 25 top selling beer brands.
Revenue
For our revenue measure, Revenue, we use monthly AC Nielsen national revenue data
pertaining to the various SKUs that constitute the top 25 beer brands sold in food stores for the
years 2002 to 2005.3 We define “brand” as the identifier for any group of products which share a
nominal label, “variant” as a subset of the brand that differs from other variants of the same
brand by some identifier or descriptor in the label, and “SKU” as any packaging or size of the
product that differs from other products of the same variant. For example, Budweiser is a brand,
Bud Light and Bud Ice are two variants of Budweiser, and a six pack of 12 ounce Bud Light
longneck bottles is a different SKU from a six pack of Bud Light 12 ounce cans.
Note that a SKU level analysis allows each branded SKU to be compared to its own
unique unbranded equivalent. Any higher level product (i.e., variant or brand) analysis will
necessarily utilize a less comparable UE (i.e., the marketing mix description of a variant is less
accurate since it must be represented as averages or sums over the SKUs that share the variant
name). For example, outside of a few high sales “star” SKUs, most SKUs do not sell nearly as
well and are not distributed as widely. Furthermore, each SKU has at least one product attribute
difference from its other similarly branded SKUs. If the data were aggregated to the brand level,

3 Each 4-week period is referred to as a month. The beer category is defined as lagers and light beers since they
constitute the vast majority of all malt beverages sold (i.e., malt liquors, stouts, ales and flavored malt beverages are
not included in the analysis).

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