Title: Branding in the nth

Dimension: Measuring Brand Equity in a Digital World

Authors: Kyle Findlay and Alice Louw

Abstract: This paper looks at various traditional metrics that market researchers use to measure brand

equity. It briefly reviews the various strengths and weaknesses of each approach, before making a case for

multi-dimensional inputs as the best approach. The paper touches on network theory and systems theory as

means to describe the multi-dimensional concepts we refer to when talking about ‘brand equity’. Finally, the

paper looks at the new tools emerging online that are designed to measure what people are saying about

brands and brand influence, and their applicability to brand equity measurement. What is clear is that a

paradigm shift is occurring: what people consider a brand to be and what they expect from brands is

changing. In light of this, it is necessary for market researchers to also change how they think about brands

and brand equity. Networks and non-linear science concepts will help us to make this transition.

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Introduction

Brand equity is a surprisingly slippery concept. The generally accepted understanding of what

constitutes ‘brand equity’ is the perceptions and associations around a brand name that is unique to

a product, above and beyond the physical attributes and assets, and that confers additional value

onto the product in the mind of customers (in terms of goodwill, prestige, improved perceptions of

quality or reliability, for example).

Joel Axelrod [1992] describes brand equity as “the incremental amount your customer will pay to

obtain your brand rather than a physically comparable product without your brand name”. Paul

Feldwick [1996] more generally breaks brand equity down into three components:

1. Brand valuation - “The total value of a brand as a separable asset – when it is sold, or

included, on a balance sheet”

2. Brand strength - “A measure of the strength of consumers’ attachment to a brand”

3. Brand image - “A description of the associations and beliefs the consumer has about the

brand”

Figure 1: Brand equity refers to the intangible value that the associations around a product's name and symbols

engenders in the market place

These definitions are all fine and well, except they do not imply a single definite method of

measuring the intangible value added by a product’s name and its symbols such as logos,

packaging, shape, etc. Unfortunately, when dealing with such an ephemeral concept, it is very

difficult to measure brand equity directly. Instead, we use proxy measures that purport to quantify

the intangible benefits that accrue to a product based on its name, reputation, packaging, etc.

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Eminent marketing scholar, David Aaker, is one of the most well-known luminaries to have taken a

stab at defining how one measures brand equity. This paper looks at the various dimensions

suggested by Aaker and which are commonly used in the market research industry today. We

summarise the measures’ various pros and cons, include our own learnings from twenty years worth

of measuring brand equity, and cast an eye into the future to better understand the movements afoot

to measure brand equity in the age of the internet and social media.

Aaker suggests four dimensions to measuring brand equity: Loyalty, perceived quality, associations

and awareness.

In addition, major research houses such as Millward Brown, Ipsos, TNS and Synovate all have their

own philosophies when it comes to the dimensions of brand equity (see Appendix 1 for a summary

of the dimensions major research houses use to define brand equity). Undoubtedly, there is some

conceptual overlap across the models, but what stands out quite clearly in both the models

employed by major research houses and those espoused by academics like Aaker, is that there is

more than one dimension to brand equity, and using a single input to derive your measurement will

inevitably lead to a measurement shortcoming.

Diving into this paper, some readers may be hoping for easy answers, but if anything, we will see

that the world of brand equity is a confusing one, and with the introduction of digital technologies

and paradigms, things only get more confusing. What readers should be asking themselves is what

this says about the state of our industry and the guiding paradigms we have so lovingly subscribed

to thus far? Often it is the case in scientific fields that before a unifying theory arises; researchers

are left with a multitude of often contradictory micro-theories. This is the stage we are at now.

Perhaps this is a sign that a unifying theory is not far off. We personally believe that the theory will

come from the areas of systems theory as informed by non-linear science, network theory and chaos

theory.

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Summary of traditional brand equity measures

Loyalty

The term loyalty is subject to a variety of interpretations and is often divided into behavioural

loyalty (based on actual purchasing behaviour) and attitudinal loyalty (based on brand perceptions).

For Aaker, this "core dimension of brand equity" is reflected in two key indicators: price premium

and customer satisfaction / loyalty [Aaker, 1996].

Price Premium refers to the additional amount that a customer is willing to pay for a brand,

compared to another brand offering similar benefits. Although not entirely objective (as products’

features are rarely exactly the same), price differentials do provide a good proxy for the added value

that a customer feels the brand brings to the physical product, and can therefore be good indicators

of intangible value when comparing similar products. The willingness to pay a price premium has

been shown to correlate strongly with brand relationships, and can be a useful indicator of the

strength of a brand's relationship with its customers [Hofmeyr & Rice, 1999].

Figure 2: The above charts show the results of a conjoint analysis combined with a measure of relationship strength in

two markets. In both cases it is clear that the strength of relationship increases the brand's ability to charge a premium.

[Hofmeyr & Rice, 1999]

Although a seemingly straight forward concept, price premium measurement can pose some

challenges. What customers pay for a brand and what they are willing to pay are often not aligned

due to in-market realities. Actual pricing within the marketplace is frequently driven by the trade

rather than the brand itself. A brand's price can differ widely across trade channels and trade

discounting often distorts price-equity relationships. There are also strong brands which do not

command a price premium due to 'scale economy branding' [Feldwick, 1996].

41

9

Strong relationship

19

2

Weak relationship

North America

Percent of consumers that defect as the price of the brand is increased

7773

67

59

51

39

Emerging Market

Percent of consumers retained as the price of the brand is increased

Strong relationship

Weak relationship

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Restricted markets pose another challenge for those attempting to use price premiums as an equity

indicator. In some markets, brands are not able to charge what their equity would allow due to legal

or other restrictions. Cigarettes brands, for example, are often forced by law to charge a minimum

price which might well exceed what their actual equity suggests. In restricted markets such as these,

price differentials are not accurate reflections of brand equity differentials and therefore the price

premium is not a relevant measure of brand equity.

Customer satisfaction / loyalty. Customer satisfaction is a very widely accepted measure to estimate

a brand's equity. However, there are a number of potential pitfalls related to using this measure.

On its own, customer satisfaction cannot fully explain the complexities observed in brand

relationships. High levels of satisfaction do not necessarily result in behavioural loyalty. In markets

where brand choice is of low importance to people, market factors such as price and convenience

often override brand satisfaction. If people do not care which brand they use (either because the

perceived difference between brands is low or they are not strongly involved in the category) high

brand switching can be observed despite the high levels of customers’ satisfaction. Variety seeking

behaviour can also cause seemingly satisfied customers to switch to alternate offerings.

In a survey of published research relating to Customer Satisfaction (from 1983 onwards), Hofmeyr

[2007 - see Appendix] found that the average correlation between customer or brand satisfaction

and behaviour was only R = 0.13, R2 = 0.02. A mere 2% of the variance in behaviour was explained

using customer satisfaction measures.

Another key limitation to using satisfaction and loyalty measures to reflect brand equity is that they

do not apply to non-users of a brand. In order to get a complete measure of a brand's equity within

the marketplace, a measure that looks beyond a brand's customer base and takes into account both

user and non-user equity is necessary.

Attitudinal loyalty is (at a basic level) frequently measured using intend-to-buy questions. Purchase

intention is, however, a notoriously unreliable indicator of what is actually likely to occur. What

people say and what they actually do are often very poorly related. In a survey of published research

on the relationship between purchase intention and real behaviour dating back to 1966, Hofmeyr

[2007 - see Appendix] found that the average correlation between what people said they intended to

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do and what they actually did was only R = 0.30, R2 = 0.09. In other words, 91% of the variance

was not captured by purchase intent!

A more "intense level of loyalty" would be measured using recommendation type questions [Aaker,

1996]. However, this type of measurement has been proven to also relate poorly to actual behaviour

(Molenaar, 2007).

To be valuable in a business context, brand equity measures need to bear some relation to actual

market behaviour - but customer satisfaction / loyalty, as a dimension by itself, does not perform

strongly in this regard.

Perceived quality and leadership

Perceived quality is considered by Aaker [1996] to be one of the "key dimensions of brand equity",

and benefits from the fact that it is applicable across product classes. Comparing quality scores for a

brand of jeans and a brand of chocolate, while based on different product attributes, can still have

meaning.

Quality alone, however, can be a misleading indicator of brand equity. The key reason for this is

that high quality is not always desired or relevant. Quality that is in excess of what people need or

desire, will not contribute to brand equity. Perhaps a more relevant measure would be brand value:

quality in relation to price.

It should also be noted that perceived quality is often circular in nature. Quality ratings have been

shown to be subject to the "big brand effect" - the bigger the brand, the higher the perceived quality

[Ehrenberg, 1993, 1997; Rice, 2008; Sharp, 2009; Amien, 2009].

Leadership. Related to perceived quality, Aaker [1996] mentions brand leadership / popularity as

another potential measure of brand equity. The rationale behind using leadership as a brand equity

construct is that if enough customers are purchasing a brand to make it a sales leader, then it must

have merit. However, the logic that says size is an indicator of merit is inherently flawed.

Researchers such as Watts [2007] and Campbell & Liddle [2008] have shown that success in terms

of market share has a somewhat arbitrary component to it due to phenomena such as cumulative

advantage.

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The use of popularity as a measure of brand equity could be applicable as a point-in-time measure,

but is less relevant as a long-term construct. The life span of “in-products” is often very short (e.g.

Crocs) so a brand's current popularity is not necessarily a good indicator of its long-term success

[Berger & Le Mens, 2009; Ries, 2009].

Awareness

Brand awareness is considered by Aaker [1996] to be an often under-valued component of brand

equity. While brand awareness is of course a vital and necessary component of brand success, it can

be argued that high levels of brand awareness are a result of "a brand's size, ubiquity and /or scale

of promotional activity" rather than an "indication of a brand's strength in the sense of the

consumer's attachment to it or preference for it" [Feldwick, 1996].

There are also various challenges associated with using awareness as a measure of equity:

In markets where brand awareness is very high, awareness can be a poor differentiator between

brands. In these situations, in order for awareness to be a relevant indicator, it would be necessary to

increase the measurement level (e.g. going from basic awareness to top-of-mind awareness.).

Perhaps the most significant pitfall related to awareness as a measure of brand equity is that it does

not differentiate between positive and negative sentiment. High awareness is not always positive. A

brand might have high awareness for negative as opposed to positive reasons (e.g. due to a product

recall or its bad reputation). Used on its own without another measure to qualify it, awareness can

be a potentially misleading proxy for brand equity.

Associations/differentiation

This set of criteria refers to the perceptions that the market has about a brand and can be further

deconstructed into several areas: perceived value, brand personality, organizational associations and

differentiation. The problem with associations and perceptions is that they are often circular in

nature. Research by Ehrenberg [1993, 1997], Rice [2008], Sharp [2009] and Amien [2009] show

that big brands tend to be rated highly on image attributes and brand users tend to rate their brands

highly on image attributes. A large amount of associations can be predicted based purely on the

brand’s size and usage. As a result, association measures become somewhat less reliable.

Regardless, let us have a look at some of the ways in which it has been suggested that brand equity

can be derived based on associations…

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Perceived value is similar to perceived quality. According to Aaker, there is an 80% overlap

between the two measures. As he puts it,

“Perceived quality has a higher association with the prestige and respect that a brand

holds, while value relates more to functional benefits and the practical utility of buying

and using the brand” [Aaker, 1996]

Somewhat more technically, perceived value refers to the differential between what a customer pays

for a brand and what they feel it is worth paying for the brand. The gap between these two amounts

is considered the perceived value and it has been suggested that this gap can serve as a measure of

brand equity.

Brand personality relies on the idea that people can imagine a brand as a person. Working with this

assumption, surveys present respondents with a list of attributes and are asked to associate the

attributes with various brands. Attributes tend to take the form of “Is a brand I trust” or “A brand

that is fun”. A similar problem to that already mentioned arises here. Certain attributes that reflect

positively on a brand such as “friendly” or “has my interests at heart” will tend to be associated

with the brand used by the respondent and the largest brands in the market will tend to get the lion’s

share of associations, thus undermining its usefulness.

A better way to go about measuring brand personality is to use indirect questions which do not rely

on a perfectly rational person. This bypasses our human need to justify our beliefs and choices by

convincing ourselves that our brand choice was the obvious choice and consequently rating it highly

on positive attributes. Possibly the best technique for measuring brand personality (and indeed,

most other attributes) is to do so indirectly using projective association. Taking its cue from best

practices in psychology, which try to avoid any form of priming or prompting as far as possible,

projective techniques expose respondents to images of people, place, events, etc. and ask them to

imagine themselves in the position of the scene depicted in the image. They are then asked how the

people in the photos would either react or what types of attributes would describe them. Doing it

this way distances the respondent from their own ego, thus making them more open to

subconsciously describing themselves through the images. For example, a respondent might be

asked to choose a photograph of another person. They will then generally choose the photograph

that they most identify with. The researcher can then ask the respondent to associate attributes with

the person in the photo, thus circumventing the ego. A researcher might ask the respondent to

describe the person in the photo’s personality – is he/she fun and outgoing or brooding and intense?

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In addition, researchers might ask respondents to pick a photo that best represents a brand and then

ask questions about the person in the photo, with the implication being that the responses relate

back to the brand personality. Some might argue that since projective association techniques have

been employed in the social sciences for decades, they offer a more reliable way of measuring a

brand or respondent’s personality. In addition, further supporting the indirect approach, research by

John Kearon and Mark Earls [2009] shows that people can more accurately report on others’

behaviour than their own.

When it comes to brand personality, there are two schools of thought. Some believe that

understanding the attributes that set your brand apart can give one an understanding of what it is

about your brand that people like, thus giving you an indication of what makes it special in their

eyes and why they buy it, if only one can get around the circular logic of traditional attribute

association. Projective techniques help in this regard.

Others believe that it is not possible to reduce customers’ beliefs down into specific attributes as we

tend to hold generalised feelings about brands that are not reducible [Rice, 2008], which makes the

entire endeavour somewhat meaningless. Such proponents suggest that we use experimental design

to measure behaviour rather than association attributes.

However, assuming that we can measure a brand’s personality using attributes, it can still be

difficult to create a broadly applicable measure of brand equity from personality attributes alone as

it is not clear that brand personality necessarily changes with brand performance. People may stop

buying a brand, but that is not to say that its personality has changed. In addition, not all brands

trade predominantly on their personality – some use market factors to their advantage (e.g. Wal-

Mart globally and Makro in South Africa).

Organisational associations are similar to brand personality associations, except that they refer to

the organisation rather than the brand (or, organisation as the brand). A few contemporary examples

of this might be Google, Microsoft and Apple. All three brands maintain a stable of products that

are relatively distinct from their siblings, but which also all fall under one collective brand – that of

the organisation. In such circumstances, the people and the values of the organisation become very

important associations in customers’ minds. For example, Apple is known for its innovative designs

while Google is known for its idealistic approach, embodied in their unofficial slogan, “Don’t be

evil”. Brands that fall into this category are essentially selling a lifestyle and set of values along

with their products, and it is on the basis of this that they set themselves apart from the competition.

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It is also on the basis of this that their brand equity is measured. However, a now familiar problem

arises in that not all brands trade on the perceptions of their organisation. As a result, organisational

associations are not a strong candidate for a generalisable measure of brand equity.

Differentiation. The three levels of association just discussed (perceived value, brand personality

and organisational associations) are all based on people’s perceptions and arguably all fall under the

more general category of “differentiation”. Differentiation refers to what makes a brand appear

different from its competitors in the eyes of the market. Particularly in markets where product

offerings are essentially the same (e.g. broadband internet providers or computer processors),

brands need to rely primarily on the different perceptions they engender in people.

Indeed, differentiation appears to be a good candidate for measuring brand equity as it strikes at the

heart of what a brand is in the first place (assuming that we can accurately encapsulate the concept

of ‘differentiation’).

In the early days of business markets, brands did not exist to any large degree. In the case of

commodities such as sugar or wheat, one farmer’s produce was pretty much indistinguishable from

the next. However, even in such commodity markets, the quality and reliability of produce varied

from one producer to the next. In order to make it easier for customers to identify which produce

came from their favourite supplier, farmers would mark their goods in recognisable ways, such as a

name or a symbol on their packaging. With this move, brands were essentially born. Now customers

were able to tell similar products apart. In other words, the products started becoming differentiated

from one another. Fast forward a century or so and we find ourselves in today’s business

environment where, with the realisation that customer perceptions are malleable and of vital

importance to business success, branding has becoming ubiquitous to the point that papers such as

this one are written about the phenomenon. As such, we can really say that differentiation strikes at

the heart of what makes a brand a brand – the bundle of associations and experiences that set one

product apart from another, and it is the value of these bundles in so far as they encourage people to

purchase a product that we are trying to measure with brand equity. Again, a point of contention

here is the idea that we can actually reduce such bundles of ‘fuzzy’ brand perceptions into distinct

associations and experiences (we know that people’s beliefs morph and change over time depending

on their own biases, which means that the fidelity of individual associations and experiences can be

diminished).

Examples of commodity brands might be in categories such as toothpicks or sugar, while PR-

focused brands are most likely to be big players such as the aforementioned Google, Apple and

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Microsoft who all carefully manage their public image in terms of social responsibility, innovation,

etc.

Branded goods

Marketing focus…

Distribution,

volumes

Salience,popularity,

functional needs

Commitment, advertising,

emotive needs

Category threats,‘through the line’

communications

PR effect,corporate

responsibility

MARKET

MIND

Commodities

Branded goods

Marketing focus…

Distribution,

volumes

Salience,popularity,

functional needs

Commitment, advertising,

emotive needs

Category threats,‘through the line’

communications

PR effect,corporate

responsibility

MARKET

MIND

Commodities

Figure 3: Brands sit on a continuum defined by their relative focus between market factors and mind factors. Branding

becomes more important as we move to the mind side of the equation where perceptions and associations become

relevant

Market behaviour

Market behaviour in terms of in-market performance measures such as market share and penetration

are perhaps the most intriguing measures in this list since prominent researchers such as Andrew

Ehrenberg argue that they negate the entire concept of brand equity.

Market share is probably one of the most fundamental measures in any business. Understanding the

size of the slice of the pie that your brand enjoys in the market is one of the most concrete and

measurable metrics available.

More intriguing however are claims by Andrew Ehrenberg [1993, 1996] and collaborators such as

Byron Sharp [2009] that market share is actually the only measure of any importance. They argue

that brand equity is a trumped up idea that really only exists in the minds of marketers. To back up

their claims they show that market share is related to:

• The associations received – big brands receive more associations on positive attributes

• Big brands have more loyal customers i.e. customers that buy the brand more often

• Dual usage overlaps more with larger brands [Sharp, 2009]

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Figure 4: Big brands get more than their fair share of the market in terms of perceptions as well as sales. They are more

ubiquitous and thus more likely to be bought by new customers and they are more likely to be top of mind because they

receive more exposure. The phenomenon of “Double Jeopardy” describes the positive feedback loop which shores up

the size of large brands. They benefit from a double whammy effect – not only are they the largest brands in the market,

but their customers actually buy them more often than smaller competitor brands’ customers buy their brands

However, Farr [1998] points to scenarios where brands are able to deviate from patterns predicted

based on size and Margarita Putter [1993] shows that there is no correlation between brand size and

the strength of the relationships that a brand has with its customers. In addition, a short-coming of

brand size is its inability to predict changes in share, whereas certain measures of brand equity, such

as relationship strength, have been shown to dip in anticipation of subsequent declines. Thus,

market share is good at predicting share when things stay relatively the same, but is not so good at

predicting when things change.

Author and epistemologist, Nassim Nicholas Taleb, uses an apt analogy to capture the poor record

of market share as a predictor when it counts: based on its experience from the previous day, it

would be fair and rational for a turkey to assume that tomorrow will be just like yesterday. Indeed,

based on the pattern observed over the past several years, this would seem to be the case. And, the

turkey would be correct every time, except for the day before Thanksgiving, when its prediction

would be horribly out of synch with reality. Predicting based on the previous period works just fine

right up until you land up on the Thanksgiving dinner table (philosophically speaking, this is known

as ‘the problem of induction”, most famously articulated by David Hume).

Regardless of these shortcomings, market share really is a good measure of a brand’s strength. It is

also one that can be relatively easily gleaned without the need for survey research. However, it fails

to strike at the heart of the ephemeral concept of brand equity as distinct from the physical features

and assets that make up a brand.

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Market Price and Distribution Coverage

In order to compensate for differences in pricing which might skew market share (even though this

might be a valid skew), Aaker suggests indexing price relative to the competition in order to give

one a more ‘objective’ measure of where one’s brand stands in the market.

Similarly, indexing the area of distribution coverage relative to the competition can give one a good

idea of the brand’s spatial strength. This can be measured in terms of the number of stores carrying

the brand or the percentage of people who have access to it. However, needless to say, accurately

measuring price-level statistics when faced with messy markets, product variants and varying

channel prices can be a serious challenge.

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The dimensions of brand equity

Based on the authors’ own experiences, we have found the strength of the relationship that a brand

has with its customers to be one of the best measurements of brand equity, for along with a strong

relationship, come the following benefits which can be translated back into actual business success.

Relationship strength…

1. …affects customers’ willingness to pay a price premium i.e. price elasticity

2. …impacts on how much customers will put up with and whether they will go out of their

way to buy your brand e.g. inertia (to stay with your brand)

3. …can be understood as the differential value unique to the brand that customers place in it

relative to other similar brands i.e. differentiation

4. …makes advocates out of your customers that will defend your brand in the face of

criticism

5. …can act as an early warning sign of impending decline for the brand

6. …is comparable across brands and categories

7. …is non-linear, just like the real world. Relationships change suddenly and dramatically

when they reach tipping points. This is directly at odds with traditional linear, Newtonian,

direct measures of brand equity such as loyalty which cannot account for sudden shifts

Understanding the relationship a brand has with its customers really means understanding the extent

to which a brand is resilient in the face of external shocks, at least in the short- to medium-term.

Indeed, brand equity is arguably only valuable to the extent that it persuades customers to stick with

your brand when presented with rational, functional reasons for either switching brands or reducing

their spend. In most markets where competing brands perform relatively similarly at the end of the

day (such as luxury brands or automobile brands), people don’t use brands so much because they

are vastly different, as because they are perceived as being different. It is this perception that

‘relationship’ measures – the idea of attachment, inertia or gravitational pull towards a brand in the

face of similar competing brands that could rationally be substituted as purchase options.

In non-linear systems sciences such as network theory and chaos theory, brands are conceptualised

as points of attraction, with their own gravity, around which customers group. The strength of the

relationship is a measure of the strength of the gravitational effect holding customers in a brand’s

orbit (and attracting nearby potential customers)1. Viewing brands through the lens of cutting-edge

science, we can imagine the multi-dimensional space that brands operate within (see Figures 4 to

1 More technically, such points of gravitational pull (e.g. brands) are referred to as ‘attractors’

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7). Since any single measure of brand equity can only measure one dimension, they are inherently

doomed to miss out on the information that only additional dimensions can provide. Indeed, even

the Net Promoter Score, one of the most popular single measure indicators of brand equity only has

been shown to have particularly low R2

values when equated to actual brand success [Keiningham,

2007]!

As Figure 4 illustrates, complex systems such as brands2 will tend to reach a level of stability over

time, and this is what Ehrenberg’s research shows – big brands stay big and little brands stay small

[1993, 1996]. It often takes disruptive innovation to shake things up. In addition, we can thank the

Double Jeopardy effect for ensuring that a brand stays in its market position due to the positive

reinforcing effect it describes.

Figures 4 to 7 show how systems theory helps us understand a brand’s position in multiple

dimensions.

Figure 2: "Left to their own devices, systems (even a 'system' as simple as a marble in a mixing bowl) tend to sink to a

state of minimum energy and maximum entropy - provided there is no input of energy from outide." [Gribbin, 2004] A

low energy state equates to a stable state, of the kind described by Ehrenberg for brands

2 Brands are complex systems made up of the interactions of marketers, customers, media, the environment, etc.

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Figure 3: "The state that systems settle into is called an attractor. In the example shown [in Figure 4], the attractor is a

single point at the bottom of the bowl. But an attractor can also be a spread-out region, as in this illustration. The marble

on the hill is bound to roll off into the valley, but everywhere in the valley bottom is equally attractive." [Gribbin, 2004]

Different brands come to rest in different basins, or stable states. These basins collectively represent the landscape of

the market, where the depth of each brand’s basin represents the size of its market share.

Figure 4: Brands can be thought of as operating in a multi-dimensional space (i.e. a ‘market’). Thus, in order to derive

an accurate measure of brand equity, we need to measure as many of these dimensions as possible [Source: Wikipedia]

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Figure 5: Non-linear dynamical systems science allows us to visualise branded entities in multiple dimensions, similar

to our understanding of how bodies orbit around each other in our solar system (a 3-dimensional example)

[Source:Wikipedia]

Finally, the last word on this section goes to Paul Feldwick [1996] who sums up the role of multiple

dimensions as follows:

“When we look for an operational definition of brand equity, we are asking the wrong

question. Brand equity is necessarily a vague concept, like ‘personal health and fitness’, or

‘a sound economy’. These concepts imply general questions: how well are we doing now?

How well can we expect to do in the future? Such questions are not answered fully by any

one measure. At certain points in time, one or more measures may be of crucial importance

– such as cholesterol level or inflation. But there is also a danger that continuing to

concentrate on one measure to the exclusion of others creates its own problems (low

inflation leads to unemployment; low cholesterol diets cause depression). Brand equity

needs to be approached in the same spirit.”

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Using tech and social media to measure brand equity

Social media is all the rage these days, with some going so far as to pronounce ‘traditional’ market

research dead in the face of freely available tools and massive sample sizes [for example,

Copernicus Consulting, 2010]. However, some counter that social media is not the Holy Grail, and

that people do not really talk about brands all that much in their daily lives, except in passing.

Often, the people whose voices we hear online are the most vocal and generally the exception to the

rule. These are the people that have a particular axe to grind or message to spread. This raises the

question of whether listening in on online conversations really captures natural conversation and

sentiment.

While the death of traditional survey research may be overrated and despite the concern just

mentioned, let us look at some of the ways in which social media might be able to help us measure

brand equity.

Boutique research house, iCrossing [2009], refers to “connectedness” as the core metric of the

digital age. They describe the concept as follows:

“Connectedness is marketing philosophy. It is a framework for, and a measure of how intimate

a brand is with its audiences. It’s a characteristic of a brand, a ‘state of being.’ Think of it as a

Zen stage in brand evolution. After all, a brand needs to be a living organism in today’s

marketing world, not an object, not a loudspeaker yelling at people. Connectedness is a way of

thinking about how successful brands do marketing: focusing on audiences, not targets;

engaging in dialogue, not shouting; and developing trust that is meaningful and lasting.”

Their definition does a good job of capturing the new idea of what a brand is – that is, a personified

entity that symbolises a set of values and ideals which customers subscribe to, and which they

expect to interact with on a one-on-one level through co-creative exchanges that both define the

brand and themselves. They expect this level of intimacy because technology allows it. As the line

blurs between business and personal life, customers are now willing partners in the collaborative

consumption experience.

iCrossing present three criteria for measuring brand success in the digital age:

1. A greater awareness of a brand’s audiences

2. Agility in its customer-facing infrastructure

3. Real activity and interactions with customers “on the ground” in cyberspace

SAMRA 2010 19

Connectedness refers to a brand’s visibility, its usefulness, its usability and the desire and

engagement it creates with customers. Using this general framework, we can evaluate some of the

other metrics that have been put forward as measures of brand equity in digital environments.

Figure 6: iCrossing's framework for what makes a connected brand

Connections: followers and fans

How many people are listening to what you say? How many publicly label themselves as fans of

your brand? Can this be used as an indication of your brand equity? Intuitively, this seems to make

some kind of sense. Quantifying the number of Facebook and Twitter followers that your brand has

gives one an idea of the critical mass of the “cult” that has grown up around your brand and this

might be used as an indication of the gravitational pull it has.

There are a few places that a brand might want to look at to quantify the number of followers or

connections it has with customers. Facebook, Twitter, Flickr and YouTube are amongst the most

popular websites and thus relevant to most brands.

In addition, it is valuable to know how many unique visitors come to your brand’s website and how

many other sites refer to your website. All of these give you an idea of how many people are

connected to your brand.

SAMRA 2010 20

However, there are a few caveats to such measures. For one thing, there is a difference between

active fans and passive fans – those that engage with your brand and those that ignore your status

updates. Most platforms make it very easy to add new links, often without any further thought to

their existence.

Engagement

Merely knowing how many people follow you does not give you an indication of whether those

people are actively listening to what your brand has to say or engaging in two-way co-creative

experiences. For example, Twitter is a good medium for gaining followers, but in the deluge of

information that flows through most Twitter users’ streams daily, what is to say that they even see

or care about what your brand is saying? Some have suggested that a better way of working out

whether people are really processing what you have to say is to be ‘listed’. Twitter gives users the

option of grouping the people that they follow into ‘lists’ united under similar topics. For example,

someone might put all the celebrities that they follow together under the list ‘People I follow

because they are famous”. It has been argued that because creating lists requires an additional level

of cognitive thought, having your brand listed implies that it has added influence on the person who

listed it and those who subscribe to the list because it stands out from the everyday noise of the

general Twittersphere. [Troy, 2009; Zeigler, 2009] As one blogger put it:

“Anything to do with numbers of followers is now dead. WHAT KIND OF LISTS you are

on will be far more important. Who cares if someone has 145,000 followers if no one will

put him on a list because they don't like his Tweet style?” [Scoble, 2009]

Twitter lists are just the most recently touted measure of engagement online. Other popular

examples are:

• Tweets about your brand

• Shared and recommended links on Reddit.com and Digg.com

• Social bookmarks on Del.ic.ious

• Brand website: visits per person to your site

• Brand website: average length of stay

• Discussions, posts and contributions on Facebook, Flickr, YouTube, etc.

[iCrossing, 2009]

Klout is a tool that measures users’ (and brands’) ‘clout’ or influence on Twitter by looking at the

number of followers an account has. It also looks at how often the account user engages in two-way

conversations with others, how often the user’s messages are ‘retweeted’ (i.e. passed on to others)

SAMRA 2010 21

and how often the user is mentioned by other users. It combines all these measures into a single

number “Klout” score. It also provides more in-depth statistics on the various measures and

attempts to categorise the individual or brand based on their activity (according to Klout, Pepsi is a

“persona”).

Search ranking

Another digital measure of your brand’s perceived strength is its ranking in search engines. Google,

Yahoo and Bing (Microsoft) all employ advanced ranking algorithms to determine the relevance

and influence of search terms (such as your brand name) and websites. They use ‘spiders’ – digital

agents that scour the links between websites, scanning for keywords – to create network metrics

such as incoming links, outgoing links and mentions in other places that determine the position a

concept needs to be placed in the search results for a related concept (e.g. “fast cars” and “Ferrari”).

While the nitty-gritty details of their algorithms are closely guarded secrets (they wouldn’t want you

to game the system after all), one can rest assured that appearing in the top search results for a term

related to your category is a very good indication of your brand’s perceived (and real?) strength.

SAMRA 2010 22

Figure 7: Klout measures both a brand or person's connections and its engagement with its network [klout.com]

SAMRA 2010 23

Figure 8: Nike’s website is the fifth natural search result when searching for the term “running shoes” via Google

SAMRA 2010 24

Sentiment analysis

Sentiment analysis is a slightly more advanced methodology than the raw number of connections or

fans a brand has. Similar to the spiders that search engines employ to scour the web, sentiment

analysis often relies on machine learning and automated agents to scrape brand mentions off

popular blogs, websites and social media platforms. The machine learning comes into play in trying

to automatically deduce the nature of the sentiment displayed in messages that mention a brand. For

example, the word “sad” in the same sentence as “McDonald’s” in a Facebook status update may be

taken as an example of negative sentiment. By collecting and analysing brand mentions, it is

possible to gain an idea of the balance between negative and positive sentiment for one’s brand.

Machine learning and such ‘intelligent’ online agent behaviour may be impressive, but not everyone

is convinced of its effectiveness just yet. As Tom Webster of the blog BrandSavant puts it:

“Actually, I’m not a huge believer in sentiment analysis–yet–for two reasons: it isn’t yet as

accurate as an intern would be, and even if it were–I’m not even sure what you do with it

other than track it over time. There is certainly no correlation I am aware of between

brand mentions and sentiment, or even “social media” sentiment and actual sentiment.

Taking snapshots of sentiment is a lot like day trading–anecdotal events will “spike”

sentiment one way or the other over the short term, and while you should never ignore a

crisis, I don’t think you need sentiment analysis to tell you if you’re in trouble. Sentiment

analysis over the medium and long term, however, may be a useful metric to track the

effectiveness of your social media campaigns over time.” [Webster, 2010]

SAMRA 2010 25

Examples of tools that measure sentiment are TweetFeel and MatterMeter:

Figure 9: When is it able to recognise it, TweetFeel highlights negative mentions of a brand in red and positive

mentions in green [www.tweetfeel.com]

Figure 10: MatterMeter is another sentiment analysis tool. In this case, 85% of users would care if BMW no longer

existed [www.mattermeter.com]

Listening tools

Much online social media research falls under the umbrella of “listening research”. Several

platforms have emerged as tools for brands that want to listen in on what people are saying about

them so that they can join the conversations and manage their relationships.

SAMRA 2010 26

These platforms tend to offer dashboard-like interfaces where metrics are gathered from across the

most popular social media and web platforms, ranging from posts and discussions to sentiment

analysis. Some of the most well-known platforms are:

1. Radian6 – “Radian6 gives you a complete platform to listen, measure and engage with your

customers across the entire social web” (www. radian6.com)

2. Conversition – “Where market research meets social media - evolisten represents the next

generation of full service market research, one that listens to digital word of mouth.”

(www.conversition.com)

3. Trackur – “Online reputation monitoring” (www.trackur.com)

Figure 11: Conversition is an example of a commercial listening tool that purports to offer brand’s a 360° view of their

online presence

As to whether these methods will replace traditional brand equity research, this question is beyond

the scope of this paper. However, given how some major online players are teaming up with

traditional research agencies (e.g. Google and WPP), it seems more likely that the two industries’

paths will criss-cross, with some casualties and some amalgamations along the way.

SAMRA 2010 27

What is clear from all these tools though is that their primary measure is the number, the strength

and the direction of the relationships that online entities have with their stakeholders and networks.

We can think of the relationship as the link that connects brands to customers. Understanding the

strength and nature of this bond is an important area of network research and a good reason to think

of relationship measures as particularly relevant in the digital world.

Relationships are central to understanding brand strength in the digital age where customers

perceive brands as personified entities that they expect to be able to interact with and receive

timeous and authentic responses from.

Conclusion

The world is changing, or at least our conception of it is. Confused? You should be. As technology

hurtles along, we are exposed to more and more information, which increases the challenge of

understanding brands in simple terms. We used to watch television on our rounded 2-dimensional

screens and this was good enough. Then along came 3-dimensional experiences such as James

Cameron’s Avatar which opened our eyes to an entirely new vista of sensory stimulation.

A similar thing is happening today in the market research world thanks to the digital and social

media revolution. We now have more data at our fingertips than we know what to do with.

However, thanks to technology, more information means we are, in theory, capable of seeing brands

in a new light, and this new light has more than one dimension. We need to start thinking in

multiple dimensions and visualising the brands we work with in these dimensions. This is a

necessary step if we are to develop a unified theory for branding that helps us to understand the

complex systems that brands operate in and the subtle ways in which we can shape them.

SAMRA 2010 28

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SAMRA 2010 32

Appendix 1: Major research houses’ dimensions of brand equity

Millward Brown’s Brand Dynamics

1. Presence > Relevance > Performance > Advantage > Bonding

TNS’ Conversion Model

1. Needs Fit/Satisfaction

2. Attractive of alternatives

3. Involvement

4. Ambivalence

Synovate’s Brand Value Creator

1. Brand Relationships

2. Category Involvement

3. Overall Evaluation

4. Importance to Person

5. Relative Appeal

6. Theory of Commitment

Ipsos’ Equity Builder

1. Differentiation

2. Relevance

SAMRA 2010 33

3. Popularity

4. Quality

5. Familiarity

Research International’s Brand Energy

1. Status (emotional and functional)

2. Momentum (emotional and functional)

SAMRA 2010 34

Young & Rubicam’s Brand Asset Valuator

SAMRA 2010 35

Appendix 2: Results from Longitudinal research linking Purchase Intention to subsequent Behaviour [Hofmeyr, 2007]

Author(s) Product Categories Nature of Study Results

Juster, F.T. (1966) Motor cars, appliances Measure purchase intention using 11-point

probability scale. Correlate with whether

or not product subsequently bought.

R = .43 (motor cars), .24 (appliances).

R2 = .18 and .06 respectively.

Bonfield, E.H. (1974) Brands of grape juice Measure purchase intention using 7-point

likelihood scale. Correlate with whether or

not the brand subsequently bought.

Results are significant.

Average correlation: .40. R2 = .16

Sewall, M.A. (1981) Women’s apparel Mall intercept. Measure purchase

intentions using 5-point scale. Compare

with subsequent purchases.

Results are significant but poor.

R = .27; R2 = .07

Miniard, P.W., C.

Obermiller and T.J. Page

(2982)

Brands of soft drink 0.49

LaBarbera, P.A. and D.

Mazursky (1983)

Margarine, coffee, toilet

paper, paper towels,

macaroni

Diary study: Purchase intention measured

every two weeks for 20 weeks. Correlated

with subsequent brands bought.

Purchase intentiont � Purchase t+1: R =

.24

Average R2 = .06

Morwitz, V.G., E, Johnson

and D. Schmittlein (1993)

Motor cars, PC’s Measure intention to buy in next six

months, every six months. Longitudinal

research measures actual behaviour.

No correlations reported. On average,

29% of those who say they will buy, do;

which means that 71% don’t.

Bemmoar, A.C. (1995) Multiple durable

categories

A meta-analysis of published studies in

which PI was measured and subsequent

behaviour was observed.

No correlations. 64% of those who say

they will definitely buy, don’t. Most

purchases come from those who say they

will not buy.

Chandon, P., V.G. Morwitz

and W.J Reinartz (2005)

On-line grocers, motor

cars, PC’s

Measure purchase intention. Observe next

purchase or purchase within six months

Correlations: .44 (grocer), .12 (motor

cars), .16 (PC’s). Average correlation: .24

(R2: .06)

Seiders, K., G. Voss, D.

Grewal, and A. Godfrey

(2005)

High end clothing and

home furnishings

Measure purchase intention. Correlate with

52 weeks of behaviour in a data-base.

Purchase intention � No of visits: R =

.11, R2 = .01; and Amount spent: R = .10,

R2 = .01

Perkins-Munn, T., l.Aksoy,

T.L. Keiningham, D. Estrin

(2005)

Trucks, Pharmaceuticals Measure respondent attitudes. Record

subsequent behaviour over a 15

month period.

Intentiont � Repurchaset+1: R = .44 and

.65 respectively. Average R2 = .31

Intentiont � SoWt+1: R = .47, .45

respectively. Average R2 = .21

SAMRA 2010 36

Appendix 3: Results from Longitudinal research linking Customer Satisfaction to Retention and Share of Wallet [Hofmeyr, 2007]

Author(s) Product Categories Nature of Study Results

LaBarbera, P.A. and D.

Mazursky (1983)

Margarine, coffee, toilet

paper, paper towels,

macaroni

Diary study: Satisfaction measured every

two weeks for 20 weeks. Correlated with

subsequent brands bought.

Satisfactiont � Purchase t+1: R = .20

Average R2 = .04

Jones, T.O. and W.E. Sasser

(1995)

Manufacturer of industrial

supplies

Longitudinal study: Measures customer

satisfaction and compares with subsequent

retention-defection.

Extremely satisfied customers six times

less likely to defect – but doesn’t report

overall retention-defection rates.

Bolton, R. N. (1998) Telecommunications Longitudinal survey of customers: Two

waves. Models satisfaction and other

inputs against length of customer duration.

Satisfaction accounts for most of the

variance explained (42%). But Bolton

fails to report percent variance explained!

Mittal, V. and W.A.

Kamakura (2001)

Motor cars Customer satisfaction in 33rd

month of car

ownership – compared with whether brand

switched or not when new car bought

Repurchase rate of dissatisfied customers

= 48%; repurchase rate of satisfied

customers = 72%.

Verhoef, P. C. (2003) Financial services Measure attitudes inc. satisfaction.

Modeled against subsequent retention-

defection.

Regression model acceptable: R = .43; R2

= .18. But satisfaction fails to make the

model.

Capraro, A.J., S.

Broniarczyk, and R.K.

Srivastava (2003)

University health plans Measure attitudes inc. satisfaction, one

month before decision. Re-contact after

decision.

Regression model including satisfaction is

significant but R2 is only .08.

Keiningham, T.L., T.

Perkins-Munn, and H.

Evans (2003)

Financial services Measure attitudinal satisfaction. Obtain

customer share of wallet from 3rd

party

sources. Fuse the data and analyze.

A dichotomized satisfaction scale (1-8; 9-

10) lifts SoW from about 10% to 15%.

Average R = .27; R2 = .07

Bowman, D. and D.

Narayandas (2004)

Processed metals Measures attitudes inc. Satisfaction.

Compares with subsequent data-base

information.

Satisfaction correlates poorly with SoW;

and does not correlate with profitability.

Gustaffson, A. M.D.

Johnson, and I. Roos (2005)

Telecommunications Measure attitudinal satisfaction. Correlate

with churn defined as ‘time spent as a

customer’

Satisfaction � Churn: R = .13, R2 = .17

Seiders, K., G. Voss, D.

Grewal, and A. Godfrey

(2005)

High end clothing and

home furnishings

Measure attitudinal satisfaction. Correlate

with 52 weeks of behaviour in a data-base.

Satisfaction � No of vists: R = .07, R2 =

.00; and Amount spent: R = .07, R2 = .00

Perkins-Munn, T., l.Aksoy,

T.L. Keiningham, D. Estrin

(2005)

Trucks, Pharmaceuticals Measure respondent attitudes. Record

subsequent behaviour over a 15

month period.

Satisfactiont � Repurchaset+1: R = .24 and

.22 respectively. Average R2 = .05