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Brand user profiles seldom differ


Abstract and Figures

It is widely thought that different brands appeal to different types of users, or should do so. Advertising and other marketing activities are often based on this presumption, and countless segmentation studies are therefore carried out. To examine this supposition we have compared the user-profiles of the ten or so leading brands in each of some 40 industries ― such as Kodak, Agfa, and Fuji for photographic film, or AA, BA, and UA for airlines ― on their users’ attitudes, lifestyles, demographics, and media exposures. The results demonstrate that users of directly competing brands hardly differ in their profiles. That is, brand segmentation generally does not exist ― substitutable brands usually compete in what for them is a single unsegmented mass market, whatever its overall structure may be. Exceptions are rare and generally relate to submarkets which are functionally different, such as caffeinated versus decaffeinated coffee. The analysis procedure used here is simple. It is outlined in some detail so that it can be readily applied to other data. Implications of the lack of brand segmentation in terms of targeting and advertising are discussed in the paper: basically that your market is like your competitors’ market.
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Competitive Brands’ User-Profiles Hardly Differ
Rachel Kennedy, Andrew Ehrenberg, Stephen Long
It is widely thought that different brands appeal to different types of users, or should do
so. Advertising and other marketing activities are often based on this presumption, and
countless segmentation studies are therefore carried out.
To examine this supposition we have compared the user-profiles of the ten or so leading
brands in each of some 40 industries such as Kodak, Agfa, and Fuji for photographic
film, or AA, BA, and UA for airlines on their users’ attitudes, lifestyles, demographics,
and media exposures.
The results demonstrate that users of directly competing brands hardly differ in their
profiles. That is, brand segmentation generally does not exist substitutable brands
usually compete in what for them is a single unsegmented mass market, whatever its
overall structure may be. Exceptions are rare and generally relate to submarkets which
are functionally different, such as caffeinated versus decaffeinated coffee.
The analysis procedure used here is simple. It is outlined in some detail so that it can be
readily applied to other data.
Implications of the lack of brand segmentation in terms of targeting and advertising are
discussed in the paper: basically that your market is like your competitors’ market.
A great variety of approaches to market segmentation have long been discussed and
practiced, as reviewed for example by Dickson and Ginter (1987), Lury (1990), Dibbs
and Simkin (1996), Anschuetz (1997), Cahill (1997), Campbell (1998), Mitchell (1998),
Gunnarson (1999), Kotler (1999), and others. Lilien and Rangaswamy (1992), typically
said that “Segmentation, the process of dividing the market into consumer groups with
similar needs, is essential for marketing success”.
Segments of apparently similar consumers may even be given names (e.g. “Worrier”,
“Sensory”, “Sociable”, or “Independents”) with advertising and products targeted
accordingly: Crest for those who want to stop decay, Macleans for Whiteners, Colgate
(stripe) for those interested in flavour, and so on (e.g. McDonald and Dunbar, 1998).
Cornish (1990) has said that only by observing how interests vary between such groups
can one work out the reasons why people behave the way they do.
Many organisations therefore invest heavily in segmentation studies. Some research
agencies have become specialists, but mostly focusing on new or proprietary
segmentation analysis techniques, as illustrated in the box below. They virtually never
report any real brand segmentation results, i.e. that a named (or coded) brand A in fact
appeals to a markedly different population of consumers than does the competitive brand
“Segmenting Markets . . . the AID Technique” (Assael, 1970).
“CHAID . . . Modelling Segmentation” (Babinec, 1990).
“Segmenting . . . With Conjoint” (Green & Krieger 1991).
“. . . Benefit Segmentation” (Dubow 1992).
“. . . A Canonical Correlation Approach” (Bologlu et al 1998).
“The Car Challenge: 21 techniques compared” (Colombo et al 1999).
“HINoV: Improved Market Segment Definition (Carmone, et al 1999).
And so on.
Collins (1971) however noted long ago that clustering or segmentation techniques
typically ask what is the “best” grouping of the data that can be made, but ignore the
more basic question whether any useful groupings of the data can actually be made at all.
In this paper we therefore ask whether the user-profiles of directly competing brands
really differ. The results say “NO”, i.e. brand segmentation hardly exists, if at all. It is
consistent with that conclusion from our analyses that few if any of the other papers in
the voluminous segmentation literature have actually reported any coherent brand-
segmentation results.
A product may of course have functionally different variants for different needs, e.g. large
and small pack-sizes, tamarind as well as tomato flavours, 2-, 3-, 4- and 5-door car-
models, or other “Stock-Keeping Units” or SKUs more generally (e.g. Singh 2000). And
each of these variants may have its own more or less “segmented” following. But because
of competition all brands as such tend largely to have each of these SKUs (except for
marginal ones). Hence brand profiles as a whole do not differ (Ehrenberg, Barnard and
Scriven 1997, Ehrenberg et al 2000).
We build in this paper on previous findings of a lack of brand segmentation which were
based on consumer-panel data (Hammond et al 1996). We now greatly extend these
earlier findings to more categories, far more potential segmentation variables, and a
different type of data. The results agree with the Titford and Clouter (1998) view that for
competition between brands, a buyer is a buyer is a buyer, whosoever he or she may be.
The lack of brand segmentation explains why the real marketing issue is not “who buys”,
but “how many buy”.
Exceptions with distinct sub-markets or functionally different groups of brands can show
up. But they are rare in the extensive brand-data analysed here. We will also note that the
practical sales implications of any brand segmentation may in any case be overblown.
The Data Analysed
Our analyses are based on extensive tabulations from BMRB’s Target Group Index (TGI)
for 42 industries in the UK. The TGI is an on-going self-completion survey of brand-
buying and consumer attitudes across many categories including fmcgs, durables, and
financial and leisure services, together with standard demographic and media usage
information. For each category, respondents state if they buy/use/serve the category, how
often, and which specific brands. Potentially relevant attitudes, opinions and interests are
captured through over 200 attitudinal and lifestyle statements with which respondents
rate their degree of agreement on a 5-point scale. Some examples are:
I loathe doing any form of housework It is important that my family thinks I’m doing well
I love to buy new gadgets and appliances In a job, security is more important than money
I tend to spend money without thinking We normally have roasts on Sundays
It’s worth paying extra for quality items I often enter competitions featured in packets or labels
A real man can down several pints of beer at a sitting People have a duty to recycle products whenever
I listen intently to radio news I make decisions based on gut feel
I enjoy eating foreign food When shopping I budget for every penny
While the standard sample for the survey is 25,000 adults, the cases we had to analyse
varied from about 500 to 100,000 adults/occasions, averaging at over 10,000. Two
batches of TGI data were analysed, with the same outcome. A first batch was for all
brands in 13 industries with 100 attitudinal variables; the second batch of data, some two
years later, compared the top 10 brands in 30 other industries, with over 200 attitude
variables. (Light bulbs were common to both batches and gave very similar findings, as
did everything else.)
As an example of the raw data, Table 1 shows the number of TGI respondents who used
each of the top 10 credit card brands in the UK, broken down by Gender and Age. The
full file is much larger, with 280 such variable-columns (i.e. the other demographics,
media, and attitudes). The brands in the table are ordered by market share, to facilitate
pattern recognition. (Other aspects of data presentation are briefly noted in the
Table SEQ Table \* ARABIC 1 Raw Data for Credit Card Holders, by Gender and Age
Gender Age (years)
Credit CardUsersMaleFemale15-1920-2425-3435-4445-5455-6465+Barclaycard Visa 3619 1840
1779 45 157 626 832 787 654 518TSB Trust card 1654 848
806 23 43 292 328 347 310 311Access Natwest 1224 655
569 13 23 197 294 276 255 166Access Midlands 1155
598 557 15 37 204 277 243 227 152Barclays MasterCard
1065 639 426 10 30 157 224 249 212 183Access Lloyds
896 486 410 13 26 161 201 196 179 120B. of Scotland
Visa 476 267 209 5 10 80 101 122 90
68Midland Visa 472 262 210 6 30 87 92 97 85 75TSB
MasterCard 471 265 206 7 10 78 79 106 95 96Co-op
Bank Visa 463 243 220 4 7 71 110 112 88 71Total
(10 brands)11495 6103 5392 141 373 1953 2538 2535 2195 1760Source:
The Analysis: Deviations from the Average Brand Profile
Instead of using the complex but to us opaque segmentation techniques such as AID or
CHAID that are commonly used in segmentation studies, we more simply sought to
contrast the profiles of the different brands in each product category. We did so
parsimoniously by comparing each brand’s profile against the profile of the average
brand in that industry. This is a simple yet effective process that we now explain.
To show the make-up of each brand’s customers, percentages profiles were calculated
from the raw data as in Table 1, together with the profile of the (unweighted) average
brand (in effect a category profile). This is illustrated for Gender and Age again in Table
Table 2 Brand Profiles as Percentages
Market Gender Age (years)
Credit CardUsersShare % MaleFemale15-1920-2425-3435-4445-5455-6465+Barclaycard Visa 3,600 31%
51 49%141723221814TSB Trust card 1,700 14% 51 49%131820211919Access Natwest
1,200 11% 54 47%121624232114Access Midlands 1,200 10% 52
48%131824212013Barclays MasterCard 1,100 9% 60 40%131521232017Access Lloyds
890 8% 54 46%231822222013B. of Scotland Visa 480 4% 56
44%121721261914Co-op Bank Visa 470 4% 53 48%121524241915Midland Visa 470
4% 56 45%161820211816TSB MasterCard 460 4% 56 4 4% 2 2 17 17 23 20 20 The
Average Brand 1,100 10% 54 46%131722221916The result showed few differences
between the brand profiles. Thus in Table 2, on average 54% of card users were male.
Eye-balling shows that this was much the same for each brand. Again for age, all the
credit cards had very few customers under 24 (4% on average), and about 20% in each of
the five larger 10-year or so age-groupings that the TGI used.
To quantify these brand similarities explicitly, the deviations of each brand’s profile from
the average profile were calculated, as illustrated Table 3. The largest deviation in the
table is 6 percentage points for Barclays MasterCard. A 6-point difference in profile is
however far too small to merit any separate marketing action. (The paradox about
marketing’s passion for segmentation is that the results are seldom implementable, or
indeed implemented.)
To summarise all these deviations, the average size of the deviations (ignoring their sign)
was computed for each measure and for each brand, i.e. the traditional Mean Absolute
Deviation or MAD, as also shown in Table 3. (All individual deviations over 5
percentage points were marked in bold in our computer output, to signal any larger
Table 3 Deviations of the Brand Profiles from the Average Brand
Gender Age (years)
Credit CardMaleFemaleMAD*15-1920-2425-3435-4445-5455-6465+MAD*Barclaycard Visa -3
330 1 0 1 -1 -1 -1
1TSB Trust card -3 3 300
1 -2 -2 -1 3 1Access Natwest -1 1
10 -1 -1 3 0 2 -2 1Access
Midlands -2 2 20012-2
0 -3 1Barclays MasterCard 6** -6** 6 0
0 -2 -1 1 1 2 1Access Lloyds 0
1Bank of Scotland Visa 2 -2 20 -1 0 0 3
0 -1 1Co-op Bank Visa -2 2 2
0 -2 -2 2 2 0 0 1Midland Visa
1 -1 10 3 2 -2 -2 -1
01TSB MasterCard 2 -2 20 -1
0 -5 0 1 5 2MAD* 2 2 2
0 1121121* Mean Absolute
Deviation ** Individual deviations of 5+ are also in bold
For the credit card brands here there was virtually no difference by the gender of their
users (an average MAD of 2, even including the larger Barclays value), or by age (lower
MADs of 1, since the absolute numbers were smaller with more classes). Table 4
illustrates this also for five typical attitude/lifestyle variables: most of the MAD’s still
average round 1 or 2 points.
Table 4 Deviations for Five Individual Attitude Statements
I am happy
with my
standard of
I try to keep
up with
I always
look for
I can't bear
untidiness MAD
0 -1 0 -1 0 0
TSB Trust
0 -2 -4 2 2 2
1 0 0 -2 1 1
-1 0 0 -3 1 1
1 1 1 1 1 1
2 2 2 -2 0 2
B. of
Vis a
1 0 2 2 2 1
Co-op Bank
Vis a
1 4 2 2 -1 2
Vis a
1 -1 1 -2 -2 1
-2 -2 -4 513
MAD 1 1 2 2 1 1
This form of analysis was repeated for every variable, for over 110,000 individual
deviations in the 42 industries.
The Results
Overall, the individual brands’ percentage profiles deviated from each other by an
average of 2 or 3 percentage points, which in our view is small in effect zero. Thus the
difference between 15% of users of Card A saying “I try to keep up with technology”
rather than Card B having only 11% technofreaks is not actionable.
Only around 8% of the individual deviations were more than 5 percentage points, and
even these larger deviations averaged at only about 9. Just 2% of individual deviations
were 10 points or more. Noticeable deviations were therefore exceptional, and were still
small even when they did occur. Brands therefore rarely differed from the average brand
in their category, and when they did so it was not by much, nor was it of practical
Table 5 presents the global results, that is the average MADs for all of the demographics,
all the media variables, and all attitudinal variables, for each of the 42 categories (ordered
on their total unweighted MAD’s for visual convenience). Three of the average MADs
are greater than 3 (for Cigarettes, Tessas and Cat food) but are explained simply by their
being based on relatively small samples.
The central figure is the overall average MAD of 2 (or 2.1 more precisely) in the bottom
right-hand corner. This pinpoints the general lack of effective deviations between the
brand and category profiles.
Table 5 Summary MADS for 42 TGI Categories
MediaAtti-tudesAVE-AGECigarettes 4466Vitamins3122Tessa accounts 6355Washing liquids 3122Cat food
3144Grocers 3122Mints3133Yo g u rt 3 1 2 2Toothbrushes3133Light bulbs (1)2122Private health ins42 33Car
tyres2122Sweets3123Stain removers 2122Crisps3133Light bulbs (2)3112Toilet soap3123Car insurance 3112Packaged
hol3133Coffee 2122Dry batteries3123Home contents 2112Other chocolate3122Paint 3112Kitchen rolls3132Shampoo
2122Nuts3122Airlines 2111Chocolate Bars3122Camera film 2111Toothpaste2122Headache tablets2111Toilet
paper2122Cars 2111Computers 3222Credit Cards 2111Baked beans 3122Mortgages 2111Record shops
3112Fuel2111Store retail cards 3122Retailers 1111Computer games 3122AVERAGE3122The occasional
larger deviations which sometimes occur tend at times to cluster for several brands and
relate to submarkets rather than to a specific brand (as would have had to characterise
real brand segmentation). But such subpatterns are usually already well-known (or are
“as to be expected”).
Regionally for example, there is a Scottish sub-market for three locally-based Scottish
banks (Bank of Scotland, Royal Bank of Scotland, and Clydesdale). For RTS breakfast
cereals, children somewhat prefer the pre-sweetened types (i.e. “children’s brands” see
already Hammond et al 1996, or ask Kellogg’s). This also seems to show up here in slight
preferences expressed by families with children for milk chocolate among different kinds
of confectionery. Some clustering of responses across different categories are also
noticeable for “Green”, for Diet, and for Exercise. Such systematic subpatterns in the data
can be pursued further (best probably someone with specialised knowledge of that
market). But they are small and rare.
Three Technical Questions
Have the wrong Segmentation Variables been used?
It seems highly unlikely that potentially powerful variables have been consistently
omitted in a widely-used and long-running and virtually public measurement tool
such as the BMRB’s Target Group Index. But it is easy to check any new candidate
segmentation measure.
One particularly relevant segmentation variable for any given brand X is the other
brands which buyers of X also buy is there any clustering, e.g. buying of brand X
going with buying of brand Y but not with brand Z? This can be well examined with
consumer-panel data, but that has already shown virtually no brand segmentation
(average MADs of 3 points, on somewhat smaller samples than here Hammond et
al 1996).
Lack of segmentation has also been the case with very extensive panel-based
segmentation studies of television viewing, e.g. cross-analysing viewers of
programme A by the other programmes they also viewed, whether of the same or a
different genre (Ehrenberg 1986; Barwise and Ehrenberg 1988).
Possible Measurement Biases?
BMRB’s TGI covers many different products and potential segmentation variables in
a lengthy self-completion questionnaire. This could be subject to measurement biases.
Such biases would however matter little here since they would be much the same for
the different brands that are being compared.
Sample Sizes?
Sample sizes of category users in the TGI are mostly large, averaging at 10,000 as
noted above. (They could easily be increased by using TGI data over two or more
years.) But for smaller brands, samples of their users are of course smaller
typically they are less than 1,000 for the five smaller brands in Table 2. This shows up
in the slightly larger MADs for these smaller brands as in Table 4 (as was also noted
by Hammond et al 1996), although it is exceptionally not so in Table 3. Typically
also, the three larger MADs in Table 5 greater than 3, for Cigarettes, Tessa
accounts, and cat food were based on relatively small samples of less than 5,000
category users.
Three broad types of possible segmentation need to be distinguished:
Brand segmentation, which refers to possible differences between the users of different
brands, say Kodak, Agfa, and Fuji 35mm film. Such differences are rare as we have seen
Category segmentation (or sub-category segmentation). This is quite common and relates
to differences between users of functionally distinct products (e.g. Cameras versus
Projectors) or sub-types of products (e.g. Professional versus Disposable cameras).
Category segmentation is probably more usefully thought of simply as “knowing
your market” (e.g. that cat food is mostly bought by cat owners, or even perhaps
just by certain discernible types or “segments” of cat owners). Such sub-markets are
usually already well-known and tend to persist. It is rare that “sophisticated”
segmentation or clustering techniques are needed to discover or rediscover them. It
is also rare that use of such techniques has successfully done so in the past (as
noted, no lasting cases seem to have been reported in the literature).
Thirdly, SKU segmentation. Thus different product-variants or Stock-Keeping Units may
each have their distinct followings, i.e. on dimensions such as pack-size, flavour, car-
models, etc. and then myriads of bar-coded interactions of these dimensions (the
1 lb pack-size of unsalted Lurpak butter). Almost nothing systematic seems to be known
about that, i.e. no generalisable results about SKU loyalty, switching, or segmentation
(Fader and Hardie 1996). This is one of the bigger agenda items for South Bank’s R&D
Initiative in the coming 2–3 years.
These three forms of segmentation brands, categories and SKUs are however
seldom distinguished in the literature (see earlier references). But they should be, because
both for category and SKU segmentation, functional differences are of the essence and
usually totally self-evident (tea differs from coffee, and liquid detergent from tablets). In
brand segmentation on the other hand as discussed in this paper, product differences are
hidden or even almost totally absent.
The key reason for the prevailing lack of brand segmentation is that products which
compete directly (i.e. by definition, brands) generally do not differ much overall in taste,
technical formulation, performance, or any functional feature of importance, including
often even their appearance (subject to the legal limitations on “passing off”).
Competitive brands deliberately aim to be “similar” so as to be able to compete: they
copy each others’ sales-effective attributes and SKUs, rather than seeking to differentiate
themselves on them (e.g. Young 1963, Ehrenberg 1974, Sampson 1993, Ehrenberg,
Barnard and Scriven 1997, Perris 1999, Ehrenberg et al 2000). And most consumers are
highly experienced and hence know that brands are brands. Hence when there is little
functionally to distinguish one brand from another as is so often the case, any reputable
brand “will do for the consumer” (Heath 1999). (The individual consumer does however
tend over time to identify with the brands he or she uses.)
The implications for brand positioning, targeting, and media planning are in our view
simple and positive. Instead of being restricted to a small segment (and even perhaps
enjoying the proverbial monopoly of a tiny niche), marketers can operate in a large,
unsegmented mass market, or at least in a large sub-market like luxury cars or dry cat
food. However, not being limited to a small niche means also that one’s brand has more
direct competitors: there is therefore more scope, and more need, for plain marketing
(e.g. promotion, selling, logistics, quality control, advertising, and brand maintenance
If your market is limited to cats, it is of course as well to know that. But there is probably
no need officiously to strive to restrict your market if you cannot see it to be so
segmented with your naked eye from well-presented data (without CHAID or conjoint).
Most often one can hardly avoid stumbling across the fact that it is cat owners who
mostly buy cat food. Even a small Usage and Attitude survey would show how far the
new product Nutrigrain is or is not eaten at breakfast time yet only when on the move
(“As Advertised”).
Another limitation of segmentation is that even when it does occur, segmentation may not
be of great sales importance. While the TGI data show that 6% more males shop at W.H.
Smith than do so at other retail chains (Smiths being a newsagent), Smiths still has about
as many females shopping there as do at other chains. Smiths should not, we think,
reposition itself towards males.
A more startling example to some occurs in the Luxury sector of the car market, where
BMW gains more sales each year from the dissimilar Renaults in France or Fords in
Britain than from the “closely clustered” Mercedes. That is simply because Renault and
Ford, as local market leaders, are so much bigger than Mercedes (Ehrenberg and Bound
It is often suggested that advertising can help to create segmentation, by differentiating
your brand from functionally similar competitors and “adding values” (e.g. see Young
1926/1963; Porter 1985; Broadbent 1990; Dickson and Ginter 1987; Perriss 1999;
Ehrenberg et al 2000). But, even the leading brands in a category which typically are
heavily advertised do not in our experience, here or earlier, attract different kinds of
customers from each other. Hence advertising does not work in the way that is intended,
i.e. to add effectively differentiating values (e.g. Ehrenberg et al 2000). In line with that,
when advertisers risk millions of dollars in considering a change of agency, the major
criterion is usually the agencies’ creative style rather than their motivational logic or
strategic targeting (Moran 1990).
The lack of a unique brand-user profile does not mean that brand marketers (or top
management) can give up on marketing, but the opposite. Marketers still need to
publicise and sell their brand, make it memorable, look and sound interesting, refresh
brand associations, sustain its quality and availability, deal well with complaints, and
generally keep the brand salient with purchasers of the category. Advertising, and
marketing more generally, can help a brand to stand out and maintain some sense of
interest or even excitement, at least among those who are actually marketing the brand.
Len Hardy (former chairman of Lever Brothers UK) interviewed:
Q: “Most brands’ sales seem pretty steady: An 8%
brand then is still an 8% brand now,
±1% or so. And there is nothing much one seems
to be able to do about it.
Do you agree?”
A: “Yee...s. I think that’s mostly right.”
Q: “Why then do you relaunch a brand?”
A: (Quick as a flash): “To encourage the marketing
In practice, when a segmented product appeal or positioning concept has been developed,
no matter how intensely it may seem to be preferred by one segment of the market,
someone in the company usually sets about broadening that positioning so that the brand
will appeal to a larger group of people and increase its potential market share (Moran
1990 again). Pretty soon, every brand is trying to appeal to every other brand’s
customers. In practice key players seem to know what works to be a brand mass
marketing. Segmentation and even one-to-one sounds efficient and modern, but old-
fashioned mass-marketing approaches to branding are what have made brands big
(Titford and Clouter 1998). Our analyses here demonstrate why.
Appendix User-friendly Data Presentation
The tables in this paper were set out so as to help both the analyst and the reader see the
patterns in the data, and also any exceptions. In Table 2A patterns are less apparent than
earlier, and no clear exceptions stand out (except perhaps 3619 as the biggest number).
The task of ignoring the decimals when reading down each column is for example
visually quite onerous.
Table 2A Reader-Unfriendly Brand Profiles
(As in Table 2, but not rounded, not ordered by size, and no averages)
Credit CardUsersShareMaleFemale15-1920-2425-3435-4445-5455-6465+Access Lloyds896
7.854. Midlands1155 10.051.848.
13.2Access Natwest1224 10.653.546.51.11.916.124.022.520.813.6B. of Scotland Visa476 Visa 3619 31.550.849.21.24.317.323.021.718.114.3Barclays
MasterCard1065 9.360. Bank Visa472
4.152.547.50.91.515.323.824.219.015.3Midland Visa471 4.155.544.51.36.418.419.520.618.015.9TSB
MasterCard463 4.056.343. Trust card1654
In contrast, the earlier Table 2 made it clear to the naked eye that the individual figures in
each column differed little from their average. Hence we can first note and then
remember that there are few differences in the profiles from brand to brand. This was
easy to see, especially once one had been told what story-line to look for. (The detailed
variations in question were brought out yet more explicitly in Tables 3 and 4, and
summarised in Table 5.)
The process which can make such data more user-friendly is at times referred to as “Data
Reduction” (e.g. Ehrenberg 1982 and 1994). This turning of data into information
involves steps such as
1. Rounding The guideline is to round to just 2 effective digits. This helps one first to
perceive and then even to remember the numbers better. Here we have used deliberate
over-rounding to just one digit, since the more precise quantities in Tables 3 to 5 do
not matter (e.g. whether the profile percentages in Table 2 are 12.8% or 13.4%, or just
13%. Or whether the deviations in Tables 3 and 4 are 1.2 and 4.3 or just the much
simpler 1 and 4).
2. Ordering by size Rearranging the rows of a table by some measure of size (e.g.
here the numbers of users or market-shares), allows one to see visual correlations (i.e.
high in one column tending to go with high in another column — or not — plus
isolated exceptions).
3. Averages These provide both a summary and a visual focus. (One can readily see
that the Male %s in Table 2 are all about the same, i.e. close to 54%, the average. That
is easier than comparing all the individual percentages with each other — i.e. the first
with the second, the first with the third, then the second with the third, the first with
the last, and so on — and remembering the results).
4. Layout This should be used to guide the eye, e.g. using white space, occasional
rules, somewhat varying type-faces, and so on.
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We are indebted to BMRB International for supplying extensive TGI data for this study.
We also acknowledge the earlier interest shown by David Mercer of the Open University.
The paper is part of South Bank’s R&D Initiative, a programme of basic research into marketing which is
supported by over ninety American and European companies.
Data for segmenting specific markets need never be truly confidential – anybody can ask a sample of
consumers some appropriate questions.
In being able to avoid the cost of doing so here – with BMRB supplying us with the TGI profiles – we
have maintained a degree of confidentiality by not citing the dates of data.
A complication in quite a few cases is that a differentiated product-variant may in fact have a singular
brand name (e.g. Kellogg’s All Bran, usually because the item was not large enough to attract a me-too or
two, or because of patents with pharmaceuticals).
... Our findings show insufficient evidence to generalise that older brands tend to have higher number of older consumers compared to newer brands. The lack of differentiation in user profiles across brands within a category is consistent with previous studies (e.g., Hammond et al., 1996, Anesbury et al., 2017, Uncles et al., 2012, Kennedy and Ehrenberg, 2000. ...
This paper compares the buying behaviours of older and younger consumers of older and newer brands in grocery retailing. We analysed 88,000 purchases of 60 brands from six categories. Behavioural loyalty measures for different consumer age cohorts were calculated and compared relative to each brand's launch date. Results showed older consumers do not buy older brands more often than newer brands. Older consumers also do not principally buy older brands. Therefore, brands of all ages compete for consumers of all ages. Findings indicate that for newer brands, older consumers should not be ignored as a market for growing the brand. For older brands, despite the default advantage of long-term exposure of older consumers, such advantage will fade if these brands fail to maintain a competitive presence in the market, as older consumers trial and become loyal to newer brands.
... The profile of buyers of each brand was compared with the average category profile and differences noted as mean absolute deviations (MADs). In a follow-up study, Kennedy and Ehrenberg (2000) examined brands in 42 varied industries in the UK using a similar method, but with a larger number of segmentation variables. Both studies showed an absence of segmentation at the brand level. ...
Marketers are becoming more aware of the growing importance of older consumers. In this paper the brand purchasing behavior of these consumers is investigated. The procedure uses Juster-scale purchase probabilities of brand choice as inputs to a Dirichlet model, enabling brand performance measures to be analyzed across age groups (< 40 years, 40–59 years, and 60–74 years). This approach is recommended for circumstances where it is too costly, too time-consuming, or too difficult to collect consumer panel data. The procedure is used to analyze purchasing in four contrasting types of product category that are representative of repertoire, subscription and mixed markets. Findings show age-based differences in product category purchasing, which impact on within-category brand purchasing (for instance, in terms of opportunities to buy more than one brand). However, patterns of buying between brands within a product category do not reveal marked age-based differences and leading brands tend to be leading for all age groups.
... Much of this literature is focused on techniques, not empirical results (e.g., Wedel and Kamakura, 2000). In contrast, there is now much systematic empirical evidence that the user profiles of substitutable brands seldom differ (Collins, 1971;Kennedy and Ehrenberg, 2000). In practice, the customers of similar brands are very similar, as would tend to follow if nearly each of them uses several brands. ...
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Sales of a brand are determined by measures such as how many customers buy the brand, how often, and how much they also buy other brands. Scanner panel operators routinely report these “brand performance measures” (BPMs) to their clients. In this position paper, we consider how to understand, interpret, and use these measures. The measures are shown to follow well-established patterns. One is that big and small brands differ greatly in how many buyers they have, but usually far less in how loyal these buyers are. The Dirichlet model predicts these patterns. It also provides a broader framework for thinking about all competitive repeat-purchase markets—from soup to gasoline, prescription drugs to aviation fuel, where there are large and small brands, and light and heavy buyers, in contexts as diverse as the United States, United Kingdom, Japan, Germany, and Australasia.Numerous practical uses of the framework are illustrated: auditing the performance of established brands, predicting and evaluating the performance of new brands, checking the nature of unfamiliar markets, of partitioned markets, and of dynamic market situations more generally (where the Dirichlet provides theoretical benchmarks for price promotions, advertising, etc.). In addition, many implications for our understanding of consumers, brands, and the marketing mix logically follow from the Dirichlet framework. In repeat-purchase markets, there is often a lack of segmentation between brands and the typical consumer exhibits polygamous buying behavior (though there might be strong segmentation at the category level). An understanding of these applications and implications leads to consumer insights, imposes constraints on marketing action, and provides norms for evaluating brands and for assessing marketing initiatives.
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This paper describes the patterns discovered in fruit and vegetable buying behaviour in the United States and India. Using claimed buying data obtained from online questionnaires we compare the patterns against those found extensively in consumer goods categories across the world. This study analyses consumer loyalty with Double Jeopardy, consumer sharing with Duplication of Purchase and brand user profiles with Mean Absolute Deviations. The results show the buying behaviour patterns of Double Jeopardy, Duplication of Purchase and that brand user profiles exist within the fruit and vegetable categories. The implications of these findings are (1) that the size of fruit and vegetable brands are largely determined by how many people buy them and not how loyal those consumers are, (2) fruit and vegetable brands share consumers with each other, and, (3) fruit and vegetable brands are not purchased by unique segments of the populations. Therefore in order to increase the number of people buying fruit and vegetable brands marketers should focus on increasing their mental and physical availability (i.e. the same strategies used for consumer good category brands).
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The study examines the extent of brand segmentation, for private labels and national brands, in four UK grocery categories. It uses a straightforward but effective method - 'within- demographic market share'. This approach brings together the concepts of segmentation and brand performance. We find that both private labels and national brands exhibit quite different levels of market share within particular demographic groups. Specifically, cheaper private labels and low priced national brands are comparatively more popular in larger households and in lower social class households. The more expensive private label and national brands have comparatively higher market shares among higher social class households. That said, it is noteworthy that expensive brands still retain some share among low socio-economic households and cheap brands still sell into high socio-economic group households. Generalising these findings to more categories will help retailers and manufacturers to understand brand strengths and weaknesses and to formulate targeting and positioning strategy.
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This paper describes the first stage of a Dirichlet-based approach to analysing and understanding loyalty and switching behaviour for SKUs. An SKU or Stock-Keeping Unit is what is actually stocked, i.e. the combination of a specific pack-size, flavour, colour, pack-design, quality/price level, model specification, etc and also the identifying brand name. The sales of a brand's different SKUs make up the brand's total turnover. Since vast numbers of competitive brands are functionally more or less similar, SKUs are necessarily highly differentiated. This initial study concerns IRI scanner-panel data on Fabric Conditioners in Philadelphia in the US. It has shown that the standard performance measures of different product-variants such as pack-sizes and "forms" broadly follow much the same patterns as do the brands. If this generalises to other products, locations and points in time, the finding is of conceptual as well as practical importance. The emerging knowledge from this study will provide insights into competitive tactics, appropriate size of the SKU repertoire, cannibalisation of other SKUs within the product category, etc.
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Our view of brand advertising is that it mostly serves to publicize the advertised brand. Advertising seldom seems to persuade. Advertising in a competitive market needs to maintain the brand's broad salience-being a brand the consumer buys or considers buying. This turns on brand awareness, but together with memory associations, familiarity, and brand assurance. Publicity can also help to develop such salience. This publicity view of advertising should affect both the briefs that are given to agencies (e.g., that cut-through is more important than having a persuasive selling proposition) and how we then evaluate the results. But since few advertisements seem actively to seek to persuade, how much do the advetisements themselves have to change, rather than just how we think and talk about them?
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Original and provocative findings that date of birth could have an effect on consumption prompted replication of this exploratory work. Date of birth potentially combines the measurement advantages of demographics with the psychological insights of psychographics when interpreted through an astrological framework. Using a different general household survey data set, consumption was again found to vary by date of birth within the alcohol, leisure and cigarette markets. Implications for segmentation and promotion are discussed.
Although cluster analysis is the procedure most frequently used to define data-based market segments, it is not without problems. This research addresses one of its major problems: the selection of the “best” subset of variables on which to cluster. If this selection is not made carefully, “noisy” variables that contain little clustering information can cause misleading results. To help isolate potentially noisy variables prior to clustering, the authors discuss a new algorithm, the Heuristic Identification of Noisy Variables (HINoV). They demonstrate its robustness with artificial data. In addition, the authors illustrate the potential of HINoV to yield more managerially useful market segments (clusters) when applied to two real marketing data sets. Implementation of HINoV is straightforward and will help avoid a major problem in using K-means cluster analysis for market segment definition, as well as for other similar types of research.
Most choice models in marketing implicitly assume that the fundamental unit of analysis is the brand. In reality, however, many more of the decisions made by consumers, manufacturers, and retailers occur at the level of the stock-keeping unit (SKU). The authors address a variety of issues involved in defining and using SKUs in a choice model, as well as the unique benefits that arise from doing so. They discuss how a set of discrete attributes (e.g., brand name, package size, type) can be used to characterize a large set of SKUs in a parsimonious manner. They postulate that consumers do not form preferences for each individual SKU, per se, but instead evaluate the underlying attributes that describe each item. The model is shown to be substantially superior to a more traditional framework that does not emphasize the complete use of SKU attribute information. Their analysis also highlights several other benefits associated with the proposed modeling approach, such as the ability to forecast sales for imitative line extensions that enter the market in a future period. Other implications and extensions also are discussed.
Market segmentation requires definition of consumer groups by variables that discriminate purchasing behavior. A multivariate program is described capable of defining homogeneous groups by a large number of variables to maximize discrimination between purchase group means. The program has specific advantages compared to traditional methods of grouping such as cross-classification, regression, and discriminant analysis. It is applied in segmenting markets by product and brand usage based on demographic and attitudinal variables.
There have been many papers written that examine how tracking measures are interpreted, but few about how well the measures themselves work. This paper examines the traditional approach to tracking to see if the measures used still stand up in today's marketing environment. My conclusion is that these measures, despite the length of time they have been in use, do not necessarily provide an accurate representation of the views held by consumers. In some cases this situation can be rectified simply by changing the way questions are asked, but in others it may be better to introduce new measures. At the end of the paper a case is discussed where traditional measures obscured the success of an advertising campaign, while new measures gave a true picture of its success.
Competitive brands seldom differ in any big way from each other. This is because any innovation with selling power tends to be quickly copied. Nor does the brands' advertising generally give very different images or values to functionally similar brands, despite what tends to be said about this. Why then do similar brands have very different market shares? It is due to the very different numbers of people to whom each brand is "salient," i.e., who feel positive about it. This can be developed, maintained, and/or nudged by advertising.
Despite the pervasive use of the terms "market segmentation" and "product differentiation," there has been and continues to be considerable misunderstanding about their meaning and use. The authors attempt to lessen the confusion by the use of traditional and contemporary economic theory and product preference maps.