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2000

Competitive Brands’ User-Profiles Hardly Differ¹

Prof. Andrew Ehrenberg, South Bank University
Stephen Long, South Bank University
and
Dr. Rachel Kennedy, University of South Australia

Summary

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 VA 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.

Introduction

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), Dibhs 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 (for example, ‘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 (for example, 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, that is, that a named (or coded) brand A in fact appeals to a markedly different population of consumers than does the competitive brand B.²

• 'Segmenting Markets,., the AID Technique' (Assael, 1970).
• 'CHAID... Modelling Segmentation' (Babinec, 1990).
• 'Segmenting... With Conjoint' (Green & Krieger 1891). 
• 'Benefit Segmentation' (Dubow 1992).
• '... A Canonical Correlation Approach' (Bologlu et al 1998).
• 'The Car Challenge: 21 techniques compared' (Colombo et al 1989).
• 'HINoV: Improved Market Segment Definition' (Carmone, et al 1999).
• And soon.

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', that is, 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, for example, 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 (for example, 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 (Ehrenherg, 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:

While the standard sample for the Survey is 25,000 adults, the case 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 hatch 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 in effect everything else.)

As an example of the raw data, Table 1 shows the number of ‘PCI 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 (that is, 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 Appendix.)

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 2.

Table 2 Brand Profiles as Percentages

The 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 ‘PCI 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, that is, 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 differences.)

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.

This form of analysis was repeated for every variable, for over 110,000 individual deviations in the 42 industries.

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 13 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 importance.

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.

Table 5: Summary MADS for 42 TGI Categories

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.

The 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 sub-patterns 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 I the pre-sweetened types (that is, '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 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 by 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 across 40 industries 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, for example, buying of brand X going with buying of brand Y but not of 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, for example, 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?

I3MRB’s ‘PCI 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 ‘PCI are mostly large, averaging at 10000 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.

Discussion

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 here.
• Category segmentation (or sub-category segmentation). This is quite common and relates to differences between users of functionally distinct products (for example, Cameras versus Projectors) or subtypes of products (for example, Professional versus Disposable cameras).
  Category segmentation is probably more usefully thought of simply as 'knowing your market' (for example, 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, that is, on dimensions such as pack-size, flavour, car-models, and so on. 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, that is, 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 51(11 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 (that is, 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’ (for example, Young 1963, Ehrenberg 1974, Sampson 1993, Ehrenherg, Barnard and Scriven 1997, Perris 1999, Ehrenherg 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 (for example, promotion, selling, logistics, quality control, advertising, and brand maintenance generally).

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 Cl-LAID 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 ‘PCI data show that 6% more males shop at WH 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 (Ehrenherg and Bound 2000).

It is often suggested that advertising can help to create segmentation, by differentiating your brand from functionally similar competitors and 'adding values' (for example, 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, that is, to add effectively differentiating values (for example, Ehrenherg 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.

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 people.'

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

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 storyline 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' (for example, 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 (for example, 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 I and 4).

2. Ordering by she – Rearranging the rows of a table by some measure of size (for example, here the nutnhers of users or market-shares), allows one to see visual correlations (that is, 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 percentages in Table 2 are all about the same, that is, close to 54%, the average. That is easier than comparing all the individual percentages with each other – that is, 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, for example, using white space, occasional rules, somewhat varying type-faces, and so on.

1. We are indebted to BMRB International for supplying extensive TOt data for this study. We also acknowledge the earlier interest shown by David Mercer of the Open University. The paper is pan of South Banks R&D Initial ice, a programme of basic research into marketing which is supported by over ninety American and European companies.
2. 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 BMItB supplying us with the TOT profiles — we have maintained a degree of confidentiality by not citing the dates of data.
3. A complication in quite a few cases is that a differentiated product-variant may in fact have a singular brand name (for example, Kelloggs All Brats, usually because the tern was not large enough to attract a me-too or two, or because of patents with pharmaceuticals).

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