“PARETO SHARE” IN CUSTOMER KNOWLEDGE BASED BRAND KNOWLEDGE
Jenni Romaniuk and Byron Sharp University of South Australia
Keywords: Brand Knowledge, Pareto, Empirical Generalisation Abstract
A modern conception of customer-based brand knowledge is that it is based upon the positive image associations that exist inside the minds of potential buyers. A brand’s total customer-based knowledge can therefore be thought of as the total sum of these associations across buyers. In this paper we take a new perspective on brand
knowledge by looking at the distribution of these positive associations across brands’ customer bases. The well-known Pareto pattern would suggest that a large portion of a brand’s knowledge resides inside the minds of a small proportion of its buyers, ie, 80% of the positive associations being held by just 20% of buyers. However we found that the Pareto pattern was not as severe as 80/20, but is very consistent. Across 145 brand and 13 categories, 20% of a brand’s customers held about 40% of the image associations.
When non-users of the brand (but users of the category) are included in the analysis the Pareto pattern, as expected, becomes more severe. This is because non-users are particularly light mentioners of a brand. This is particularly so for very small brands that can show a particularly severe Pareto distribution, if for no other reason than that fewer than 20% of category users hold any image associations for the brand. We thus found no evidence of some brands being stronger or weaker than competitors, other than that some brands have more users and are therefore better known. That these patterns are very similar to what is seen in buying behaviour suggests that image associations reflect buying propensities.
A modern conception of customer-based brand knowledge is that it concerns the number of positive image associations’ buyers hold (Keller, 2003; Krishnan, 1996). Brand knowledge is by definition an aggregate level, ie brand level, phenomenon so it can be considered to depend on the total number of positive image associations held by the market. Yet there has been little study of brand knowledge from this
perspective. For example almost nothing is known about the distribution of brand associations across a brand’s customer base. It is this issue that we are concerned with in this paper.
The commonly referred to Pareto distribution would suggest that as much as 80% of a brand’s positive image associations are held by 20% of its customers. These
customers would presumably be the brand’s underpinning – its advocates, its ‘true loyalists’. These customers know, and perhaps love, the brand deeply, and
now discuss how the Pareto distribution might theoretically vary between brands and the implications for brand strength.
Brand knowledge: Number of positive associations
Under the popular customer-based brand equity paradigm (Keller 1993) a key component of equity is the perceptions or knowledge buyers hold about brands in their memory. One measure of knowledge is the number of association held about the brand (Alba and Marmorstein 1987). Brands with more equity (consumer or financial based) have more positive image associations (eg, Aaker 1996; Krishnan 1996). Also the number of attributes associated with the brand has been found to be positively related to brand choice (Alba and Marmorstein 1987), something that might be expected as a positive outcome of higher brand equity.
It is known that at individual level, consumers vary widely in their knowledge of specific brands. However very little is known about how this knowledge is
distributed and the extent to which this varies between brands. Considerable work has been conducted into how brands differ in the nature and structure of their
customer base and so we draw on one such pattern, the Pareto Share (Rungie, Laurent et al. 2002), to see if we can see regularities that allow us to better understand how brands differ in the structure of the knowledge that is held about them.
We would expect differences in the Pareto structure of brand knowledge based on marketing/branding strategy. Some brands presumably successfully focus on building relationships with more valuable, core customers and this might be reflected in an even more “severe” Pareto, where more than 80% of the positive image associations are held by 20% of customers. Modern CRM and loyalty-based marketing initiatives might lead to a more severe Pareto pattern in a brand’s image responses.
Other brands might win their market share through focussing on distribution, or mass marketing, and/or price promotions. Presumably such brands could be widely known but there would be little reason for any buyer to hold many positive image
associations. We might expect a weak Pareto share for such brands where far less than 80% of image responses would come from 20% of its buyers. Perhaps even that all of the brand’s buyers would hold roughly the same few image responses, so 20% of buyers would hold 20% of the brand’s positive image associations. Such a brand could not claim to have a core of committed loyalists.
Pareto analysis is normally conducted on a brand’s customer base. The inclusion of respondents who do not buy the brand naturally increases the severity of the Pareto distribution. For example, the economist Pareto found that 80% of the land in
England was owned by 80% of landowners (Koch 1999). But if he had increased the scope of his population by including non-landowners (or people outside of England) in the analysis then this 80% of England would be owned by far less than 20% of his population of interest.
Non-customers of a brand are less likely to know the brand and less likely to hold positive image associations. So when non-customers are included in a Pareto analysis they make the Pareto more severe, as the existing customers now seem much more committed to the brand in comparison to these (non customer) respondents. This is
probably evident more so for weak brands that fail to reach outside of their customer base. Presumably strong brands have knowledge even outside of their customer base. Consequently while we expect to see that strong brands have a more severe Pareto distribution of image responses within their customers base, as argued above, we would not expect this distribution to become much more severe with the inclusion of non-customers into the data analysis.
In contrast weak knowledge brands should have a less severe Pareto distribution amongst their buyers. And this becomes much more severe when non-buyers are included. Because these non-buyers hold little or no positive image associations about the brand.
Thus we have 2 key propositions:
1. We expect differences between brands in the Pareto distribution of their brand knowledge across their user base. And we argue that this can be interpreted as a sign of brand knowledge strength. With strong brands having a definite core of highly committed buyers, who hold a disproportionate amount of positive image associations. And weak brands having their brand knowledge more equally spread across their buyers.
2. We expect any brand’s Pareto distribution of positive image responses to become more severe as a greater proportion of non-users of the brand is included in the analysis. But that this change would be more pronounced for some brands. These would be weaker brands in that they have failed to build brand knowledge beyond their current customer base.
Both of these propositions turn out to be refuted by our findings. Research approach
In order to measure the number of positive image associations each user had for each brand we used a free choice, brand-attribute association grid. Customers are given attributes and asked which brands, if any, they associate with brands in the
marketplace (Joyce 1963). Lists of brands can be provided, or elicited unprompted. This type of measure (referred to as a ‘pick any’ approach (Levine 1979; Holbrook, Moore et al. 1982)) is less typical in academic marketing research, though it has been a very popular in commercial market research (Brown 1985), particularly in modern brand knowledge monitors.
A sum total of responses across the total attribute battery was calculated for each brand and the distribution of responses across respondents was examined for each brand.
An illustration of this, and the interim calculations, is shown in Table 1. Column 1 is the number of attributes mentioned by each group. Column 2 is the sample size at each group. Column 3 is multiplication of Columns 1 and 2. Column 4 is Column 3 figures as a percent of total responses given for the brand. Column 5 is respondents in each row as a percent of total sample size. Column 6 and 7 are the Cumulative
percents starting from 0 up to 100. The concentration figure reported the share of responses given by the top 20% (or closest to) responders. In Table 1, 18% respondents provide 41% of responses.
Table 1: "Pareto share" calculation for UK Soup Brand
Column 1 Column 2 Column 3 Column 4 Column 5 Column 6 Column 7 No of
Attributes n @ resplevel
No of att X n @
resp level "share" ofresponses respondents"share" of
Cumm "share" of
Cumm "share" of respondents
25 1 25 1 0 0 0
22 1 22 1 0 1 0
21 1 21 1 0 2 0
20 1 20 1 0 2 1
19 1 19 1 0 3 1
9 16 144 5 3 36 15
8 25 200 7 5 41 18
7 36 252 9 7 48 23
6 51 306 11 10 57 30
5 47 235 8 9 68 39
4 71 284 10 14 76 48
3 61 183 6 12 86 62
2 99 198 7 19 92 74
1 23 23 1 4 99 93
0 15 0 0 3 100 97
522 2854 100 100
The Pareto share for all brands was far less severe than the, perhaps apocryphal, 80/20 distribution. Typically 40-50% of the positive image associations were held by 20% of each brand’s users, and the other half were held by 80% of the brand’s users. So we have a Pareto share of about 40/20. And surprisingly this is very consistent between competing brands, regardless of the size of the brands. The Pareto share was similar for brands with more users as well as brands with fewer users. An example of the results for the UK ice cream market is shown in Table 2. In the whole market, the concentration for the smaller brand (Ben and Jerry’s) is much greater than that for other brands.
However this difference disappears when the only brand users are included, with all brands displaying the 40/20 share pattern. This suggests that there is a high degree of similarity in the distribution of brand knowledge within the customer bases of
Table 2: UK Ice cream market
Whole market Brand users only % brand users % responses % people % responses % people
Walls 87 46 20 48 22
Nestle 50 49 22 48 22
Haagen Daas 43 47 15 49 23
Carte D'or 34 52 20 53 24
Ben & Jerry's 11 48 5 54 23
This analysis was conducted over a range of markets, with a summary of results showing in Table 2. Therefore brands can expect that about 25% of their users will account for 50% of the image responses provided. As shown in Table 2, this
concentration is similar across 145 brands in 13 categories. Note – because the scale is discrete it is not possible to get a standardised figure for 20% across all markets. Therefore the nearest figure to 20% is included in the table.
Table 3: Results across 13 markets
Brand Users only Market % responses % people
Condiments 49 34
Soft drinks 49 23
Soups 49 23
Fruit & Veg drinks 49 20
Ice cream 46 23
Energiser drinks 51 26
Mineral Water 50 19
Bus banking 51 25
FF (Wendy's) 49 34
Electronics 49 22
Bus bank (tel) 44 28
Personal banking 49 28
Computers 48 19
Average 49 25
Std Dev 2 5
Discussion and implications
Drawing on a conceptualisation of brand knowledge as the number of positive associations held by the brand, we have documented a new empirical generalisation, namely that 40% of a brand’s knowledge resides in 20% of its buyers. This
generalisation holds across 13 different categories and 145 brands within those categories. It also varied very little between the user bases of brands of differing market shares, something that is quite surprising given the assertions that brands can differ substantially in the knowledge they have.
As expected, the concentration did become more extreme when non-users were included as they are typically light mentioners of the brand. This was particularly noticeable for small brands as they had many more light ‘mentioners’. Therefore differences in brand knowledge across the whole market appear to be more of a reflection of brand size. Once this is taken into account brands differ little.
So while 1/2 of a brand’s knowledge lies with 20% of its buyers, the other half of a brand’s knowledge lies with 80% of its customer base. That this does not vary substantively between big and not-so-big brands suggests that when a brand increases in share, it is linked (either before or after) to a propensity to mention the brand for all users.
This has substantive implications for marketing management. It suggests that expecting that targeting a small group of buyers and expecting that growth in their propensity of mentioning the brand will lead to an increase in the brand’s buying propensity seems misguided. Greater concentration of image responses seems to be only indicative of a very small brand.
In terms of marketing communications, this empirical generalisation tells us that typically there is a large group of buyers/brand users who have a low propensity to think of the brand. This emphasises the need for constant reinforcing to keep that knowledge level up. This reinforces the importance of mass media and the reminder function of advertising (Ehrenberg, Barnard et al. 2002). A brand’s salience, like its sales, depends on a large number of light ‘responders’. These people can easily forget about the brand, which would be evident not only in market research surveys, but also in buying situations.
This research is important but very much a first step in understanding the structure of brand knowledge. There is considerable future research to be conducted to test the conditions under which Pareto share varies, also to explore this generalisation under different conceptualisation of brand knowledge.
These could include the number of attributes provided, the nature of the market, the size of the brand, geographical location, method of measuring knowledge structures and the like.
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