Marketing’s 60/20 Pareto Law


Available for download from the SSRN
Marketing’s 60/20 Pareto Law
Byron Sharp, Jenni Romaniuk, Charles Graham.
Ehrenberg-Bass Institute, draft report 2019.
Brief description
We confirm our 2007 conclusions concerning the Pareto Law. Our conclusions are now supported by
many other data sets, and independent analyses.
It’s wrong to talk about an 80/20 law in marketing. A brand’s heaviest 20% of buyers generally
contribute not much more than half of a brand’s sales, and these same buyers will contribute less in the
following time period. Indeed, even for stable brands half of last year’s heavy buyers will then not even
qualify to be in the top 20%, while the people who were light or non-brand buyers last year will
contribute more to sales this year than they did last year.
The exact sales contribution of the top 20% (a brand’s Pareto share metric) depends on the time period
and some other technical decisions made by the person calculating the metric, and brand size and
some category characteristics. But it’s reasonable to expect that almost half of your brand’s sales will
always come from your very lightest 80% of buyers. It is also apparent that growth comes largely from
these light, very light/non-brand buyers and so it would be foolhardy to ignore them.
In the 12 years since we issued our report on the Pareto Law (Sharp and Romaniuk 2007) new analyses
have been done by our corporate sponsors and recently by other academics. These confirm our key
1 It is law-like and applies across brands and categories.
2 It’s not as severe as 80/20.
3 The chosen analytical time period affects the calculation of Pareto metrics.
4 Next year, your heaviest 20% of customers won’t be so heavy, the light buyers will be heavier,
and some of the non-buyers will buy (this is the law of buyer moderation).
[see page 52, How Brands Grow, 2010]
In this report we very briefly update our findings, and urge commentators to fuss less about the exact
Pareto percentage and focus more on the strategic implications.
New Results
Twelve years ago (Sharp and Romaniuk 2007), we noted that it wasn’t known if marketing’s Pareto law,
really was law-like, let alone the ’fixed in stone’ 80% Pareto share suggested by textbooks and MBA
Electronic copy available at:
classes . Scientific laws are patterns that are fairly consistent/reliable over a wide set of known
conditions. This means we know roughly what to expect, in a given situation. Should it really be
expected that your top 20% of customers will contribute the vast bulk of your sales? Part of the
reputation of the Ehrenberg-Bass Institute is built on asking questions about existing beliefs.
Precise expectations of Pareto Share can be obtained from the NBD-Dirichlet model of brand choice
(Goodhardt et al 1984) which fits with a number of well-established empirical laws (see Sharp 2010 for a
list). It is still useful to know if there is any value in referring to a Pareto law.
Just intuitively it’s hard to know which would be more surprising, that rival brands and brands in
different categories would have rather similar Pareto shares (i.e that was an empirical law) or that it
would be around 80/20?
Our research report concluded that there was a law-like pattern: that it could generally be expected
that, in a year, a consumer brand’s heaviest (most frequent) 20% of buying consumers contributed
around half of a the brand’s total sales that year. Specifically we reported the brand average across
categories was 59% – from a high of 68% for Dog Food to a low of 44% for hair conditioner .
Since then a number of sponsors of the Ehrenberg-Bass Institute have analysed their own brands,
confirming our findings and showing the law holds in more categories and countries. There have also
been academic articles published, which are described in a table at the end of this article.
Together these studies show that it’s reasonable to talk of a Pareto Law but wrong to refer to it
as 80/20. A few authors have tried to claim that the Pareto Share is not far from 80% but they have
had to use very long windows (5-6 years) of analysis to make this point, which is atypical in brand
management - rarely would any brand metric be calculated using such a long timeframe .
Differences in analytical approaches, as well as sample completeness , do affect the calculation of
Pareto shares. But this should not distract from the really important implication – while every brand has
some customers who are more valuable than others, a large proportion of its sales (especially future
sales) come from its very lightest customers.
So it’s vital to understand and reach these very light/non brand buyers, at minimum to maintain their
loyalties and not decline or, better still, to nudge them upwards and grow. That is the major challenge
that research should be tackling, not attempts to try to ‘prove’ a law that never existed.
Here is a short list of Pareto related questions we suggest might be useful to tackle:
Are the anecdotal claims that in (some?) business-to-business categories the Pareto share is
extremely high (e.g. 90/20) true? And if there is an extreme Pareto is this due to the large
differences in business size among customers? Or is it because of two or more populations
within the customer base ?
Does a slightly higher or lower than expected Pareto for a category or a brand have any
important implications?
As Professor Jan Benedict-Steenkamp admits “I grew up with the 80/20 rule and [before reading How Brands Grow]
kind of accepted it for a long time without much thinking. Who could argue with a famous economist?”
One reason that brand average varies between categories is because of the dierent market shares of brands in
dierent categories and markets. In general smaller brands tend to have a slightly higher Pareto Share, though to make
things complicated, this varies with the time window used for the analysis (over very short periods small brands tend to
have lower Pareto Shares than their larger competitors). See Rungie, Laurent and Habel 2002.
The timeframe slightly increases the Pareto share because it allows more very light customers to be counted in the
analysis, and these make the contribution of the heavier customers look greater.
We explain the problem of using loyalty card data here. And
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Heavy category buyers are more likely to buy a new brand launch - how much of this is
because these category buyers have a greater interest in variety seeking/novelty versus simply
a function of probability of being actively buying in the category at the time of launch?
Do heavy brand buyers buy the same brand multiple times because they perceive it addressing
a wide range of Category Entry Points/Category contexts? Or because it is the one that can
address the Category Entry Point they most commonly encounter?
Electronic copy available at:
Appendix - other Pareto studies
Analysis period
Key findings
Brynjolfsson, Hu, and
Simester, 2011
One month window.
Women’s clothing
retailer’s sales from
catalog or online.
Top 20% of products
were responsible for
almost 60% of sales,
ever so slightly lower
pareto share for internet
sales cf catalog.
Romaniuk and Sharp,
On year window.
Grocery categories in
‘developing markets’, eg
India, Malaysia, Turkey,
Kenya, China, Mexico.
For the average brand,
its top 20% of buyers
were responsible for
53% of sales.
Steenkamp, 2017
One year window.
European consumer
panel data, many
categories from dog
food to skin care.
For the average brand,
its top 20% of buyers
were responsible for
50% of sales.
Kim, Singh, Winer, 2017
Six year window.
US consumer panel
data, 22 grocery product
At “umbrella brand”
level, the top 20% of
buyers contributed 73%
of sales. At brand level,
65%. Cigarettes had the
highest Pareto share.
Graham, Sharp, Dawes,
Trinh, 2017
One quarter, one year,
and five year windows.
European consumer
panel data, 22 grocery
In one quarter 20% of a
brand’s buyers typically
contribute only 40% of
sales, rising over a year
to 50%, and to 60% in a
five year window.
dunnHumby, 2017
1 year and 5 year
UK data from one sole
supermarket loyalty
card, seven grocery
Average Pareto shares
of 62% for top-5 brands,
and 73% for smaller
brands. When calculated
over a five year period it
was 66% for larger
brands and 79% for
smaller brands.
McCarthy, Winer, 2019
Two year window.
Sales of publicly listed
(non-CPG) companies,
measured from credit/
debit card panel
At company level, the
top 20% of buyers
contributed 67% on
average, but 58% for
subscription firms (eg
Planet Fitness, New York
Electronic copy available at:
Brynjolfsson, E., Hu, Y. J., & Simester, D. (2011). Goodbye pareto principle, hello long tail: The effect of
search costs on the concentration of product sales. Management Science, 57(8), 1373-1386.
Goodhardt, G. J., Ehrenberg, A., & Chatfield, C. (1984). The Dirichlet: A comprehensive model of buying
behaviour. Journal of the Royal Statistical Society, 147(5), 621-655. doi:10.2307/2981696
Graham, Charles., Sharp, Byron., Trinh, Giang., & Dawes, John. (2017). Ultra-lights – The Unbearable
Lightness of Buying. Ehrenberg-Bass Institute report for sponsors. University of South Australia.
Kim, B. J., Singh, V., & Winer, R. S. (2017). The Pareto rule for frequently purchased packaged goods: an
empirical generalization. Marketing Letters, 28(4), 1-17. doi:
McCarthy, D. M., & Winer, R. S. (2019). The Pareto rule in marketing revisited: is it 80/20 or 70/20?
Marketing Letters, 1-12. doi:10.1007/s11002-019-09490-y
Rungie, C., Laurent, G., & Habel, C. (2002). A new model of the Pareto effect (80:20 rule) at the brand
level. Paper presented at the ANZMAC, Melbourne.
Sharp, Byron (2010). How Brands Grow. Oxford University Press.
Sharp, Byron, & Romaniuk, Jenni. (2007). There is a Pareto Law - but not as you know it. Ehrenberg-Bass
Institute report for sponsors. University of South Australia.
Sharp, Byron, & Romaniuk, Jenni. (2016). How Brands Grow: Part 2. Oxford University Press. (pp. 1-22)
Steenkamp, Jan-Benedict. (2017, 21st August). How to grow your brand? Mass makes might. Retrieved
Electronic copy available at:
Full-text available
In this article, the Hoover index and asymmetry coefficient of the Pareto curve were tested to measure the state of the systems. When applying the new concept to the analysis of party systems in 18 European countries (158 election cases), we found that most of them showed a left-hand asymmetry of the Pareto curve and a concentration of inequality close to the Pareto principle
Marketers who want to protect their brand's share or grow it need to know who to reach and nudge with advertising. This paper uses continuous household panel data for 55 leading, advertised brands in 12 CPG categories to quantify their target market over different time frames and conditions (market type, brand size and dynamism). Results demonstrate that the customer base (brand penetration) must swell dramatically over time to maintain, let alone grow, market share. For stable brands, penetration typically doubles from its level in one quarter to a year, then again from 1 to 5 years as brands continue to attract lighter buyers who underpin long run sales. Over 5 years, over a third of brand buyers are so light that they buy the brand just once, but such buyers are vital to sales and critical to growth. As well as quantifying the 5‐year target audience for brands across these conditions, we test the predictive accuracy of the NBD‐Dirichlet as a benchmark. The implications for advertising and media strategy are detailed. The long‐term lessons for targeting become clear—unless brands “target the market”, they have adopted a counter growth strategy.
Full-text available
Digital loyalty programmes are an increasingly common tool for business-to-business marketers hoping to increase repeat sales through deeper customer engagement. In consumer markets, such programmes do little to influence behavioural loyalty and disproportionately attract the firm's existing heavy buyers. Industrial buying, however, relies on direct sales channels and features negotiation and reciprocity. Loyalty effects may therefore differ in B2B, and although no clear picture yet exists, such knowledge is important as B2C digital loyalty programmes grow in popularity. Here, the authors describe programme membership's evolving characteristics over in a B2B scheme that was launched in the US metal-cutting tools manufacturer customer base. Findings are consistent with the idea that the scheme recruited the heaviest buyers earliest and had an insignificant effect on total revenue. The authors discuss managerial implications, particularly about (1) managing the rollout of similar schemes and (2) refocussing on the programme objectives to maintain sales from the lightest rather than the heaviest buyers.
Full-text available
In a recent paper, Kim, Singh, and Winer (Marketing Letters 491–507, 2017) studied the Pareto rule across 22 different CPG categories. The authors found an average Pareto ratio (PR) of .73, meaning that 73% of sales came from the top 20% of customers. In this paper, we use a unique dataset of 339 publicly traded non-CPG companies to see whether/when the Kim et al. result holds. We have additional data on these companies, including whether they are product or service companies, whether they sell to customers on a subscription or non-subscription basis, financial and industry information, and summaries of customer purchase behavior. We find that the overall average PR is .67 with product companies having a ratio of .67, and service companies .66. We find that non-subscription businesses have a PR of .68, substantially higher than that of subscription businesses at .59. We estimate the correlates of PR by industry and other factors. Preliminary results show much higher PRs for profits than sales.
Full-text available
Many markets have historically been dominated by a small number of best-selling products. The Pareto principle, also known as the 80/20 rule, describes this common pattern of sales concentration. Several papers have provided empirical evidence to explain the Pareto rule, although with limited data. This article provides a comprehensive empirical investigation on the extent to which the Pareto rule holds for mass-produced and distributed brands in the consumer-packaged goods (CPG) industry. We used a rich consumer panel dataset from A.C. Nielsen with 6 years of purchase histories from over 100,000 households. Our analysis utilizes a large number of potential factors such as brand attributes, category attributes, and consumer purchase behavior to explain variation in the Pareto ratio at the brand level across products. Our main conclusion is that the Pareto principle generally holds across a wide variety of CPG categories with the mean Pareto ratio at the brand level across product categories of .73. Several variables related to consumer purchase behavior (e.g., purchase frequency and purchase expenditure) are found to be positively correlated with the Pareto ratio. In addition, niche brands are more likely to have a higher Pareto ratio. Finally, brand/category size, promotion variables, change-of-pace brands, and market competition variables are negatively correlated with the Pareto ratio.
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The Dirichlet is a stochastic model of purchase incidence and brand choice which parsimoniously integrates a wide range of already well-established empirical regularities.
Full-text available
"More than anything else, however, I'm just plain envious. It's a book I wish I had the intelligence to write... Reading Sharp's critique of the cult of differentiation made me smile. And I laughed out loud at his characterisation of supposedly committed consumers as "uncaring cognitive misers"."--Marketing Week "...marketers need to move beyond the psycho-babble and read this book... or be left hopelessly behind."--Joseph Tripodi, The Coca-Cola Company "Until every marketer applies these learnings, there will be a competitive advantage for those who do."--Mitch Barnes,The Nielsen Company "A scientific journey that reveals and explains with great rigour the Laws of Growth."--Bruce McColl, Mars Incorporated "This book puts marketing's myth-makers, of which there are many, in their proper place."--Thomas Bayne, MountainView Learning "A truly thought-provoking book."--Timothy Keiningham, IPSOS Loyalty "The evidence in this book should make any marketer think hard about how they manage their brands."--Kevin Brennan, General Manager, Snacks and Marketing Director, Kellogg UK "This book should be required reading on any marketing course."--Colin McDonald, the 'father' of Single-Source analysis and author of Tracking Advertising & Monitoring Brands "There is competitive advantage here for those who understand and follow this book's lessons."--Jack Wakshlag, Chief Research Officer, Turner Broadcasting Systems, Inc.
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Many markets have historically been dominated by a small number of best-selling products. The Pareto principle, also known as the 80/20 rule, describes this common pattern of sales concentration. However, information technology in general and Internet markets in particular have the potential to substantially increase the collective share of niche products, thereby creating a longer tail in the distribution of sales. This paper investigates the Internet's "long tail" phenomenon. By analyzing data collected from a multichannel retailer, it provides empirical evidence that the Internet channel exhibits a significantly less concentrated sales distribution when compared with traditional channels. Previous explanations for this result have focused on differences in product availability between channels. However, we demonstrate that the result survives even when the Internet and traditional channels share exactly the same product availability and prices. Instead, we find that consumers' usage of Internet search and discovery tools, such as recommendation engines, are associated with an increase the share of niche products. We conclude that the Internet's long tail is not solely due to the increase in product selection but may also partly reflect lower search costs on the Internet. If the relationships we uncover persist, the underlying trends in technology portend an ongoing shift in the distribution of product sales. This paper was accepted by Ramayya Krishnan, information systems.
Ultra-lights -The Unbearable Lightness of Buying. Ehrenberg-Bass Institute report for sponsors
  • Charles Graham
  • Byron Sharp
  • Giang Trinh
  • John Dawes
Graham, Charles., Sharp, Byron., Trinh, Giang., & Dawes, John. (2017). Ultra-lights -The Unbearable Lightness of Buying. Ehrenberg-Bass Institute report for sponsors. University of South Australia.
A new model of the Pareto effect (80:20 rule) at the brand level
  • C Rungie
  • G Laurent
  • C Habel
Rungie, C., Laurent, G., & Habel, C. (2002). A new model of the Pareto effect (80:20 rule) at the brand level. Paper presented at the ANZMAC, Melbourne.
There is a Pareto Law -but not as you know it. Ehrenberg-Bass Institute report for sponsors
  • Byron Sharp
  • Jenni Romaniuk
Sharp, Byron, & Romaniuk, Jenni. (2007). There is a Pareto Law -but not as you know it. Ehrenberg-Bass Institute report for sponsors. University of South Australia.
How to grow your brand? Mass makes might
  • Steenkamp
Steenkamp, Jan-Benedict. (2017, 21st August). How to grow your brand? Mass makes might. Retrieved from