The Pareto rule in marketing revisited: is it 80/20 or 70/20?


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.

This is a preview of subscription content, log in to check access.

Fig. 1


  1. 1.

    The overall PR is nearly identical, at .68, if we were to weight each company by its respective market capitalization.

  2. 2.

    Other concentration measures could be used instead, such as the Gini index. However, these measures are all highly correlated with one another, and this concentration measure is most consistent with the Pareto Rule.

  3. 3.

    This data is available through Compustat Daily Updates/North America—Daily and Compustat Daily Updates—Fundamentals Quarterly

  4. 4.

    See, for example,

  5. 5.

    There is no universally accepted rule for how to categorize companies as product versus service firms. As such, while most sectors could be categorized without ambiguity (e.g., auto parts sellers and grocers), some sectors required subjective judgment on the part of the authors. For example, sit down restaurants (e.g., Cheescake Factory Inc.) could technically be considered product firms in that they sell food products to consumers, but could also be considered service firms in that consumers are primarily paying for the experience of being in a restaurant and of being served. Sit down restaurants were categorized as service firms. The categorization of products and services by sector is available upon request from the authors.

  6. 6.

    Going forward, we will report only the unweighted figures as these compare most closely with the Kim figures, which were also unweighted. In addition, the normal understanding of the PR implies lack of weighting by company or product size. Corresponding weighted figures are available from the authors upon request.

  7. 7.

    The seemingly large difference in PR values for subscription and non-subscription physical product companies is statistically insignificant due to the small number of subscription product companies in the sample. The vast majority of subscription firms sell services and not physical products.

  8. 8.

    The results are substantively the same when we apply a logit transform to the concentration measure. These results are also shown in Table 2.

  9. 9.

    All control variables except for market capitalization were available through Compustat with the following variable names: Assets – Total, Revenue – Total, and North American Industry Classification Code. We computed the year-on-year change in trailing 12-month revenues, by calculating the percentage change in revenues in calendar year 2017 relative to calendar year 2016.

  10. 10.

    For example, Abercrombie & Fitch Co. (NYSE: ANF) (the parent company) sells through four brands (the children brands): Abercrombie & Fitch, abercrombie kids, Gilly Hicks, and Hollister. The latter four brands are shown in credit card statements, which Second Measure then maps to the former parent company.

  11. 11.

    It could be that these results are also in part driven by the fact that multi-brand firms are by construction able to cross-sell across a more diverse range of products and services, all else being equal. However, testing this empirically would require data that we do not have access to.

  12. 12.

    The ideal measure of customer concentration is customer lifetime value (“CLV”) and not observed revenue or profit over a finite period of time, but CLV data was not available.


  1. Abbey, J., Ketzenberg, M., Metters, R. (2018) A more profitable approach to product returns. Accessed 10 October 2018

  2. Chatterjee, S., & Lubatkin, M. (1990). Corporate mergers, stockholder diversification, and changes in systematic risk. Strategic Management Journal, 11(4), 255–268.

    Article  Google Scholar 

  3. Fader, P. (2012). Customer centricity: focus on the right customers for strategic advantage. Wharton Digital Press.

  4. Kim, B. J., Singh, V., & Winer, R. S. (2017). The Pareto rule for frequently purchased packaged goods: an empirical generalization. Marketing Letters, 28(December), 491–507.

    Article  Google Scholar 

  5. McCarthy, D. M., Fader, P. S., & Hardie, B. G. S. (2017). Valuing subscription-based businesses using publicly disclosed customer data. Journal of Marketing, 81(January), 17–35.

    Article  Google Scholar 

  6. MNI Targeted Media (2016). The inside scoop on consumer packaged goods, 2017 Factbook. Accessed 10 October 2018

  7. National Retail Federation (2016). Consumer returns in the retail industry. Center/NRF Retail Return Fraud Final_0.pdf. Accessed 10 October 2018

  8. O’Connell, A. (2007). Improve your return on returns. Harvard Business Review, 85(November), 30–34.

    Google Scholar 

  9. Perold, A. F. (2007). Fundamentally flawed indexing. Financial Analysts Journal, 63(November–December), 31–37.

    Article  Google Scholar 

  10. Reagan, C. (2016). A $260 billion ‘Ticking Time Bomb’: the costly business of retail returns. Accessed 10 October 2018

  11. Sharp, B., Romaniuk, J. (2007). There is a Pareto law but not as you know it. Ehrenberg-Bass Institute Report for Corporate Sponsors, No. 42.

  12. Twedt, D. W. (1964). How important to marketing strategy is the heavy users? Journal of Marketing, 28(1), 71.

    Google Scholar 

  13. Wilson Perumal & Co (2014). Whale curves. Accessed 10 October 2018

Download references


The authors acknowledge the support of Second Measure, who provided access to data used in the manuscript. The authors do not have any financial interest, direct or indirect, in the companies studied in the manuscript.

Author information



Corresponding author

Correspondence to Daniel M. McCarthy.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

McCarthy, D.M., Winer, R.S. The Pareto rule in marketing revisited: is it 80/20 or 70/20?. Mark Lett 30, 139–150 (2019).

Download citation


  • Pareto rule
  • 80/20
  • Empirical generalizations