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.
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The overall PR is nearly identical, at .68, if we were to weight each company by its respective market capitalization.
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.
This data is available through Compustat Daily Updates/North America—Daily and Compustat Daily Updates—Fundamentals Quarterly
See, for example, https://en.wikipedia.org/wiki/Subscription_business_model.
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.
Going forward, we will report only the unweighted figures as these compare most closely with the Kim et.al. 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.
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.
The results are substantively the same when we apply a logit transform to the concentration measure. These results are also shown in Table 2.
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.
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.
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.
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.
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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.
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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). https://doi.org/10.1007/s11002-019-09490-y
- Pareto rule
- Empirical generalizations