Private labels or store brands are an important source of profits for retailers and a formidable source of competition for national brand manufacturers. Market share of private labels, however, varies dramatically across categories. The authors propose and test a framework to explain this variation in order to understand the determinants of private label success in the U.S. supermarket industry. They find that private labels perform better in large categories offering high margins. Private labels also do better when competing against fewer national manufacturers who spend less on national advertising. Surprisingly, high quality is much more important than lower cost.
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3. Shapiro (1992).
4. Until recently, SAMI was a major supplier of audit data collected by monitoring warehouse withdrawals in most major U.S. markets. SAMI’s product became less valuable as Nielsen and Information Resources, Inc. began offering more timely and accurate point-of-sale electronic data. See:
“The Rebirth of Private Label,” Advertising Supplement, Progressive Grocer, January 1990, pp. 75–82.
5. “National Brands, Private Labels, and How They Compete,” Progressive Grocer, October 1976, pp. 47–56; and
“Brand Power 1982,” Progressive Grocer, October 1982, pp. 49–104.
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7. P.B. Fitzell, Private Labels: Store Brands and Generic Products (Westport, Connecticut: Avi Publishing Company, 1982).
8. The regression results, with t-statistics in parentheses, were where PLMS = private label market share and I = disposable income. The R2 for the regression is .57 and the Durbin-Watson statistic is 1.89, indicating the absence of autocorrelation.
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P. Milgrom and J. Roberts, “Price and Advertising Signals of New Product Quality,” Journal of Political Economy 94 (1986): 615–641; and
B. Wernerfelt, “Umbrella Branding as a Signal of New Product Quality: An Example of Signalling by Posting a Bond,” Rand Journal of Economics 19 (1988): 458–466.
12. C.A. Montgomery and B. Wernerfelt, “Risk Reduction and Umbrella Branding,” Journal of Business 65 (1992): 31–50.
13. For more on PIMS, see:
R.D. Buzzell and B.T. Gale, The PIMS Principles: Linking Strategy to Performance (New York: Free Press, 1987);
M.E. Porter, Competitive Strategy (New York: Free Press, 1980); and
R. Jacobson and D.A. Aaker, “The Strategic Role of Product Quality,” Journal of Marketing 51 (1987): 31–14
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16. Montgomery and Wernerfelt (1992).
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18. R. Sethuraman, “The Effect of Marketplace Factors on Private Label Penetration in Grocery Products” (Cambridge, Massachusetts: Marketing Science Institute, Report No. 92–128, 1992).
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23. Klein and Leffler (1981);
Milgrom and Roberts (1986); and
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26. The correlations were as follows: manufacturers with brands r = .77, manufacturers with UPCs r = .60, and brands with UPCs r = .64.
27. BAR/LNA Multi-Media Service (New York: Leading National Advertisers, 1988).
28. Thomas Food Industry Register (New York: Thomas Publishing Company, 1990).
29. The dependent variable in all the analyses was a logit transformation of each product category i’s private label market share (PLMSi),
A logit transformation is standard when the dependent variable is bounded as in the case of the market share variable (0 percent to 100 percent). The logit transformation also corrects for heteroskedasticity.
30. The food category serves as the base group with an implied coefficient of zero.
31. It is possible that an observed positive relation between margin and private label share is driven by the systematically higher margins offered by private labels. To assess the seriousness of this problem, we obtained private label margins from a large supermarket chain in Chicago for a subset of 138 of the categories and then backed them out of the aggregate category numbers. The estimated coefficient for the margin variable is actually slightly larger (.14 versus .11) when private labels are not included, suggesting that endogeneity is not a serious problem in this case.
32. The correlation between the number of manufacturers and the share of the private label is strongly negative (–.61) compared to the same correlations with shares of national brands: the top national brand (–.21), the second national brand (–.03), the third national brand (+.06), and the fourth national brand (+.17). Market share data for the top four brands in each category comes from:
Product Summary Report (New York: Mediamark Research, 1988).
33. The overall fit of this model decreases very little when the product class terms are dropped (R2 drops from .72 to .71). The decrease is not significant using the Chow models comparison test (F(3,166) = 2.29, p>.05).
34. We randomly selected two-thirds of the categories, estimated Model 3, and then used the model to predict the shares of the remaining categories in the hold-out sample. We did this procedure ten times to ensure stable results. The estimates are remarkably consistent. Also the overall fit in the hold-out samples is close to that obtained on the complete data set (.69 versus .71).
35. M.J. McCarthy, “What’s in a Name? Increasingly, Little, Research Suggests,” Wall Street Journal, 12 October 1990, p. B1; and
M.J. McCarthy, “Soft-Drink Giants Sit Up and Take Notice As Sales of Store Brands Show More Fizz,” Wall Street Journal, 6 March 1992, pp. B1–3.
The authors gratefully acknowledge the support of the Graduate School of Business at the University of Chicago; Dominick’s Finer Foods, Northlake, Illinois; and a National Science Foundation grant #SES-8910755 to Stephen J. Hoch.