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Long Tail or Steep Tail? A Field Investigation into How Online Popularity Information Affects the Distribution of Customer Choices

  • Tucker, Catherine
  • Zhang, Juanjuan

The internet has made it easier for customers to find and buy a wide variety of products. This may lead to a "long tail" effect as more customers buy low-volume products. However, the internet has also made it easier for customers to find out which products are most popular. This could lead to a "steep tail" effect as customers flock towards the most popular products. Using data from a field experiment with a website that lists wedding service vendors, we find empirical evidence that a steep tail exists. The most popular vendors become more popular when customers can easily observe previous customers' click-through behavior. Then, we ask whether this steep tail effect "complements" the long tail, by attracting customers who would otherwise have chosen nothing, or "competes with" the long tail, by shifting customers from less popular vendors to popular ones. We find evidence of a complementary effect, where the steep tail indicates new interest in the most popular vendors from outside, with negligible cannibalization of interest for less popular vendors. The findings suggest that popularity information can serve as a powerful marketing tool that facilitates product category growth. They also explain the prevalence of firm practices to highlight bestsellers.

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Paper provided by Massachusetts Institute of Technology (MIT), Sloan School of Management in its series Working papers with number 39811.

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Date of creation: 07 Dec 2007
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Handle: RePEc:mit:sloanp:39811
Phone: 617-253-2659
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  1. Anderson, Lisa R & Holt, Charles A, 1997. "Information Cascades in the Laboratory," American Economic Review, American Economic Association, vol. 87(5), pages 847-62, December.
  2. Rosen, Sherwin, 1981. "The Economics of Superstars," American Economic Review, American Economic Association, vol. 71(5), pages 845-58, December.
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  4. M. C. Jones, 2006. "Book Review," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(10), pages 1149-1151.
  5. Jerry A. Hausman & Bronwyn H. Hall & Zvi Griliches, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," NBER Technical Working Papers 0017, National Bureau of Economic Research, Inc.
  6. Erik Brynjolfsson & Yu (Jeffrey) Hu & Michael D. Smith, 2003. "Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers," Management Science, INFORMS, vol. 49(11), pages 1580-1596, November.
  7. Banerjee, Abhijit V, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, MIT Press, vol. 107(3), pages 797-817, August.
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