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

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  • Tucker, Catherine
  • Zhang, Juanjuan

Abstract

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.

Suggested Citation

  • Tucker, Catherine & Zhang, Juanjuan, 2007. "Long Tail or Steep Tail? A Field Investigation into How Online Popularity Information Affects the Distribution of Customer Choices," Working papers 39811, Massachusetts Institute of Technology (MIT), Sloan School of Management.
  • Handle: RePEc:mit:sloanp:39811
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    File URL: http://hdl.handle.net/1721.1/39811
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    Cited by:

    1. Claussen, Jörg & Kretschmer, Tobias & Mayrhofer, Philip, 2010. "Private Regulation by Platform Operators – Implications for Usage Intensity," Discussion Papers in Business Administration 11374, University of Munich, Munich School of Management.
    2. Zhihong Ke & De Liu & Daniel J. Brass, 2020. "Do Online Friends Bring Out the Best in Us? The Effect of Friend Contributions on Online Review Provision," Information Systems Research, INFORMS, vol. 31(4), pages 1322-1336, December.
    3. Heski Bar-Isaac & Guillermo Caruana & Vicente Cunat, 2012. "Search, Design, and Market Structure," American Economic Review, American Economic Association, vol. 102(2), pages 1140-1160, April.
    4. Alexander Cuntz, 2018. "Creators’ Income Situation in the Digital Age," LIS Working papers 755, LIS Cross-National Data Center in Luxembourg.
    5. Tanzila Rahman Lubna, 2022. "Customers’ satisfaction level regarding e-commerce in Bangladesh during COVID-19," International Journal of Science and Business, IJSAB International, vol. 15(1), pages 94-101.
    6. Sanjeev Dewan & Jui Ramaprasad, 2012. "Research Note ---Music Blogging, Online Sampling, and the Long Tail," Information Systems Research, INFORMS, vol. 23(3-part-2), pages 1056-1067, September.
    7. Engström, Per & Forsell, Eskil, 2018. "Demand effects of consumers’ stated and revealed preferences," Journal of Economic Behavior & Organization, Elsevier, vol. 150(C), pages 43-61.
    8. Gal OEstreicher-Singer & Barak Libai, 2011. "Assessing Value in Product Networks," Working Papers 11-29, NET Institute, revised Sep 2011.
    9. Andres Hervas-Drane, 2007. "Word of Mouth and Taste Matching: A Theory of the Long Tail," Working Papers 07-41, NET Institute, revised Jan 2009.
    10. Ajay Agrawal & John Horton & Nicola Lacetera & Elizabeth Lyons, 2015. "Digitization and the Contract Labor Market: A Research Agenda," NBER Chapters, in: Economic Analysis of the Digital Economy, pages 219-250, National Bureau of Economic Research, Inc.
    11. Andreas Hefti & Julia Lareida, 2021. "Competitive attention, Superstars and the Long Tail," ECON - Working Papers 383, Department of Economics - University of Zurich.
    12. Qian Wang & Jijun Yu & Weiwei Deng, 2019. "An adjustable re-ranking approach for improving the individual and aggregate diversities of product recommendations," Electronic Commerce Research, Springer, vol. 19(1), pages 59-79, March.
    13. Saeed Pahlevan Sharif & Paolo Mura, 2019. "Narratives on Facebook: the impact of user-generated content on visiting attitudes, visiting intention and perceptions of destination risk," Information Technology & Tourism, Springer, vol. 21(2), pages 139-163, June.

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    Keywords

    Long Tail; Steep Tail; Customer Learning; Decisions Under Uncertainty; Internet Marketing; Category Management;
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