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Daily deals: prediction, social diffusion, and reputational ramifications

Published:08 February 2012Publication History

ABSTRACT

Daily deal sites have become the latest Internet sensation, providing discounted offers to customers for restaurants, ticketed events, services, and other items. We begin by undertaking a study of the economics of daily deals on the web, based on a dataset we compiled by monitoring Groupon and LivingSocial sales in 20 large cities over several months. We use this dataset to characterize deal purchases; glean insights about operational strategies of these firms; and evaluate customers' sensitivity to factors such as price, deal scheduling, and limited inventory. We then marry our daily deals dataset with additional datasets we compiled from Facebook and Yelp users to study the interplay between social networks and daily deal sites. First, by studying user activity on Facebook while a deal is running, we provide evidence that daily deal sites benefit from significant word-of-mouth effects during sales events, consistent with results predicted by cascade models. Second, we consider the effects of daily deals on the longer-term reputation of merchants, based on their Yelp reviews before and after they run a daily deal. Our analysis shows that while the number of reviews increases significantly due to daily deals, average rating scores from reviewers who mention daily deals are 10% lower than scores of their peers on average.

References

  1. K. S. Anand and R. Aron. Group buying on the web: A comparison of price-discovery mechanisms. Manage. Sci., 49(11):1546--1562, November 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. A. Arabshahi. Undressing Groupon: An Analysis of the Groupon Business Model. Available at http://www.ahmadalia.com, Dec. 2010.Google ScholarGoogle Scholar
  3. J. W. Byers, M. Mitzenmacher, M. Potamias, and G. Zervas. A Month in the Life of Groupon. Technical report arXiv:1105.0903, arXiv.org, May 4, 2011.Google ScholarGoogle Scholar
  4. J. W. Byers, M. Mitzenmacher, and G. Zervas. Daily Deals Dataset. http://www.cs.yale.edu/homes/zg/daily-deals-dataset.html.Google ScholarGoogle Scholar
  5. J. W. Byers, M. Mitzenmacher, and G. Zervas. Daily Deals: Prediction, Social Diffusion, and Reputational Ramifications. Technical report arXiv.org:1109.1530, arXiv.org, September 7, 2011.Google ScholarGoogle Scholar
  6. B. J. Calder and B. Sternthal. Television commercial wearout: An information processing view. Journal of Marketing Research, 17(2):pp. 173--186, 1980.Google ScholarGoogle ScholarCross RefCross Ref
  7. H. Chun, H. Kwak, Y.-H. Eom, Y.-Y. Ahn, S. Moon, and H. Jeong. Comparison of online social relations in volume vs interaction: a case study of cyworld. In Proc. of the 8th ACM SIGCOMM Conference on Internet Measurement, pages 57--70, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. C. S. Craig, B. Sternthal, and C. Leavitt. Advertising wearout: An experimental analysis. Journal of Marketing Research, 13(4):pp. 365--372, 1976.Google ScholarGoogle ScholarCross RefCross Ref
  9. U. M. Dholakia. How effective are groupon promotions for businesses? Available at http://www.ruf.rice.edu/ dholakia, Sept. 2010.Google ScholarGoogle Scholar
  10. B. Edelman, S. Jaffe, and S. D. Kominers. To Groupon or Not to Groupon: The Profitability of Deep Discounts. HBS Working Paper 11-063: http://hbswk.hbs.edu/item/6597.html, Aug. 2010.Google ScholarGoogle Scholar
  11. Groupon, Inc. S-1 Filing. Filed with the U.S. Securities and Exchange Commission, June 2, 2011.Google ScholarGoogle Scholar
  12. R. J. Kauffman and B. Wang. New buyers' arrival under dynamic pricing market microstructure: The case of group-buying discounts on the internet. J. Manage. Inf. Syst., 18(2):157--188, October 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. D. Kempe, J. M. Kleinberg, and É. Tardos. Maximizing the spread of influence through a social network. In KDD, pages 137--146, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. H. Kwak, C. Lee, H. Park, and S. Moon. What is Twitter, a social network or a news media? In Proceedings of the 19th International Conference on the World Wide Web, WWW '10, pages 591--600, New York, NY, USA, 2010. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. J. Leskovec, J. Kleinberg, and C. Faloutsos. Graph evolution: Densification and shrinking diameters. ACM Trans. Knowl. Discov. Data, 1(1), March 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. J. Leskovec, A. Singh, and J. Kleinberg. Patterns of influence in a recommendation network. In Advances in Knowledge Discovery and Data Mining, volume 3918 of LNCS, pages 380--389. Springer-Verlag, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. M. Luca. Reviews, Reputation, and Revenues: The Case of Yelp.com. HBS Working Paper 12-016: http://hbswk.hbs.edu/item/6833.html, Sept. 2011.Google ScholarGoogle Scholar
  18. J. Stoppelman. Why Yelp has a Review Filter. http://officialblog.yelp.com/2009/10/why-yelp-has-a-review-filter.html.Google ScholarGoogle Scholar
  19. B. Viswanath, A. Mislove, M. Cha, and K. P. Gummadi. On the evolution of user interaction in Facebook. In Proceedings of the 2nd ACM Workshop on Online Social Networks, pages 37--42, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. C. Wilson, B. Boe, A. Sala, K. P. Puttaswamy, and B. Y. Zhao. User interactions in social networks and their implications. In Proceedings of the 4th ACM European Conference on Computer Systems, EuroSys '09, pages 205--218, New York, NY, USA, 2009. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. M. Ye, C. Wang, C. Aperjis, B. A. Huberman, and T. Sandholm. Collective Attention and the Dynamics of Group Deals. Technical report available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1949452, October 25, 2011.Google ScholarGoogle Scholar

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      cover image ACM Conferences
      WSDM '12: Proceedings of the fifth ACM international conference on Web search and data mining
      February 2012
      792 pages
      ISBN:9781450307475
      DOI:10.1145/2124295

      Copyright © 2012 ACM

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      • Published: 8 February 2012

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