The dynamics of viral marketing

ABSTRACT

We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We analyze how user behavior varies within user communities defined by a recommendation network. Product purchases follow a ‘long tail’ where a significant share of purchases belongs to rarely sold items. We establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a model that successfully identifies communities, product, and pricing categories for which viral marketing seems to be very effective.

REFERENCES

• 1: Chris Anderson, The Long Tail: Why the Future of Business Is Selling Less of More, Hyperion, 2006
• 2: Anderson, R. M. and May, R. M. 2002. Infectious Diseases of Humans: Dynamics and Control. Oxford University Press.
• 3: Anonymous. 2005. Profiting from obscurity: What the long tail means for the economics of e-commerce. Economist.
• 4: Bailey, N. 1975. The Mathematical Theory of Infectious Diseases and its Applications. Griffin, London, UK.
• 5: Bass, F. 1969. A new product growth for model consumer durables. Manage. Sci. 15, 5, 215--227.
• 6: Bowman, D. and Narayandas, D. 2001. Managing customerinitiated contacts with manufacturers: The impact on share of category requirements and word-of-mouth behavior. J. Market. Resear. 38, 3 (Aug.), 281--297.
• 7: Bronson, P. 1998. Hotmale. Wired Mag. 6, 12.
• 8: Brown, J. J. and Reingen, P. H. 1987. Social ties and word-of-mouth referral behavior. J. Consum. Resear. 14, 3, 350--362.
• 9: Erik Brynjolfsson , Yu (Jeffrey) Hu , Michael D. Smith, Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers, Management Science, v.49 n.11, p.1580-1596, November 2003 [doi>10.1287/mnsc.49.11.1580.20580]
• 10: Burke, K. 2003. As consumer attitudes shift, so must marketing strategies.
• 11: Centola, D. and Macy, M. 2005. Complex contagion and the weakness of long ties. ftp://hive.soc.cornell.edu/mwm14/webpage/WLT.pdf.
• 12: Chevalier, J. and Mayzlin, D. 2006. The effect of word-of-mouth on sales: Online book reviews. J. Market. Resear. 43, 3, 345.
• 13: David Maxwell Chickering, Optimal structure identification with greedy search, The Journal of Machine Learning Research, 3, p.507-554, 3/1/2003 [doi>10.1162/153244303321897717]
• 14: Clauset, A., Newman, M. E. J., and Moore, C. 2004. Finding community structure in very large networks. Physical Rev. E 70, 066111.
• 15: DeBruyn, A. and Lilien, G. 2004. A multi-stage model of word-of-mouth through electronic referrals.
• 16: Erdös, P. and Rényi, A. 1960. On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci. 5, 17--61.
• 17: Frenzen, J. and Nakamoto, K. 1993. Structure, cooperation, and the flow of market information. J. Consum. Resear. 20, 3 (Dec.), 360--375.
• 18: Goldenberg, J., Libai, B., and Muller, E. 2001. Talk of the network: A complex systems look at the underlying process of word-of-mouth. Market. Lett. 3, 12, 211--223.
• 19: Gomes, L. 2006. It may be a long time before the long tail is wagging the web. The Wall Street Jounal. July 26 2006.
• 20: Granovetter, M. 1978. Threshold models of collective behavior. Ameri. J. Sociol. 83, 6, 1420--1443.
• 21: Granovetter, M. S. 1973. The strength of weak ties. Ameri. J. Sociol. 78, 1360--1380.
• 22: Daniel Gruhl , R. Guha , David Liben-Nowell , Andrew Tomkins, Information diffusion through blogspace, Proceedings of the 13th international conference on World Wide Web, May 17-20, 2004, New York, NY, USA [doi>10.1145/988672.988739]
• 23: Hill, S., Provost, F., and Volinsky, C. 2006. Network-based marketing: Identifying likely adopters via consumer networks. Statist. Sci. 21, 2, 256--276.
• 24: Holme, P. and Newman, M. E. J. 2006. Nonequilibrium phase transition in the coevolution of networks and opinions. Physical Rev. E 74, 056108.
• 25: Jurvetson, S. 2000. What exactly is viral marketing? Red Herring 78, 110--112.
• 26: David Kempe , Jon Kleinberg , Éva Tardos, Maximizing the spread of influence through a social network, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2003, Washington, D.C. [doi>10.1145/956750.956769]
• 27: Killworth, P. and Bernard, H. 1978. Reverse small world experiment. Social Netw. 1, 159--192.
• 28: Jure Leskovec , Lada A. Adamic , Bernardo A. Huberman, The dynamics of viral marketing, Proceedings of the 7th ACM conference on Electronic commerce, p.228-237, June 11-15, 2006, Ann Arbor, Michigan, USA [doi>10.1145/1134707.1134732]
• 29: Leskovec, J., Singh, A., and Kleinberg, J. 2006. Patterns of influence in a recommendation network. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD).
• 30: Greg Linden , Brent Smith , Jeremy York, Amazon.com Recommendations: Item-to-Item Collaborative Filtering, IEEE Internet Computing, v.7 n.1, p.76-80, January 2003 [doi>10.1109/MIC.2003.1167344]
• 31: Alan L. Montgomery, Applying Quantitative Marketing Techniques to the Internet, Interfaces, v.31 n.2, p.90-108, March 2001 [doi>10.1287/inte.31.2.90.10630]
• 32: Resnick, P. and Zeckhauser, R. 2002. Trust among strangers in internet transactions: Empirical analysis of ebays reputation system. In The Economics of the Internet and E-Commerce. Elsevier Science.
• 33: Matthew Richardson , Pedro Domingos, Mining knowledge-sharing sites for viral marketing, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775057]
• 34: Rogers, E. M. 1995. Diffusion of Innovations, Fourth ed. Free Press, New York, NY.
• 35: Strang, D. and Soule, S. A. 1998. Diffusion in organizations and social movements: From hybrid corn to poison pills. Ann. Rev. Sociol. 24, 265--290.
• 36: Mani R. Subramani , Balaji Rajagopalan, Knowledge-sharing and influence in online social networks via viral marketing, Communications of the ACM, v.46 n.12, December 2003 [doi>10.1145/953460.953514]
• 37: Travers, J. and Milgram, S. 1969. An experimental study of the small world problem. Sociometry 32, 425--443.
• 38: Watts, D. 2002. A simple model of global cascades on random networks. In Proceedings of the National Academy of Science 99, 9 (April), 4766--5771.
• 39: Wu, F. and Huberman, B. A. 2004. Social structure and opinion formation. Available at http://ideas.repec.org/p/wpa/wuwpco/0407002.html.
• 40: Yang, S. and Allenby, G. M. 2003. Modeling interdependent consumer preferences. J. Market. Resear. 40, 3 (Aug.), 282--294.

INDEX TERMS

Primary Classification:
  J. Computer Applications
  J.4 SOCIAL AND BEHAVIORAL SCIENCES
      Subjects: Economics

General Terms:
Economics

Keywords:
Viral marketing, e-commerce, long tail, network analysis, recommender systems, word-of-mouth