The Dynamics of Viral Marketing ∗
Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA
Lada A. Adamic
School of Information, University of Michigan, Ann Arbor, MI
Bernardo A. Huberman
HP Labs, Palo Alto, CA 94304
April 20, 2007
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.
With consumers showing increasing resistance to traditional forms of advertising such as TV or newspaper ads, marketers have turned to alternate strategies, including viral marketing. Viral marketing exploits existing social networks by encouraging customers to share product information with their friends. Previously, a few in depth studies have shown that social networks aﬀect the adoption of individual innovations and products (for a review see [Rog95] or [SS98]). But until recently it has been diﬃcult to measure how inﬂuential person-to-person recommendations actually are over a wide range of products. Moreover, Subramani and Rajagopalan [SR03] noted that “there needs to be a greater understanding of the contexts in which viral marketing strategy works and the characteristics of products...