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.
@cite_28: Network-based marketing refers to a collection of marketing techniques that take advantage of links between consumers to increase sales. We concentrate on the consumer networks formed using direct interactions (e.g., communications) between consumers. We survey the diverse literature on such marketing with an emphasis on the statistical methods used and the data to which these methods have been applied. We also provide a discussion of challenges and opportunities for this burgeoning research topic. Our survey highlights a gap in the literature. Because of inadequate data, prior studies have not been able to provide direct, statistical support for the hypothesis that network linkage can directly affect product service adoption. Using a new data set that represents the adoption of a new telecommunications service, we show very strong support for the hypothesis. Specifically, we show three main results: (1) Network neighbors''--those consumers linked to a prior customer--adopt the service at a rate 3--5 times greater than baseline groups selected by the best practices of the firm's marketing team. In addition, analyzing the network allows the firm to acquire new customers who otherwise would have fallen through the cracks, because they would not have been identified based on traditional attributes. (2) Statistical models, built with a very large amount of geographic, demographic and prior purchase data, are significantly and substantially improved by including network information. (3) More detailed network information allows the ranking of the network neighbors so as to permit the selection of small sets of individuals with very high probabilities of adoption.
@cite_9: Abstract A consumer’s preference for an offering can be influenced by the preferences of others in many ways, ranging from social identification and inclusion to the benefits of network externalities. In this article, the authors introduce a Bayesian spatial autoregressive discrete-choice model to study the preference interdependence among individual consumers. The autoregressive specification can reflect patterns of heterogeneity in which influence propagates within and across networks. These patterns cannot be modeled with standard random-effect specifications and can be difficult to capture with covariates in a linear model. The authors illustrate their model of interdependent preferences with data on automobile purchases and show that preferences for Japanese-made cars are related to geographically and demographically defined networks.
Social networks can be composed by using various information, i.e. geographic similarity, age, similar interests and so on. Yang and Allenby @cite_9 showed that the geographically defined network of consumers is more useful than the demographic network for explaining consumer behavior in purchasing Japanese cars. A recent study by @cite_28 found that adding network information, specifically whether a potential customer was already talking to" an existing customer, was predictive of the chances of adoption of a new phone service option. For the customers linked to a prior customer the adoption rate of was 3--5 times greater than the baseline.