Network-Based Marketing: Identifying Likely Adopters Via Consumer Networks

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

Learning and Inference in Massive Social Networks

Researchers and practitioners increasingly are gaining access to data on explicit social networks. For example, telecommunications and technology firms record data on consumer networks (via phone calls, emails, voice-over-IP, instant messaging), and social-network portal sites such as MySpace, Friendster and Facebook record consumer-generated data on social networks. Inference for fraud detection [5, 3, 8], marketing [9], and other tasks can be improved with learned models that take social networks into account and with collective inference [12], which allows inferences about nodes in the network to affect each other. However, these socialnetwork graphs can be huge, comprising millions to billions of nodes and one or two orders of magnitude more links. This paper studies the application of collective inference to improve prediction over a massive graph. Faced initially with a social network comprising hundreds of millions of nodes and a few billion edges, our goal is: to produce an approximate consumer network that is orders of magnitude smaller, but still facilitates improved performance via collective inference. We introduce a sampling technique designed to reduce the size of the network by many orders of magnitude, but to keep linkages that facilitate improved prediction via collective inference. In short, the sampling scheme operates as follows: (1) choose a set of nodes of interest; (2) then, in analogy to snowball sampling [14], grow local graphs around these nodes, adding their social networks, their neighbors’ social networks, and so on; (3) next, prune these local graphs of edges which are expected to contribute little to the collective inference; (4) finally, connect the local graphs together to form a graph with (hopefully) useful inference connectivity. We apply this sampling method to assess whether collective inference can improve learned targeted-marketing models for a social network of consumers of telecommunication services. Prior work [9] has shown improvement to the learning of targeting models by including social-neighborhood information—in particular, information on existing customers in the immediate social network of a potential target. However, the improvement was restricted to the “network neighbors”, those targets linked to a prior customer thought to be good candidates for the new service. Collective inference techniques may extend the predictive influence of existing customers beyond their immediate neighborhoods. For the present work, our motivating conjecture has been that this influence can improve prediction for consumers who are not strongly connected to existing customers. Our results show that this is indeed the case: collective inference on the approximate network enables significantly improved predictive performance for non-network-neighbor consumers, and for consumers who have few links to existing customers. In the rest of this extended abstract we motivate our approach, describe our sampling method, present results on applying our approach to a large real-world target marketing campaign in the telecommunications industry, and finally discuss our findings.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

The Gift of Gab: Evidence TelE-Commerce Firms Can Profit from Viral Marketing

Viral or buzz marketing takes advantage of communication linkages to propagate positive influence regarding a product or service. TelE-commerce is an ideal domain within which to study viral marketing, because communication linkages can be observed. In this paper, we follow a new telE-commerce service. In particular, we observe how the communication networks of existing customers influence the rate of product diffusion. The main contribution of this paper is evidence that consumers are more likely to purchase a service if they have previously spoken to a person who has the service. In addition, we offer the following three contributions: 1) the clarification that this need not be evidence of viral influence, we suggest different explanations; 2) we also describe the relation of these explanations to theories of purchasing behavior; and 3) we present some evidence to discern from among the explanations.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

Viral Marketing: Identifying Likely Adopters Via Consumer Networks

We investigate the hypothesis: those consumers who have communicated with a customer of a particular service have increased likelihood of adopting the service. We survey the diverse literature on such "viral marketing," providing a categorization of the specific research questions asked, the data analyzed, and the statistical methods used. We highlight a striking gap in the literature: no prior study has had both of the two key types of data necessary to provide direct support for the hypothesis: data on communications between consumers, and data on product adoption. We suggest a type of service for which both types of data are available telecommunications services. Then, for a particular telecommunication service, we show support for the hypothesis. Specifically, we show three main results. 1) there is such a "viral" effect and it is statistically significant, resulting in take rates 3-5 times greater than a baseline group; 2) attributes constructed from the consumer network can improve models for ranking of targeted customers by likelihood of adoption, and 3) observing the network allows the firm to target new customers that would have fallen through the cracks, because they would not have been identified based solely on the traditional set of attributes used for marketing by the firm. We close with a discussion of challenges and opportunities for research in this area. For example, can one determine whether the reason for the viral effect is customer advocacy (e.g., via "word of mouth") versus network-identified homophily?Information Systems Working Papers Serie

Network-Based Marketing: Identifying Likely Adopters via Consumer Networks

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.NYU, Stern School of Business, IOMS, Center for Digital Economy Researc

Building an Effective Representation for Dynamic Networks

A dynamic network is a special type of network composed of connected transactors which have repeated evolving interaction. Data on large dynamic networks such as telecommunications networks and the Internet are pervasive. However, representing dynamic networks in a manner that is conducive to efficient large-scale analysis is a challenge. In this article, we represent dynamic graphs using a data structure introduced in an earlier article. We advocate their representation because it accounts for the evolution of relationships between transactors through time, mitigates noise at the local transactor level, and allows for the removal of stale relationships. Our work improves on their heuristic arguments by formalizing the representation with three tunable parameters. In doing this, we develop a generic framework for evaluating and tuning any dynamic graph. We show that the storage saving approximations involved in the representation do not affect predictive performance, and typically improve it. We motivate our approach using a fraud detection example from the telecommunications industry, and demonstrate that we can outperform published results on the fraud detection task. In addition, we present a preliminary analysis on Web logs and e-mail networks

Viral Marketing: Identifying Likely Adopters Via Consumer Networks

We investigate the hypothesis: those consumers who have communicated with a customer of a particular service have increased likelihood of adopting the service. We survey the diverse literature on such "viral marketing," providing a categorization of the specific research questions asked, the data analyzed, and the statistical methods used. We highlight a striking gap in the literature: no prior study has had both of the two key types of data necessary to provide direct support for the hypothesis: data on communications between consumers, and data on product adoption. We suggest a type of service for which both types of data are available telecommunications services. Then, for a particular telecommunication service, we show support for the hypothesis. Specifically, we show three main results. 1) there is such a "viral" effect and it is statistically significant, resulting in take rates 3-5 times greater than a baseline group; 2) attributes constructed from the consumer network can improve models for ranking of targeted customers by likelihood of adoption, and 3) observing the network allows the firm to target new customers that would have fallen through the cracks, because they would not have been identified based solely on the traditional set of attributes used for marketing by the firm. We close with a discussion of challenges and opportunities for research in this area. For example, can one determine whether the reason for the viral effect is customer advocacy (e.g., via "word of mouth") versus network-identified homophily?Information Systems Working Papers Serie

The Gift of Gab: Evidence TelE-Commerce Firms Can Profit from Viral Marketing

Viral or buzz marketing takes advantage of communication linkages to propagate positive influence regarding a product or service. TelE-commerce is an ideal domain within which to study viral marketing, because communication linkages can be observed. In this paper, we follow a new telE-commerce service. In particular, we observe how the communication networks of existing customers influence the rate of product diffusion. The main contribution of this paper is evidence that consumers are more likely to purchase a service if they have previously spoken to a person who has the service. In addition, we offer the following three contributions: 1) the clarification that this need not be evidence of viral influence, we suggest different explanations; 2) we also describe the relation of these explanations to theories of purchasing behavior; and 3) we present some evidence to discern from among the explanations.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

Learning and Inference in Massive Social Networks

Researchers and practitioners increasingly are gaining access to data on explicit social networks. For example, telecommunications and technology firms record data on consumer networks (via phone calls, emails, voice-over-IP, instant messaging), and social-network portal sites such as MySpace, Friendster and Facebook record consumer-generated data on social networks. Inference for fraud detection [5, 3, 8], marketing [9], and other tasks can be improved with learned models that take social networks into account and with collective inference [12], which allows inferences about nodes in the network to affect each other. However, these socialnetwork graphs can be huge, comprising millions to billions of nodes and one or two orders of magnitude more links. This paper studies the application of collective inference to improve prediction over a massive graph. Faced initially with a social network comprising hundreds of millions of nodes and a few billion edges, our goal is: to produce an approximate consumer network that is orders of magnitude smaller, but still facilitates improved performance via collective inference. We introduce a sampling technique designed to reduce the size of the network by many orders of magnitude, but to keep linkages that facilitate improved prediction via collective inference. In short, the sampling scheme operates as follows: (1) choose a set of nodes of interest; (2) then, in analogy to snowball sampling [14], grow local graphs around these nodes, adding their social networks, their neighbors’ social networks, and so on; (3) next, prune these local graphs of edges which are expected to contribute little to the collective inference; (4) finally, connect the local graphs together to form a graph with (hopefully) useful inference connectivity. We apply this sampling method to assess whether collective inference can improve learned targeted-marketing models for a social network of consumers of telecommunication services. Prior work [9] has shown improvement to the learning of targeting models by including social-neighborhood information—in particular, information on existing customers in the immediate social network of a potential target. However, the improvement was restricted to the “network neighbors”, those targets linked to a prior customer thought to be good candidates for the new service. Collective inference techniques may extend the predictive influence of existing customers beyond their immediate neighborhoods. For the present work, our motivating conjecture has been that this influence can improve prediction for consumers who are not strongly connected to existing customers. Our results show that this is indeed the case: collective inference on the approximate network enables significantly improved predictive performance for non-network-neighbor consumers, and for consumers who have few links to existing customers. In the rest of this extended abstract we motivate our approach, describe our sampling method, present results on applying our approach to a large real-world target marketing campaign in the telecommunications industry, and finally discuss our findings.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc