Network-Based Marketing: Identifying Likely Adopters Via Consumer Networks

By Shawndra Hill, Foster J. Provost, C. Volinsky. 2006

618 citations recorded by the source corpus.

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.

Subfields: Marketing, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/72074b2c0496a818cd07fad832c47d37fa55d76b

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Citations in the corpus

A Survey of Statistical Network Models (2009, background): “Other applications include locating the crucial missing link in a business or a terrorist network, or calculating the probability that a customer will purchase a new product, given the pattern of purchases of his friends [142].”
Classification in Networked Data: A Toolkit and a Univariate Case Study (2007, positive): “If a ﬁrm can know actual social-network links between consumers, for example through communications records, statistical, network-based marketing techniques can perform signiﬁcantly better than traditional targeted marketing based on demographics and prior purchase data (Hill et al., 2006a).”
Modeling Information Diffusion in Implicit Networks (2010, background): “This setting naturally applies to viral marketing [6], [18], where we observe peopl purchasing products or adopting particular behavior witho u explicitly knowing who was the influencer.”
Dynamic Effects of Social Influence and Direct Marketing on the Adoption of High-Technology Products (2014, methodology): “Prior research has used mobile call graphs to analyze network effects to model retention (Nitzan and Libai 2011) and adoption (Hill, Provost, and Volinsky 2006).”
Who cares about your Facebook friends? Credit scoring for microfinance (2015, background): “[20], which uses the social relationships observed in calling behavior to predict product/service adoption.”
Creating Social Contagion through Viral Product Design: A Randomized Trial of Peer Influence in Networks (2011, neutral): “…1623–1639 issn 0025-1909 eissn 1526-5501 11 5709 1623 http://dx.doi.org/10.1287/mnsc.1110.1421 © 2011 INFORMS Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks Sinan Aral, Dylan Walker Information, Operations, and Management Sciences, Stern…”
On the Convexity of Latent Social Network Inference (2010, background): “And, in a viral marketing setting, we observe people purchasing products or adopting particul ar behaviors without explicitly knowing who was the influencer that caused the adoption or the purchas e [11].”
The role of social networks in information diffusion (2012, background): “On one hand, pairs of individuals who interact more often have greater opportunity to inﬂuence one another and have more aligned interests, increasing the chances of contagion [11, 27].”
A bias/variance decomposition for models using collective inference (2008, background): “Relational autocorrelation is a statistical dependency between the values of the same variable (e.g., the class label) on related entities and is a nearly ubiquitous characteristic of relational datasets (Jensen and Neville 2002; Taskar et al. 2002; Bernstein et al. 2003; Hill et al. 2006).”
Opinion Leadership and Social Contagion in New Product Diffusion (2011, methodology): “…earlier studies have assessed the existence of contagion in new product adoption after controlling for marketing effort using actual network data (Hill et al. 2006, Van den Bulte and Lilien 2001) or trying to proxy for network ties by geographical propinquity (Bell and Song 2007, Grinblatt et…”
Do Your Online Friends Make You Pay? A Randomized Field Experiment on Peer Influence in Online Social Networks (2014, background): “…playing roles that range from inspiring political action to driving viral and word-of-mouth spread of products and services (Aral and Walker 2011, Hill et al. 2006, Iyengar et al. 2011, Manchanda et al. 2008, Mayzlin 2006), and as such, represent a vast reservoir of social and economic inﬂuence.”
An Empirical Analysis of User Content Generation and Usage Behavior on the Mobile Internet (2011, background): “There is some evidence of this influence in the business world, such as adoption of new services and products (Hill et al. 2006, Tucker 2008, Aral et al. 2009, Nair et al. 2010, Nam et al. 2010, Iyengar et al. 2011, Oestricher-Singer and Sundararajan 2010), switching from an existing service…”
Audience selection for on-line brand advertising: privacy-friendly social network targeting (2009, background): “An opportunity is that advertisers tend to believe that brand aﬃnity is likely to cluster in social networks [21], similar to product aﬃnity [16].”