The Dynamics of Viral Marketing

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

Why Pass On Fashion Viral Message? The Moderating Role of Consumers’ Fashion Traits, Message Traits and Individual Dynamics Traits in Social Media

Social media is being used as platforms for viral marketing. Although there is some evidence on the usefulness of viral marketing from the marketers’ perspective, little is known about the motivations, attitudes, and behaviors of consumers engaged in this marketing instrument. As the social media platform continues to grow, understanding the characteristics and motivations of key players is invaluable for marketers and communicators who deal with message strategy and media outreach. Fashion companies have noticed this change and want to be present in the social media in different ways. This research represents a comprehensive attempt to examine the factors that impact on consumer usage of social media and their behavioral intentions to forward fashion viral messages by developing an attitudinal model that integrated uses and gratification theory and elaboration likelihood model; proposing that the relationship is moderated by individual dynamic traits, message traits and consumer fashion traits. Empirical data was collected from a convenience sample of 381 college students in two southeastern universities via an online survey. Validity and reliability of research scales were assessed. Hypothesized relationships and moderating effects were tested using two-step structural equation modeling approach. Within the general model the findings show that individuals will forward a fashion viral message if they have stronger favorable utilitarian and value-expressive attitudes towards the message. Motivations did not significantly impact attitudes of the consumer with an exception of the dimension of interpersonal utility that impacts individual’s value expressive attitude. The results of moderating variables indicate that message traits (functional and experiential oriented) impact consumers’ utilitarian and value-expressive attitudes. In relation to consumer fashion traits, there was no significant difference among consumers with high or low fashion leadership and those with high or low fashion involvement. Individual dynamics traits (viral dynamics and structural social capital) showed some moderating effects on the relationships between motivations and attitudes toward a social media marketing message. The results add to existing literature related to viral marketing and validate the claim that viral marketing can be used for marketing purposes resulting in increased business for firms. Theoretical and practical implications were provided based on research findings

A Population Dynamics Approach to Viral Marketing

Souto, P. C., Silva, L. V., Pinto, D. C., & Santos, F. C. (2020). A Population Dynamics Approach to Viral Marketing. In H. Cherifi, S. Gaito, J. F. Mendes, E. Moro, & L. M. Rocha (Eds.), Complex Networks and Their Applications VIII : Proceedings of the 8th International Conference on Complex Networks and Their Applications, COMPLEX NETWORKS 2019 (Vol. 1, pp. 399-411). (Studies in Computational Intelligence; Vol. 881 SCI). Springer. https://doi.org/10.1007/978-3-030-36687-2_33The symbiosis of Social Media and viral campaigns has recently become ubiquitous. In many recent phenomena (e.g., the Cambridge Analytica scandal), rumours in viral marketing programs are present without being even noticed by consumers. Yet, the study of population dynamics and its complex patterns of interaction remains largely elusive. Here, we propose an agent-based Marketing referral model to study the impact on firms’ dissemination and profitability of biased behavior in a population of opportunistic individuals. We show that those agents only interested in collecting rewards without any brand recognition are responsible for most of Marketing campaign success and dissemination, for a large range of different cost structures, network characteristics, and number of invites. This effect is further amplified whenever the difference between the cost of using the service and the reward collected after bringing a new customer is higher.authorsversionpublishe

Learning user-specific latent influence and susceptibility from information cascades

Predicting cascade dynamics has important implications for understanding information propagation and launching viral marketing. Previous works mainly adopt a pair-wise manner, modeling the propagation probability between pairs of users using n^2 independent parameters for n users. Consequently, these models suffer from severe overfitting problem, specially for pairs of users without direct interactions, limiting their prediction accuracy. Here we propose to model the cascade dynamics by learning two low-dimensional user-specific vectors from observed cascades, capturing their influence and susceptibility respectively. This model requires much less parameters and thus could combat overfitting problem. Moreover, this model could naturally model context-dependent factors like cumulative effect in information propagation. Extensive experiments on synthetic dataset and a large-scale microblogging dataset demonstrate that this model outperforms the existing pair-wise models at predicting cascade dynamics, cascade size, and "who will be retweeted".Comment: from The 29th AAAI Conference on Artificial Intelligence (AAAI-2015

Effects of the Network Structure on the Dynamics of Viral Marketing

We present an analysis of how the structure of a social network influences the diffusion of information in a viral marketing context. We performed diffusion simulations on a large number of real world and artificially generated network datasets. We analyze how the characteristics of a network and parameter settings like the selection of initial start nodes influences the diffusion. The results indicate that the network structure has a significant effect on the diffusion. Extreme cases show a difference in the diffusion of over 65%. Our investigation also proves that a viral marketing diffusion may be predicted without the knowledge of the whole network. We further provide useful recommendations for marketers which could be taken into consideration when marketing campaigns are conducted

Theories for influencer identification in complex networks

In social and biological systems, the structural heterogeneity of interaction networks gives rise to the emergence of a small set of influential nodes, or influencers, in a series of dynamical processes. Although much smaller than the entire network, these influencers were observed to be able to shape the collective dynamics of large populations in different contexts. As such, the successful identification of influencers should have profound implications in various real-world spreading dynamics such as viral marketing, epidemic outbreaks and cascading failure. In this chapter, we first summarize the centrality-based approach in finding single influencers in complex networks, and then discuss the more complicated problem of locating multiple influencers from a collective point of view. Progress rooted in collective influence theory, belief-propagation and computer science will be presented. Finally, we present some applications of influencer identification in diverse real-world systems, including online social platforms, scientific publication, brain networks and socioeconomic systems.Comment: 24 pages, 6 figure