Modeling Viral Marketing Dynamics in Social Networks - Findings From Computational Experiments with Agent-Based Simulation Models

In this article, we provide a computational simulation model which offers alternative explanations on viral marketing campaign effectiveness compared to standard aggregate diffusion models by (1) combining individual preference information and heterogeneity among individuals as well as (2) simulating differing degrees of interconnectedness among individuals within social networks. Our model provides novel evidence when and how particular seeding and targeting strategies in viral marketing campaigns may foster and when they impede rapid diffusion within networks. Implications for practical marketing campaigns as well as modeling of different market characteristics are discussed.