The highly competitive telecommunications' market demands companies establish a good customer relation process. There are several traditional approaches to address business' issues according to behavioral segmentation and predictive models. All these traditional approaches such as clustering and predictive models are well founded unsupervised and supervised learning approaches, respectively. Although these models can be quite effective in terms of predictions, or even in terms of customer understanding, they just present an isolated knowledge about the customers. They do not consider relationships or the influence among customers, especially over the time, in an evolution perspective.Social network analysis can be used to enhance the knowledge related to the customers' influence in an internal community. This new proposition in order to evaluate the customers can clarify aspects about the virtual communities inside the telecommunications networks, allowing companies to deploy a more effective action plan to better diffuse their products and services. Evolution's perspective should consider the network behavior in distinct timeframes, allowing a more complex cross analysis and also the opportunity to establish a correlation between the strength of the relationship and the influence over time for some particular business events such as bundle diffusion. Bearing in mind that the influence happens first when the relationship is stronger can allow companies to prepare a marketing campaign to optimize the bundle diffusion targeting the right subset of customers.The focus of this book is on the telecommunications industry and how it can use social network analysis. The book will focus on three primary areas: (1) overview of social networks, (2) shows the tools that can be used to address social network problems, and (3) a case study to show how to effectively use tools (i.e., software) to solve social network business issues.