Social media is an interactive vehicle for communication accessed on a daily basis by hundreds of millions of people. Businesses can derive significant benefits from listening to what users are publicly saying on social media, by transforming the massive amounts of textual content into insights specific to enterprise applications. Defining, extracting and representing entities such as people, organizations and products, and their inter-relationships enables the building of comprehensive consumer profiles that can be leveraged in enterprise applications such as customer retention and acquisition, campaign management and lead generation. Building these social media profiles requires a combination of text analytics and entity resolution, while the utilization of such profiles in applications requires statistical models and machine learning. In this talk I will describe the work in progress at IBM Research on how such consumer insights, both at the level of an individual and at the level of appropriate micro-segments, can be used in applications in companies ranging from movie studios to financial services and insurance companies. I will also provide a brief overview of text, entity and statistical analysis tools that can operate in a distributed environment over very large amounts of data.