Abstract: A method and system target an audience segment in real-time based on social activities of users. A set of keywords associated with a brand and a set of keywords associated with a first plurality of users is compared. When there is match between the two sets of keywords, a seed audience segment that includes a subset of the first plurality of users is generated. User profiles of users from the seed audience segment based on features are generated. Subsequently, model files of features, feature threshold scores, and an overall feature threshold score corresponding to the user profiles are calculated and provided to a real-time bidding (RTB) server. When the RTB server receives a bid request for a cookie, the RTB server computes a model score corresponding to the cookie based on the model files and accepts the bid request if the model score is above the overall feature threshold score.

Abstract: An advertisement campaign optimization system, method, and a computer product for determining real time bidding data corresponding to one or more advertisement campaigns targeting one or more users are disclosed. In an embodiment, the method comprises receiving a bid request for the one or more advertisement campaigns. User interest data corresponding to the one or more users is determined. One or more bidding rules based on a first data set and a second data set are also determined. The first data set comprises data corresponding to the one or more users and the second data set comprises data corresponding to the one or more advertisement campaigns. The method determines real time bidding data based on the user interest data and the one or more bidding rules.