Influence Maximization (IM) consists in finding in a network the top-k influencers who will maximize the diffusion of information. However, the exponential growth of online advertisement is due to Real-Time Bidding (RTB) which targets users on webpages. It requires complex ad placement decisions in real-time to face a high-speed stream of users. In order to stay relevant, the IM problem should be updated to answer RTB needs. While traditional IM generates a static set of influ-encers, they do not fit with an RTB environment which requires dynamic influence targeting. This paper proposes RTIM, the first IM algorithm capable of targeting users in a RTB environment. We also analyze influence scores of users in several social networks and provide a thorough experimental process to compare static versus dynamic IM solutions.