In online social networks the goal of users is to maximize their influence through various actions. These can include macro-level actions such as reaching a large audience with their user generated content or micro-level actions such as engaging with a specific target user. These actions have specific quantifiable metrics of success, such as the number of impressions for a piece of content, or being "followed" in the social network by a targeted user. Because users in social networks exhibit behavioral patterns, it should be possible with the right user model and optimization framework to find the optimal policy to maximize a specific influence metric in a social network. We show this to be the case for the two examples mentioned here: maximizing the reach of content and engaging with a target user. For maximizing the reach of content, we show how one can select the timing of content to maximize its reach. For reaching a target user, we propose the follow-back problem, where to goal is to engage with the neighbors of the target user in order to maximize the probability that the target user will "follow-back".