Predicting the effect of persuasion campaigns is difficult, as belief changes may cascade through a network. In recent years, political campaigns have adopted micro-targeting strategies that segment voters into fine-grained clusters and target those cluster more specifically. At present, there is little evidence that explores the efficiency of this method. Through an Agent-Based Model, the current paper provides a novel method for exploring predicted effects of strategic persuasion campaigns. The voters in the model are rational and revise their beliefs in the propositions expounded by the politicians in accordance with Bayesian belief updating through a source credibility model. The model provides a proof of concept and shows strategic advantages of micro-targeted campaigning. Despite having only little voter data allowing crude segmentation, the micro-targeted campaign consistently beat stochastic campaigns with the same reach. However, given substantially greater reach, a positively perceived stochastic candidate can nullify or beat a strategic persuasion campaigns.