The use of language is ubiquitous in any marketing communication such as emails or product descriptions and the problem - how to formulate such messages so that they are effective - is a decision problem most communicators face. In this article, we address this problem by answering the unresolved question: What is the role of language in predicting how persuasive a message will be? We propose a natural language processing approach to measure language complexity and predict message persuasiveness. We use a dataset including 134 debates with 129,480 sentences on different topics, and a follow-up experiment to investigate the roles of content complexity and syntactic complexity of the message in predicting the persuasiveness of a message. The main finding is that syntactic complexity has a significant and strong impact on persuasion: When the syntax is more complex, persuasiveness of the message is diminished. The model shows that using both content complexity and syntactic complexity as predictors, improves the accuracy of predicting the persuasiveness of a message by about 32%, compared to a baseline model where use of language is not factored in. The systematic classification of syntactic complexity has only recently become possible through advances in natural language processing. We provide practitioners with a tool to assess language in which we account for both diction and syntax simultaneously, to develop and predict the persuasiveness of their messages.