Persuasive communication relies not only on content but also on its delivery. This column uses machine learning algorithms to examine the persuasiveness of delivery in startup pitches to venture capitalists. Positive (i.e. passionate, warm) pitches increase the probability of funding, but on receiving funding, high-positivity startups tend to underperform. Women are more heavily judged on delivery when evaluating single-gender teams, but they are neglected when pitching alongside men. The results of an experiment suggest that persuasion delivery works mainly through leading investors to form inaccurate beliefs.
Many economic decisions are made after interpersonal persuasive communications, such as pitches to investors, sales presentations, and fundraiser events (McCloskey and Klamer 1995). In the economics literature, these interactions are often formalised by persuasion models (DellaVigna and Gentzkow 2010), which mostly focus on the content in persuasive interactions. The content may be informational, like the net present value (NPV) of a project and the key function of a new product (Stigler 1961). Conversely, the content may be noninformational, like the ‘framing’ (Mullainathan et al. 2008), the appealing peripheral content catering to people’s intuition and attracting attention (Bertrand et al. 2010), or the ‘models’ that lead receivers to interpret data and facts in a certain way (Schwartzstein and Sunderam 2020).
Beyond content, however, it is widely believed that the delivery in persuasive communications matters for the final outcome. Features like facial expression, tone of voice, or diction of speech can be impactful. These persuasion delivery features are dynamic and multi-dimensional and go beyond static traits of persuaders like how they look. As William Carlos Williams wrote, “It is not what you say that matters but the manner in which you say it...”