As crowdfunding has emerged as a popular source of funding for new ventures, budding entrepreneurs often struggle to deliver a convincing pitch to attract backers. We adopt an n-gram language model to analyze narratives of over 21,000 film projects from Kickstarter and find that the choice of words is critical to crowdfunding success. Using penalized logistic regression, we identify the relative power of phrases to predict funding outcome, resulting in a dramatic reduction in error rate. Consistent with the language expectancy theory, the linguistic analyses show that successful projects usually include words that reflect the credibility of project creators and meaningful social interactions, whereas failed projects exude negativism or uncertainty. While good word choices vary among movie genres, words of lower cognitive complexity dampen the chances of funding. These findings have broad implications for text analysis and natural language generation for persuasive marketing communications.
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© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
- Language expectancy theory (LET)
- Natural language processing (NLP)
- Promotion pitch
- Text mining