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Speaking the same language: the power of words in crowdfunding success and failure


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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  3. Due to the space limit, Table A1-A6 are included in the supplementary online appendices.

  4. The descriptive statistics, correlation table, model results, and phrases signaling funding success and failure with their β coefficients are reported in Table A1-A4.


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Correspondence to Ling Peng.

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Peng, L., Cui, G., Bao, Z. et al. Speaking the same language: the power of words in crowdfunding success and failure. Mark Lett 33, 311–323 (2022).

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  • Crowdfunding
  • Text mining
  • Natural language processing (NLP)
  • Language expectancy theory (LET)
  • Promotion pitch