Using Artificial Intelligence to Unlock Crowdfunding Success for Small Businesses

Teng Ye, Jingnan Zheng, Junhui Jin, Jingyi Qiu, Wei Ai and Qiaozhu Mei

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Abstract: While small businesses are increasingly turning to online crowdfunding platforms for essential funding, over 40% of these campaigns may fail to raise any money, especially those from low socio-economic areas. We utilize the latest advancements in AI technology to identify crucial factors that influence the success of crowdfunding campaigns and to improve their fundraising outcomes by strategically optimizing these factors. Our best-performing machine learning model accurately predicts the fundraising outcomes of 81.0% of campaigns, primarily based on their textual descriptions. Interpreting the machine learning model allows us to provide actionable suggestions on improving the textual description before launching a campaign. We demonstrate that by augmenting just three aspects of the narrative using a large language model, a campaign becomes more preferable to 83% human evaluators, and its likelihood of securing financial support increases by 11.9%. Our research uncovers the effective strategies for crafting descriptions for small business fundraising campaigns and opens up a new realm in integrating large language models into crowdfunding methodologies.

Date: 2024-04
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp, nep-ent, nep-pay and nep-sbm
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