Predicting crowdfunding success with visuals and speech in video ads and text ads

What is it about?

Predicting Crowdfunding Success with Visuals and Speech in Video Ads and Text Ads Purpose – For the case of many content features, we investigate which content features in video and text ads more contribute to accurately predicting the success of crowdfunding by comparing prediction models. Design/methodology/approach – With 1,368 features extracted from 15,195 Kickstarter campaigns in the U.S, we compare base models such as LR (Logistic Regression) with tree-based homogeneous ensembles such as XGBoost (eXtreme Gradient Boosting) and heterogeneous ensembles such as XGBoost + LR. Findings – XGBoost shows higher prediction accuracy than LR (82% vs. 69%), in contrast to the findings of a previous relevant study. Regarding important content features, humans (e.g., founders) are more important than visual objects (e.g., products). In both spoken and written language, words related to experience (e.g., eat) or perception (e.g., hear) are more important than cognitive (e.g., causation) words. In addition, a focus on the future is more important than a present or past time orientation. Speech aids (see, compare) to complement visual content are also effective and positive tone matters in speech. Theoretical implications – Our research makes theoretical contributions by finding more important visuals (human) and language features (experience, perception, future time). Also, in a multi-modal context, complementary cues (e.g., speech aids) across different modalities help. Furthermore, the non-content parts of speech such as positive ‘tone’ or pace of speech are important. Practical implications – Founders are encouraged to assess and revise the content of their video or text ads as well as their basic campaign features (e.g., goal, duration, reward) before they launch their campaigns. Next, overly complex ensembles may suffer from overfitting problems. In practice, model validation using unseen data is recommended. Originality/value – Rather than reducing the number of content feature dimensions (Kaminski and Hopp, 2020), by enabling advanced prediction models to accommodate many contents features, prediction accuracy rises substantially.

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