Predicting Highly Rated Crowdfunded Products

Online crowdfunding platforms have given creators new opportunities to obtain funding. Despite the popularity and success of many projects on the platforms, the quality of crowd-funded products in the market (e.g., Amazon) was not statistically and scientifically evaluated yet. To fill the gap, in this paper, we (i) compare crowdfunded products with traditional products in terms of their ratings in the largest e-commerce market, Amazon; (ii) analyze characteristics of the successful products (received ≥4 star) and unsuccessful products (received <; 4 star); and (iii) build machine learning models in three different stages, which predict whether a crowdfunded product will receive high star ratings or not. Our experimental results show that crowdfunded products, on average, received lower rating than traditional products. Our predictive models effectively identify which product will receive high star-ratings from customers on Amazon. The datasets used in this paper will be available at http://web.cs.wpi.edu/~kmlee/data.html.