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Crowdfunding platforms have become important sites where people can createprojects to seek funds toward turning their ideas into products, and backsomeone else's projects. As news media have reported successfully fundedprojects (e.g., Pebble Time, Coolest Cooler), more people have joinedcrowdfunding platforms and launched projects. But in spite of rapid growth ofthe number of users and projects, a project success rate at large has beendecreasing because of launching projects without enough preparation andexperience. Little is known about what reactions project creators made (e.g.,giving up or making the failed projects better) when projects failed, and whattypes of successful projects we can find. To solve these problems, in thismanuscript we (i) collect the largest datasets from Kickstarter, consisting ofall project profiles, corresponding user profiles, projects' temporal data andusers' social media information; (ii) analyze characteristics of successfulprojects, behaviors of users and understand dynamics of the crowdfundingplatform; (iii) propose novel statistical approaches to predict whether aproject will be successful and a range of expected pledged money of theproject; (iv) develop predictive models and evaluate performance of the models;(v) analyze what reactions project creators had when project failed, and ifthey did not give up, how they made the failed projects successful; and (vi)cluster successful projects by their evolutional patterns of pledged moneytoward understanding what efforts project creators should make in order to getmore pledged money. Our experimental results show that the predictive modelscan effectively predict project success and a range of expected pledged money.

How to Succeed in Crowdfunding: a Long-Term Study in Kickstarter