Emerging dynamics in crowdfunding campaigns

Huaming Rao, Anbang Xu, Xiao Yang, Wai Tat Fu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Crowdfunding platforms are becoming more and more popular for fund-raising of entrepreneurial ventures, but the success rate of crowdfunding campaigns is found to be less than 50%. Recent research has shown that, in addition to the quality and representations of project ideas, dynamics of investment during a crowdfunding campaign also play an important role in determining its success. To further understand the role of investment dynamics, we did an exploratory analysis of the time series of money pledges to campaigns in Kickstarter to investigate the extent to which simple inflows and first-order derivatives can predict the eventual success of campaigns. Using decision tree models, we found that there were discrete stages in money pledges that predicted the success of crowdfunding campaigns. Specifically, we found that, for the majority of projects that had the default campaign duration of one month in Kickstarter, money pledges inflow occurring in the initial 10% and 40-60%, and the first order derivative of inflow at 95-100% of the duration of the campaigns had the strongest impact on the success of campaigns. In addition, merely utilizing the initial 15% money inflows, which could be regarded as "seed money", to build a predictor can correctly predict 84% of the success of campaigns. Implication of current results to crowdfunding campaigns is also discussed.

Original languageEnglish (US)
Title of host publicationSocial Computing, Behavioral-Cultural Modeling, and Prediction - 7th International Conference, SBP 2014, Proceedings
Number of pages8
ISBN (Print)9783319055787
StatePublished - 2014
Event7th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2014 - Washington, DC, United States
Duration: Apr 1 2014Apr 4 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8393 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other7th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2014
Country/TerritoryUnited States
CityWashington, DC


  • Crowdfunding
  • decision tree
  • dynamic
  • predictor

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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