Emerging Dynamics in Crowdfunding Campaigns

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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 by applying a decision tree model to train predictors over 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.

Figure 1

Figure 1: Prediction accuracies over time by using the values of money inflows and the selected significant time before cur- rent time

The results based on the  the values of money inflows are shown in Figure 1:

  • As expected, the performance of the predictors steadily improves.
  • With only the first 15% of the money inflows, out predictor can achieve 84% accuracy.
  • The most “active” periods could be around the first 10% as well as between the 40-60%.
Figure 2

Figure 2: Prediction accuracies over time by using the derivative of money inflows and the selected significant time before current time

The results based on the the derivative of money inflows are shown in Figure 2:

  • The performance of the predictors does not increase much until the very last stage.
  • The most important period also does not change until the end, jumping from 5% to 100%.

So according to the above results, we reach the conclusion:

  • The periods around 10% and 40%-60% during a campaign had a stronger impact.
  • “Seed money” (init 15% money inflow) may probably determine the final result of a campaign.
  • Don’t give up and you can still make it at the very end of the campaign.

For more, please see our full paper, Emerging Dynamics in Crowdfunding Campaigns.

Huaming Rao, Nanjing University of Science & Technology and University of Illinois at Urbana-Champaign
Anbang Xu, University of Illinois at Urbana-Champaign
Xiao Yang, Tsinghua University
Wai-Tat Fu, University of Illinois at Urbana-Champaign

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About Huaming Rao

I’m a PH.D. candidate from Nanjing University of Science & Technology (NUST), Nanjing, P.R China, supervised by Prof. Chuancai Liu. major on artificial intelligence & pattern recognition, and now I'm studying in the University of Illinois at Urbana-Champaign (UIUC) as a visiting student working in the CASCAD Lab , supervised by Prof. Wai-Tat Fu. My research interests broadly lie in the fields of machine learning, crowdsourcing and social computing. I'm also an experienced web developer as a fan of Ruby on Rails, AngularJs, etc. BTW, I'm enthusiastic about TDD.