Voice of Charity: Prospecting the Donation Recurrence Donor Retention in Crowdfunding

Hongke Zhao, Binbin Jin, Qi Liu, Yong Ge, Enhong Chen, Xi Zhang, Tong Xu

Research output: Contribution to journalReview articlepeer-review

15 Scopus citations

Abstract

Online donation-based crowdfunding has brought new life to charity by soliciting small monetary contributions from crowd donors to help others in trouble or with dreams. However, a crucial issue for crowdfunding platforms as well as traditional charities is the problem of high donor attrition, i.e., many donors donate only once or very few times within a rather short lifecycle and then leave. Thus, it is an urgent task to analyze the factors of and then further predict the donors behaviors. Especially, we focus on two types of behavioral events, e.g., donation recurrence (whether one donor will make donations at some time slices in the future) and donor retention (whether she will remain on the crowdfunding platform until a future time). However, this problem has not been well explored due to many domain and technical challenges, such as the heterogeneous influence, the relevance of the two types of events, and the censoring phenomenon of retention records. In this paper, we present a focused study on donation recurrence and donor retention with the help of large-scale behavioral data collected from crowdfunding. Specifically, we propose a Joint Deep Survival model, i.e., JDS, which can integrate heterogeneous features, e.g., donor motives, projects recently donated to, social contacts, to jointly model the donation recurrence and donor retention since these two types of behavioral events are highly relevant. In addition, we model the censoring phenomenon and dependence relations of different behaviors from the survival analysis view by designing multiple innovative constraints and incorporating them into the objective functions. Finally, we conduct extensive analysis and validation experiments with large-scale data collected from Kiva.org. The experimental results clearly demonstrate the effectiveness of our proposed models for analyzing and predicting the donation recurrence and donor retention in crowdfunding.

Original languageEnglish (US)
Article number8669972
Pages (from-to)1652-1665
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume32
Issue number8
DOIs
StatePublished - Aug 1 2020
Externally publishedYes

Keywords

  • Crowdfunding
  • deep learning
  • donor retention
  • ranking constraints
  • survival analysis

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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