Ketto and the Science of Giving: A Data-Driven Investigation of Crowdfunding for India

2025-09-15

AI Summary

This study investigates the determinants of donor participation and fundraising success on Ketto, India’s leading medical-focused crowdfunding platform. As the first systematic, data-driven analysis of Ketto, it integrates heterogeneous data—including project metadata, textual descriptions, geospatial information, and donation records—and employs descriptive statistics, spatial analysis, and predictive modeling. Key findings reveal a critical paradox: although chronic-disease-related campaigns constitute the largest demand segment, they exhibit the lowest success rates. Fundraising efforts are disproportionately concentrated in high-population states and Tier-1 cities. Moreover, higher comment frequency, regular campaign updates, and moderate campaign duration extension significantly and positively correlate with funding attainment. The study fills a critical gap in empirical crowdfunding research within the Indian context and provides actionable, evidence-based insights for platform design optimization and targeted public health interventions.

Technology Category

• Data Mining & Knowledge Management: Knowledge Acquisition from the Web
• Humans and AI: Crowd Sourcing and Human Computation
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Web Mining and Content Analysis: Community question answering

Abstract

The main goal of this paper is to investigate an up and coming crowdfunding platform used to raise funds for social causes in India called Ketto. Despite the growing usage of this platform, there is insufficient understanding in terms of why users choose this platform when there are other popular platforms such as GoFundMe. Using a dataset comprising of 119,493 Ketto campaigns, our research conducts an in-depth investigation into different aspects of how the campaigns on Ketto work with a specific focus on medical campaigns, which make up the largest percentage of social causes in the dataset. We also perform predictive modeling to identify the factors that contribute to the success of campaigns on this platform. We use several features such as the campaign metadata, description, geolocation, donor behaviors, and campaign-related features to learn about the platform and its components. Our results suggest that majority of the campaigns for medical causes seek funds to address chronic health conditions, yet medical campaigns have the least success rate. Most of the campaigns originate from the most populous states and major metropolitan cities in India. Our analysis also indicates that factors such as online engagement on the platform in terms of the number of comments, duration of the campaign, and frequent updates on a campaign positively influence the funds being raised. Overall, this preliminary work sheds light on the importance of investigating various dynamics around crowdfunding for India-focused community-driven needs.

Problem

• Investigating crowdfunding platform Ketto for social causes in India
• Identifying factors influencing campaign success through predictive modeling
• Analyzing medical campaigns' low success rates despite high demand

Innovation

• Predictive modeling with campaign metadata
• Analyzing donor behaviors and engagement
• Geolocation and feature-based success factors