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Unveiling Crowdfunding Futures: Analyzing Campaign Outcomes through Distributed Models and Big Data Perspectives

Giuseppe Pipitò, Emanuele Macca

TL;DR

This study aims to investigate and analyse the targets of reward-based crowdfunding campaigns through machine learning techniques, employing distributed models and structures, thereby empowering companies to refine their strategies and enhance the efficacy of their funding endeavors.

Abstract

Crowdfunding has emerged as a widespread strategy for startups seeking financing, particularly through reward-based methods. However, understanding its economic impact at both micro and macro levels requires thorough analysis, often involving advanced studies on past campaigns to extract insights that aiding companies in optimizing their crowdfunding project types and launch methodologies. Such analyses are often beyond the scope of basic data analysis techniques and frequently demand advanced machine learning tools, such as distributed computing, due to the large volume of data involved. This study aims to investigate and analyse the targets of reward-based crowdfunding campaigns through machine learning techniques, employing distributed models and structures. By harnessing the power of distributed computing, it unravels intricate patterns and trends within crowdfunding data, thereby empowering companies to refine their strategies and enhance the efficacy of their funding endeavors. Through this multifaceted approach, a deeper understanding of the economic dynamics underlying crowdfunding ecosystems can be attained, fostering informed decision-making and sustainable growth within the startup landscape.

Unveiling Crowdfunding Futures: Analyzing Campaign Outcomes through Distributed Models and Big Data Perspectives

TL;DR

This study aims to investigate and analyse the targets of reward-based crowdfunding campaigns through machine learning techniques, employing distributed models and structures, thereby empowering companies to refine their strategies and enhance the efficacy of their funding endeavors.

Abstract

Crowdfunding has emerged as a widespread strategy for startups seeking financing, particularly through reward-based methods. However, understanding its economic impact at both micro and macro levels requires thorough analysis, often involving advanced studies on past campaigns to extract insights that aiding companies in optimizing their crowdfunding project types and launch methodologies. Such analyses are often beyond the scope of basic data analysis techniques and frequently demand advanced machine learning tools, such as distributed computing, due to the large volume of data involved. This study aims to investigate and analyse the targets of reward-based crowdfunding campaigns through machine learning techniques, employing distributed models and structures. By harnessing the power of distributed computing, it unravels intricate patterns and trends within crowdfunding data, thereby empowering companies to refine their strategies and enhance the efficacy of their funding endeavors. Through this multifaceted approach, a deeper understanding of the economic dynamics underlying crowdfunding ecosystems can be attained, fostering informed decision-making and sustainable growth within the startup landscape.
Paper Structure (18 sections, 5 equations, 2 figures, 5 tables)

This paper contains 18 sections, 5 equations, 2 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Materials
  3. Business Understanding
  4. Data Mining Goals
  5. Data Understanding
  6. Variables
  7. Preliminary analysis
  8. Methods
  9. Preprocessing
  10. Features engineering
  11. Data integration
  12. Data preparation for models
  13. Stratified sampling
  14. Modeling
  15. Numerical results
  16. ...and 3 more sections

Figures (2)

  • Figure 1: Relations between Successful, Failed and Canceled. The points $\varepsilon_1$ and $\varepsilon_2$ represent the percentage of successful campaigns that have collected at least $50\%$ and $20\%$ of the goal, respectively.
  • Figure 2: Totals per year: new open projects (a); numbers of backers expressed in million (b); amount of money required and promised in billion (c); and, percentage of projects failed and successful (d).

Theorems & Definitions (6)

  • Remark 1
  • Remark 2
  • Remark 3
  • Remark 4
  • Remark 5
  • Remark 6