Developing a crowdfunding optimization model to bridge the financing gap for small business enterprises through data-driven strategies
1 University of Sunderland, UK.
2 Independent Researcher, Lagos, Nigeria.
3 Independent Researcher, Port Harcourt, Nigeria.
Review
International Journal of Scholarly Research and Reviews, 2024, 05(02), 052–069.
Article DOI: 10.56781/ijsrr.2024.5.2.0048
Publication history:
Received on 18 September 2024; revised on 2274 October 2024; accepted on 29 October 2024
Abstract:
This study explores the development of a crowdfunding optimization model aimed at addressing the financing gap faced by small business enterprises (SBEs). Crowdfunding has emerged as a significant alternative funding source for SBEs, especially those unable to access traditional financing. However, the success of crowdfunding campaigns remains inconsistent, highlighting the need for optimized, data-driven strategies to improve funding outcomes. This research proposes a model that leverages data analytics to identify key factors influencing the success of crowdfunding campaigns and provides recommendations for campaign design, target audience engagement, and funding goal setting. The model incorporates historical crowdfunding data, social media engagement metrics, and business profiles to generate predictive insights into campaign success rates. Machine learning algorithms are employed to identify patterns in successful campaigns, such as the optimal timing of campaign launches, ideal contribution amounts, and audience targeting strategies. Additionally, the model aims to support SBEs in crafting more compelling narratives, reward structures, and outreach plans to enhance backer confidence and investment likelihood. The primary objective of this model is to bridge the financing gap by equipping SBEs with actionable insights to optimize their crowdfunding efforts, thereby increasing their chances of securing necessary capital. By employing a data-driven approach, this research contributes to both the theoretical understanding of crowdfunding dynamics and practical applications for business owners seeking alternative financing. Key findings from preliminary testing indicate that data-driven optimizations, such as personalized messaging and tiered reward systems, significantly increase backer participation and funding success. This model has the potential to be a transformative tool for SBEs, fostering greater financial inclusivity and sustainability. Future research will explore the integration of real-time data analytics and adaptive learning systems to continuously refine crowdfunding strategies.
Keywords:
Crowdfunding; Small Business Enterprises (SBES); Data-Driven Strategies; Financing Gap; Machine Learning; Predictive Modeling; Campaign Optimization; Alternative Financing; Social Media Analytics; Business Finance.
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0