An Open-Source AI-Driven CRM Model: Balancing Performance, Cost, and Accessibility for Small Businesses

Zongze Li

European Journal of AI, Computing & Informatics, 2025, vol. 1, issue 4, 34-42

Abstract: An increasing number of small businesses utilize CRM platforms to optimize customer engagement, improve sales productivity, and facilitate informed decision-making. However, the high cost and complexity of proprietary AI-driven CRM platforms often prevent small and medium-sized enterprises (SMEs) from accessing advanced analytics tools. This study presents an open-source AI-driven CRM model designed to balance performance, cost-efficiency, and accessibility for SMEs. The proposed system integrates modular data management, AI analytics-including predictive modeling, customer segmentation, and natural language processing-and an intuitive user interface, all built on widely available open-source frameworks. A prototype implementation demonstrates that lightweight AI models can deliver accurate insights while maintaining low computational requirements, enabling deployment on standard hardware or affordable cloud services. Evaluation results show that the system achieves reliable performance, reduces total cost of ownership compared to commercial alternatives, and supports easy adoption by non-technical staff. Key challenges, such as data quality, model generalization, and organizational readiness, are discussed, along with potential improvements including automated data cleaning, adaptive AI models, and multilingual support. Overall, this study demonstrates that open-source AI-driven CRM solutions can democratize access to advanced business intelligence, empowering small businesses to compete effectively in a data-driven market while maintaining affordability, transparency, and usability.

Keywords: open-source CRM; artificial intelligence; small business; predictive analytics; customer segmentation; cost-efficiency (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:dba:ejacia:v:1:y:2025:i:4:p:34-42

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