Enhancing Small Business Growth through AI-Driven Customer Relationship Management Systems

Thesis Abstract

Small businesses are vital drivers of economic development, yet they often face significant challenges in customer retention, engagement, and competitive differentiation due to limited resources and technological adoption. This study investigates the potential of Artificial Intelligence (AI)-driven Customer Relationship Management (CRM) systems to enhance growth trajectories among small enterprises. The primary aim is to evaluate how AI-enabled CRM tools influence customer management practices and contribute to increased sales, customer loyalty, and overall business expansion. Specific objectives include examining the extent of AI integration within small business CRM practices, identifying factors that facilitate or hinder AI adoption, and measuring the impact of AI-driven CRM functionalities on key performance indicators such as customer retention rates and revenue growth. The research adopts a mixed-methods design, combining quantitative survey analysis with qualitative interviews to provide a comprehensive understanding of AI-driven CRM implementation in small businesses. The population comprises small enterprises within the retail and services sectors operating in a metropolitan region, with a total of 500 businesses identified through industry directories. A stratified random sampling technique is employed to select a sample of 150 businesses for survey participation, with purposive sampling of 20 business owners and managers for in-depth interviews. Data collection instruments include standardized questionnaires measuring CRM usage, perceptions of AI utility, and business performance metrics, alongside semi-structured interview guides exploring adoption challenges and success factors. Quantitative data will be analyzed using multiple regression analysis to ascertain the relationship between AI-driven CRM practices and business performance outcomes, ensuring control for potential confounding variables such as firm size, years of operation, and sector. Descriptive statistics will summarize the extent of AI integration, while factor analysis will identify underlying dimensions influencing AI adoption. Thematic analysis will be applied to qualitative interview data to extract nuanced insights into barriers, enablers, and contextual influences on AI CRM utilization. Expected findings suggest a positive correlation between AI-enabled CRM functionalities—such as personalized marketing automation, predictive analytics, and real-time customer feedback systems—and improved customer retention, increased sales, and enhanced competitive positioning. The study anticipates identifying critical success factors, including managerial digital literacy, perceived AI usefulness, and organizational readiness, which mediate the relationship between AI adoption and business growth. Additionally, the research is expected to reveal prevalent barriers such as limited technological expertise, high implementation costs, and resistance to change among small business owners. This study contributes to knowledge by providing empirical evidence on the practical impact of AI-driven CRM systems in the context of small enterprises, filling a notable gap in existing literature that predominantly focuses on larger firms or generic CRM applications. It extends theoretical understanding through the integration of the Technology Acceptance Model (TAM) and Diffusion of Innovation Theory to explain factors influencing AI CRM adoption among small businesses. The study concludes that strategic implementation of AI-based CRM tools can significantly enhance small business growth when complemented by targeted training and organizational change management. Policy recommendations include the need for tailored government support programs to facilitate affordable AI integration and capacity-building initiatives for small business owners. The findings underscore the importance of fostering digital literacy, promoting awareness of AI benefits, and developing scalable AI solutions compatible with small business budgets. Future research avenues are suggested to explore longitudinal impacts of AI-driven CRM systems and their integration with other digital platforms for holistic business transformation.