This study proposes a conceptual framework for data-driven process optimization in micro-enterprises, focusing on the application of funnel analysis and bottleneck identification to enhance operational efficiency and business performance. Micro-enterprises, often characterized by limited resources, informal structures, and minimal data infrastructure, face significant challenges in monitoring and improving process flows across customer acquisition, conversion, and service delivery stages. The framework addresses these challenges by integrating simplified analytics techniques with structured process mapping to enable actionable insights without requiring advanced technological capabilities. Drawing from operations management, business analytics, and small business performance literature, the study conceptualizes the business process as a multi stage funnel, where each stage represents a critical transition point subject to drop offs, delays, or inefficiencies. Funnel analysis is employed to quantify conversion rates, identify leakage points, and evaluate performance variability across stages, while bottleneck identification techniques are used to diagnose constraints that limit throughput and overall system effectiveness. The framework emphasizes the use of low-cost digital tools, basic data collection methods, and visual dashboards to support continuous monitoring and iterative improvements. In addition, it incorporates key performance indicators such as conversion efficiency, cycle time, process yield, and customer retention to guide decision making and track progress. The model also highlights the role of owner manager involvement, process discipline, and data literacy in ensuring successful implementation within micro enterprise contexts. By aligning analytical simplicity with practical applicability, the proposed framework offers a scalable and adaptable approach to process optimization that bridges the gap between theory and practice in resource constrained environments. The expected contribution lies in empowering micro enterprises to leverage data insights for improved decision making, enhanced customer journey management, and increased competitiveness. Furthermore, the framework supports the integration of continuous feedback mechanisms and iterative experimentation to refine processes over time. Future research should focus on empirical validation, sector specific adaptations, and the integration of emerging digital platforms to further enhance scalability and long-term sustainability.