Sales territory planning is crucial for maximizing sales performance, distributing workload evenly among representatives, and minimizing travel expenses. Manual assignments, rule-based systems, and simple clustering algorithms alike often fall short in terms of scalability, fairness, and adaptability to market dynamics. In this paper, a comprehensive graph-based framework is introduced that treats customers, depots, and travel paths as nodes and edges of a graph structure. The model incorporates multiple key attributes, including customer value, travel distance, and sales representative capacity, to generate geographically coherent, workload-balanced territories. These territories are also aligned with strategic business objectives. The framework supports dynamic adjustments, leveraging advanced feature engineering and preprocessing techniques to adapt to changing sales data and operational conditions. Experimental evaluations demonstrate that graph-based territory planning outperforms traditional approaches in terms of workload equity, the number of unused trips, and overall customer coverage. Additionally, the model's outputs are transparent and interpretable, enabling sales managers to make more informed and confident decisions. Looking forward, the use of real-time data sources, such as live traffic updates and customer activity logs, combined with machine learning approaches, presents an opportunity to enhance responsiveness and territory optimization further. This graph-based approach can also be applied in other domains beyond sales, such as service delivery, field maintenance, and healthcare outreach. The proposed framework offers a practical, scalable, and adaptable solution for modern sales organizations seeking to remain competitive in a complex and highly data-driven environment.

Sales territory planning, graph theory, workload balancing, optimization, dynamic modeling

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