Sales Prioritization Dashboard

This thesis addresses the need for a data-driven approach to sales prioritization for FMCG companies in the HORECA sector by designing and developing a customized Sales Prioritization Dashboard. The dashboard aims to optimize the decision-making process for sales representatives by integrating predictive analytics, demographic insights, and performance metrics to recommend high-priority stores. Leveraging multiple data sources—including CRM data, OpenStreetMap, and Google API—the dashboard provides recommendations, sales forecasts, and performance evaluations to improve store visits’ effectiveness and increase sales potential. This project adopts a structured methodology, from data extraction and cleaning to model selection and testing, to create a responsive and user-centered interface. Initial testing shows promising results, with Ridge Regression and Random Forest models demonstrating strong predictive accuracy. This thesis offers a comprehensive solution for transforming sales decision-making and provides a foundation for further enhancements in data-driven sales tools.