Geospatial Analytics and Clustering for Potential Revenue Identification in the Agriculture Sector: The CNH Case Study in the NAFTA Market In today’s competitive landscape, medium-to-large enterprises increasingly seek systematic ways to drive revenue by leveraging existing data assets. Combining geographic information with market evidence enables more selective commercial actions grounded in the spatial distribution of firms and the characteristics of their surrounding territory. This thesis focuses on the agricultural machinery domain within the CNH North American commercial perimeter. The analysis is restricted to agricultural transactions and aims to identify potential revenue opportunities by combining geospatial customer aggregation, local product behavior and interpretable recommendation logic.