A New Approach to Customer Segmentation Using Location Data` Customer segmentation is a crucial process of dividing a client base into groups of potential customers that share similar characteristics and needs. Traditionally, customer segmentation faced challenges such as inconsistent or incomplete internal sales data, large investments in third-party data, and small sample sizes from surveys or panels. However, location intelligence can help solve these challenges by enriching existing sales territories with publicly accessible demographic, geographic, and behavioral data. This approach enables industries to effectively identify and optimize their customer segments. For instance, a retail survey found that 60% of retailers have difficulty boosting sales with geographic and socio-economic data. By applying clustering procedures to location data, retailers can create more granular views of their market segments, such as areas shaded yellow and orange indicating price-conscious customers. Similarly, urban developers and city planners use location data to understand commuting routes and behaviors, leading to the development of customer segments for transportation. This type of segmentation is useful for fleet routing and tracking, and can help build accurate predictive models that assist route optimization.