Heatmap-Based Store Placement System

Heatmap-Based Store Placement System

Author Name : Devraj Kanki, Ravindra Bedade, Prasad Bavke, Narandre Bhosale, Mahesh Thombare, Mrs. Sheetal Chavan

ABSTRACT In the competitive landscape of retail, selecting optimal store locations is a crucial determinant of business success. Traditional site selection methods often rely on subjective judgment or historical data alone, leading to suboptimal decisions. This paper proposes a data-driven approach using heatmap-based spatial analysis to identify and prioritize high-potential store locations. By aggregating and visualizing data such as foot traffic, population density, competitor distribution, and demographic trends, we generate heatmaps that clearly indicate customer hotspots. Utilizing tools like Python, QGIS, and Tableau, we analyze these heatmaps to score and rank potential sites. Our results demonstrate that incorporating heatmap analytics can significantly enhance the accuracy and profitability of store placement decisions. The methodology is validated through a case study, showing marked improvements in customer acquisition and sales performance in the selected areas. This approach provides a replicable, scalable model for strategic expansion in urban retail environments.