Architectural Innovation Meets Data Intelligence: Reimagining Retail Design and Planogram Optimization

Abstract

This paper investigates how the convergence of architecture, data analytics, and artificial intelligence (AI) is reimagining the design
and operational planning of physical retail environments. Amidst a rapidly evolving consumer goods landscape, traditional retail planograms
and static layouts have failed to adapt to real-time behavioral insights and market shifts. Drawing from professional experience in architectural
design and data systems engineering, the authors present a new framework that integrates store design tools (Revit, Rhino/Grasshopper) with
AI-driven retail data platforms (Cohere Commerce) to simulate and implement behavior-responsive merchandising strategies. Through case
studies across the U.S., Europe, China, and Korea—including innovative retailers such as Pangdonglai, GS25, 7-Eleven Japan, and Target—
this article demonstrates how data-enhanced planograms improve store flow, SKU performance, and layout efficiency. We conclude with a
scalable methodology for architects and retailers to deploy adaptive, smart retail environments that respond to consumer demand, operational
feedback, and spatial constraints.