Mining Customer Journeys to Uncover Empirical Retail Agglomerations

Abstract

Shopping centers are a cornerstone of the retail system, with their success hinging on offering tenant mixes and layouts that stimulate cross-shopping. Despite the rise of e-commerce, physical retail remains vital as consumers increasingly seek blended digital–in-store experiences. Yet, traditional approaches to analyzing shopper behavior often rely on surveys or simple frequency counts, which fail to capture the complexity of customer journeys. This study addresses this gap by applying spatial big data and unsupervised machine learning to investigate empirical retail agglomerations. The research explores how structured, non-random co-visitation patterns within a shopping center can be systematically identified and leveraged within a tenant-mix strategy. Drawing on 24 million anonymized visits to a major Canadian shopping center, the study employs GeoAI and association rule mining, specifically the Apriori algorithm, to uncover high-frequency and high-lift co-visitation rules. Results reveal structured journeys that highlight strong co-visitation between anchors and specialty tenants, confirming that shopping center behavior is far from random. These patterns suggest optimal adjacencies and provide a data-driven framework for leasing and tenant layout. The study contributes theoretically by extending retail agglomeration research using unsupervised methods to examine behavioral clustering on a large-scale dataset empirically. For practitioners, the research approach offers actionable insights for leasing and tenant-mix optimization.