The modern retail industry faces mounting pressure to optimise every square foot of physical store space in an era of intensifying e-commerce competition. This research paper investigates the relationship between customer movement pattern clustering and shopper flow efficiency (SFE) in retail store environments, employing a data-driven, secondary-data synthesis approach. Drawing on 47 peer- reviewed studies, industry analytics reports, and open-access retail datasets, a consolidated dataset of 200 store-level observations was constructed, spanning grocery, fashion, electronics, and mixed/hypermarket retail formats across five geographic regions. The study operationalises three independent variable dimensions: (a) customer movement patterns — foot traffic paths, dwell time, and heatmap density indices; (b) store layout design — aisle integration, product placement, and planogram compliance; and (c) shopper behaviour clustering — cluster count, silhouette score, and within-cluster dwell-time variance. The dependent variable, shopper flow efficiency, is measured as a composite index capturing congestion frequency, navigation smoothness, and conversion rate proxies. Reliability analysis confirmed strong internal consistency across all constructs (Cronbach\\\'s α = 0.871–0.966). Regression analysis revealed that the full model explains 71.4% of SFE variance (R² = 0.714; F(9,190) = 22.67, p < 0.001), with cluster silhouette score emerging as the dominant predictor (β= 0.512, p < 0.001). Independent samples t-tests demonstrated a 19.5-point SFE differential between high- and low-quality clustering stores. ANOVA confirmed no significant geographic variation (p = 0.396), indicating broad cross-regional generalisability. The null hypothesis is definitively rejected, confirming that analytical rigour in clustering methodology — not merely the presence of sensing infrastructure — is the primary determinant of layout optimisation success.