Physical stores often schedule people by habit, historical norms, or aggregate data that hides local peaks and troughs. BIEM turns microscopic traffic, dwell, and service patterns into a measurable demand surface, revealing where additional capacity has the highest marginal impact and where resources are routinely underused. By stabilizing human signals into structured features over time, BIEM links staffing misalignment to specific zones, roles, and dayparts. Retailers gain a defensible basis for reallocating people, refining roles, and adjusting shift structures so that staff presence matches the situations that matter most.