Local optimizations often shift friction from one part of the store to another. We build models that integrate signals across space, time, and use cases, so that interventions improve the system as a whole instead of creating hidden side effects.
Physical retail produces fragmented data streams—traffic, dwell, queues, service, availability—that are typically analyzed in isolation. BIEM fuses these microscopic signals into holistic models that capture how changes in one area ripple through others, across stores and over time. By structuring human and environmental signals into shared feature spaces, BIEM uncovers system-level patterns and trade-offs that single-use-case tools cannot see. Retailers gain a defensible basis for designing interventions, policies, and experiments that improve overall performance rather than simply shifting bottlenecks around.