Poorly aligned checkout capacity turns healthy demand into queues, walkaways, and frustrated staff. We expose how customers actually arrive at checkout, where bottlenecks form, and which allocation decisions keep waits short without overspending on idle lanes.
Physical stores experience sharp, local spikes in checkout demand that are difficult to see from aggregate sales or ticket data. BIEM turns pre-checkout flow and queue behavior into a measurable signal, showing how customers build up around different payment points, how long they wait, and when they abandon. By interpreting patterns in approach paths, queue formation, and service time, BIEM connects checkout performance to specific layouts, staffing choices, and lane configurations. Retailers gain a defensible basis for opening and closing lanes, steering customers between options, and redesigning checkout zones so that peak demand is absorbed with minimal friction.