Why queues remain a key problem for retail and how AI helps eliminate them?
Queues in stores are one of the most common sources of customer dissatisfaction and direct financial losses for retail businesses. Customers leave without waiting, average check sizes decrease, and staff workload increases during peak hours. Classic methods – opening additional registers, redistributing staff, or static forecasts – are no longer sufficiently effective. Artificial intelligence models are taking the lead, capable of dynamically managing customer flow in real-time.
Neural networks transform store management into a predictable, algorithmically precise process: they analyze video streams, POS system data, RFID tags, sales history, weather conditions, and even local events to predict peak periods and automatically rearrange the checkout zone's workflow.
How do neural networks optimize customer flow?
AI systems operate at the intersection of computer vision, predictive analytics, and optimization models. Their task is not just to track queue length, but to prevent its occurrence. Retail gets the ability to manage load in the same way that logistics companies manage routing.
Key mechanisms of AI in the store
- Load forecasting. The model predicts periods of increased customer flow, taking into account dozens of parameters: time of day, weather, promotions, payday.
- Dynamic register opening. AI recommends when to turn on additional registers or redirect staff to high-load areas.
- Video stream analysis. Computer Vision in real-time tracks queue length and customer density in the hall.
- Self-checkout optimization. Models identify moments when users are more likely to make mistakes and adjust interface scenarios accordingly.
Systems of this class are being implemented in both large chains and small stores, as the launch of solutions has become noticeably easier. To estimate the approximate project budget, you can
→ use the cost calculator for developing a neural network by EasyByte.
Where does AI have the maximum effect?
The greatest efficiency is achieved at checkout points, where even a small delay multiplies for dozens of customers. However, neural networks also improve other parts of the process:
- Optimization of cashier schedules. AI creates convenient schedules for employees and stable ones for the business, taking into account flow forecasts.
- Avoidance of bottlenecks. Models find scenarios where customers accumulate near promotional stands or display areas.
- Improved customer experience. Faster checkout automatically increases loyalty and NPS.
If the business needs to select a suitable architecture or discuss integration possibilities with existing systems, you can
→ schedule a free consultation with an EasyByte expert.
Real-world cases of using AI to reduce queues and speed up checkout
Case #1: DK Stores (Delek US) – reducing queues by 67% with Mashgin AI checkouts
→ The DK Stores network has implemented self-checkout based on Mashgin computer vision: the customer simply places goods on the surface, and the system recognizes them without barcode scanning. According to the case, service speed increased 3 times, and the average checkout time decreased by 67% – some customers complete their purchase in less than 15 seconds. This allowed reducing queues and redistributing staff to more valuable tasks.
Case #2: Sodexo Live! & VisioLab – accelerating self-checkout in food courts and events
→ Sodexo Live! tested AI self-checkouts from VisioLab at several venues (arenas, congress centers, stadiums), where computer vision automatically recognizes dishes and generates a receipt. Over 4,400 transactions were processed through the system in 14 days with an accuracy of 99.8%; the average operation time was 24–26 seconds instead of “a minute or more” in the classic format. Queues have noticeably decreased, and revenue during peak hours has increased due to the increased throughput of food service points.
📌FAQ: frequently asked questions about using AI to reduce queues
Question: What data is needed to implement AI in a store?
Answer: Video camera data, POS statistics, sales history, staff schedules, weather data, and customer activity data.
Question: Can AI be implemented without replacing equipment?
Answer: Yes. Most solutions integrate with existing cameras and POS systems.
Question: How quickly can the effect be seen?
Answer: The first results usually appear within 2–6 weeks after launching a pilot.
Question: How accurate is AI in predicting checkout load?
Answer: Models achieve an accuracy of 85–95% depending on the amount of data and seasonality.
Question: Is such a system suitable for small format stores?
Answer: Yes, especially for stores with high traffic and narrow checkout zones.