Join leading companies like CarMax, Discount Tire, and Yamaha who are using Leverege to transform their real-world operations.
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Join leading companies like CarMax, Discount Tire, and Yamaha who are using Leverege to transform their real-world operations.
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Leading companies like CarMax have implemented AutoTrace to track hundreds of thousands of mobile assets, like vehicles and car keys, reducing costs and driving new revenue.
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Leading companies like TPI Composites rely on WorkWatch to improve production efficiency, security and safety with complete operational visibility.
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Leading companies like Discount Tire have implemented PitCrew in all their service centers to achieve maximum performance and throughput.
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Leading companies like Schnucks Markets have implemented ExpressLane wherever they have lines of people or vehicles, delighting customers with shorter wait times and faster service.
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Compare re-identification (RE-ID), facial recognition, and Wi-Fi tracking to understand which technology offers the best customer journey.
Businesses want to improve operations, reduce wait times, and boost sales, all while maintaining customer trust. That is where technology like re-identification (RE-ID) comes in.
RE-ID offers the best of both worlds. It delivers precise customer journey tracking without compromising privacy. Compared to facial recognition and Wi-Fi sniffing, it is emerging as the most ethical and effective method. Especially when powered by solutions like ExpressLane, RE-ID can help teams optimize store layouts, queues, and staffing decisions while keeping customers completely anonymous.
Re-identification, or RE-ID, is a computer vision technique that anonymously tracks the same individual across multiple camera views. It does not rely on personal information like facial features, names, or device IDs. Instead, RE-ID creates a unique visual signature using clothing patterns, body shape, and movement. For example, if a person in a red jacket and jeans walks into the store, the system notes those visual traits. When another camera later sees a person with matching features, it recognizes them as the same visitor.
Modern RE-ID uses deep learning. Neural networks are trained on large datasets to generate feature vectors—mathematical representations of a person’s appearance. These vectors allow systems to match individuals across different camera angles and lighting conditions, even when the face is not visible. The result is a continuous journey map for each visitor, typically visualized as an anonymous dot or path on a floorplan.
RE-ID hits the sweet spot between operational insight and customer anonymity. It allows businesses to understand how visitors move through a space without ever knowing who those individuals are. There is no facial scanning, no phone tracking, and no personally identifiable information captured. Instead, people are represented as anonymous IDs based purely on appearance and motion patterns.
This approach not only safeguards consumer privacy, it also aligns well with global data protection laws such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States. Because RE-ID avoids biometric identifiers and does not require consent in the same way that facial recognition or device tracking often does, it provides a safer path to gathering behavioral data.
Unlike more invasive technologies, RE-ID makes it possible to collect meaningful analytics—such as time spent in zones, pathing trends, and queue performance—without crossing ethical boundaries. It turns what could feel like surveillance into a more responsible and respectful form of operational intelligence.
This privacy-first design is increasingly important in today’s environment, where consumers are more aware of how their data is collected and more sensitive to feeling watched. By using RE-ID, businesses can improve the customer experience and optimize operations while preserving public trust.
Although facial recognition can be highly accurate when the conditions are ideal, it introduces serious privacy concerns. The technology captures biometric data and can identify individuals personally, which is problematic in retail settings where customers have not opted in. Shoppers are likely to feel uneasy if they know their face is being analyzed, especially if it is unclear how that data will be used. Legally, retailers must be extremely cautious. In many jurisdictions, collecting facial data without explicit consent is illegal. Even beyond compliance, facial recognition is often not practical. It struggles when people wear masks, look away from the camera, or are viewed from the side. This makes it unreliable for tracking a customer’s full journey.
Wi-Fi tracking depends on detecting signals from shoppers’ smartphones. While it may seem easy to deploy, its reliability has decreased significantly. Newer phones randomize their identifiers to prevent tracking, and some shoppers disable Wi-Fi entirely. This means many people will not be counted. Additionally, Wi-Fi tracking does not follow people—it follows devices. A group of four shoppers might be counted as one visitor if only one person has an active phone. It also lacks location precision. It can approximate that a device is near a zone, but it cannot tell if someone is standing at a specific shelf or checking out. Lastly, tracking personal devices without permission is still considered invasive by many and may violate privacy regulations.
By combining re-identification with real-time video analytics, customer journey systems unlock a clear view into how people experience a physical space—from entry to exit. Solutions like ExpressLane leverage RE-ID to create a continuous, anonymous record of each visitor’s journey. This includes how long they waited, which zones they visited, and where bottlenecks or drop-off points occurred.
In environments like grocery stores, quick-service restaurants, and retail locations, this means operators can measure the full customer experience. ExpressLane captures key metrics such as wait times, service durations, queue length, and abandonment rates, allowing staff to respond to issues in the moment rather than after the fact.
For example, if a queue is growing too long, the system can trigger an alert so staff can open another checkout lane or reallocate resources. If customers frequently abandon the line at a specific point, managers can investigate the cause—whether it's understaffing, slow service, or unclear signage.
The power of RE-ID is that it provides these insights without identifying anyone personally. The customer becomes an anonymized path through space, helping businesses optimize layout, staffing, and service without compromising privacy. This blend of customer journey analytics and real-time operational awareness makes queue intelligence a critical tool for improving both performance and experience.
Retailers today need insight, but not at the cost of customer privacy. Of the available technologies, RE-ID is the only method that provides high-accuracy tracking without collecting personal data. Facial recognition may offer identity-level precision, but it raises red flags from both legal and ethical standpoints. Wi-Fi tracking, while easy to deploy, is fading in effectiveness and customer trust.
If you want to understand customer behavior in physical spaces, RE-ID is the clear winner. And if you want the best RE-ID-powered platform for queue analytics and journey tracking, ExpressLane delivers results that are both impactful and privacy-safe.
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