[2026 Latest] Prevention and Store Operations via Behavior Analysis AI

In the retail sector, shoplifting is a serious issue that directly erodes profits. However, as of 2026, with the accelerating labor shortage, few stores can afford the cost of stationing full-time security guards. In our consulting practice, we frequently see cases where crime prevention has become reactive due to a decline in veteran staff capable of sensing "suspicious movements." The solution to this challenge is a prevention system using "AI Suspicious Behavior Detection Cameras." We will explain the practical implementation process and operational techniques to turn customer service into a defensive weapon, shifting from traditional "recording the crime" surveillance to proactive "approaching before the crime" prevention.

The interior of a clean Japanese drugstore. A small black dome-shaped AI camera installed near the ceiling of an aisle overlooks neatly arranged product shelves. Japanese POP displays are attached to the shelves, and in the background, a Japanese store clerk is focused on restocking products, looking down at their hands. The interior is simple with no brand logos, and soft light from natural sources and LEDs illuminates the aisle.

1. From "Surveillance" to "Approaching": The Paradigm Shift of Behavior Analysis AI

A common concern we hear in our consulting practice is, "We have security cameras installed, but they're only used to check footage after an incident has occurred." This does not reduce the amount of loss. The essence of behavior analysis AI lies in quantifying "hesitation" and "scanning the surroundings" in real-time just before shoplifting occurs.

Even in stores that cannot hire security guards, AI acts as a set of "eyes," sending notifications to staff smartwatches or intercoms the moment suspicious behavior is detected. By simply having a staff member naturally ask, "Are you looking for something?" upon receiving this notification, you can maximize the psychological barrier that discourages the crime.

2. Three Signals of "Suspicious Behavior" Detected by AI

Figure: Detection Accuracy of Precursor Behaviors via AI Behavior Analysis (Verification data from stores where we implemented the system)

A common issue on the floor is confusing simple browsing with suspicious loitering. The latest AI makes judgments by combining the following three signals.

  • Unnatural Loitering: Staying in front of a specific shelf for a long time without appearing to be selecting a product.
  • Wary Glances: Frequently checking the positions of staff or other customers based on head orientation (pose estimation).
  • Product Interaction Patterns: Gestures such as unnaturally hiding a product once picked up, or the distance traveled while carrying an item without placing it in a basket.

By integrating this data and issuing alerts only when thresholds are exceeded, we prevent staff fatigue caused by false positives.

At an office desk, a Japanese data analyst is analyzing a store's heat map and behavior logs displayed on a PC screen. The screen shows a store floor plan with customer traffic patterns visualized in red and blue lines, alongside a graph showing the number of suspicious behavior detections by time of day. The analyst is intently focused on the screen from a 45-degree angle, operating a mouse with their right hand. In the background, a whiteboard with graphs written in Japanese is visible.

3. Integration into Store Operations and Staff Training

Even if you implement AI, it is a wasted asset if on-site operations do not follow suit. In our consulting, we emphasize scripting "how staff should move when an alert is triggered." The key here is the stance of "serving the customer" rather than "doubting" them.

An approach like "Are you looking for something?" appears as "attentive service" to normal customers, while exerting powerful pressure on those planning a crime by signaling that they are "being watched." This duality is the key to increasing crime prevention effectiveness without damaging the store's brand image. Furthermore, as is the case in our In-house EC Construction and Growth Support, behavioral data from physical stores is a valuable asset that leads to a better understanding of online customers.

4. The "Customer Service DX" Perspective: Balancing Crime Prevention and Sales Growth

The use of AI cameras is not limited to crime prevention. Behind suspicious behavior, there are also actions of "customers who are troubled because they cannot find a product." Correctly identifying these also contributes to preventing lost opportunities.

In our actual consulting, we design operations to function as "Customer Service Recommendation Alerts" simultaneously with crime prevention alerts. This allows staff to shift their mindset from the stressful task of "catching shoplifters" to the positive task of "helping customers in need."

A tablet device in a store's backroom. The screen displays a Japanese notification reading "Customer Service Recommended: Aisle 3," and below it, the silhouette of the target person captured by the camera and their dwell time are shown. Next to the device, a customer service manual written in Japanese and a smartwatch for staff are placed. Soft afternoon light streams in through the window, illuminating the wood grain of the desk.

FAQ

Q. Is there a risk of being rude to customers due to false positives?
A. AI alerts are used strictly as a "starting point for customer service." Rather than labeling someone as a culprit, staff approach them with standard service inquiries such as "May I help you?"; consequently, regular customers perceive this as an enhancement in hospitality.
Q. Is the implementation cost lower than hiring security guards?
A. Yes. While an initial investment is required, since 24/7 monitoring can be maintained with cloud costs starting from several tens of thousands of yen per month, a return on investment (ROI) is achievable within one year in most cases when compared to labor costs.
Q. Can existing security cameras be utilized?
A. While it depends on the resolution and network environment, there are many solutions available that can turn existing cameras into AI-enabled ones by adding an edge analysis unit. We will propose the optimal configuration based on the specific conditions of your site.

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Summary

To address shoplifting losses, it is essential to integrate AI-driven "suspicious behavior pattern recognition" with "natural customer service greetings" by staff. Overcome resource constraints that prevent hiring security guards by standardizing surveillance through technology. This system is not just for crime prevention; it represents a major step toward digitizing customer behavior in physical stores and improving the quality of customer service.

Published: October 16, 2026 / By: Osamu Yasuda
WRITTEN BY
Osamu Yasuda

Osamu Yasuda

Senior Managing Director & COO

Meets Consulting Inc.

Supported 100+ EC operations & logistics projects; specialist in operations and cost optimization

References

  • [1] National Police Agency: Crime Situation in 2023 and Current Status of Shoplifting Countermeasures
  • [2] Japan Retailers Association: Survey Report on Balancing Crime Prevention and Customer Service through Store DX
Disclaimer: This article is for informational purposes only and is not intended as a substitute for professional advice. It does not guarantee any specific results.