HOW SHOPLIFTERS ARE CAUGHT

All retail chains are subject to shoplifting in one way or another. Theft is one of the items of loss that retail management tries to reduce. But how are shoplifters caught? By certain means: video surveillance, anti-theft devices or security guards.

But there are also more advanced and effective methods — the use of predictive video analytics based on artificial intelligence or so-called computer vision.

Methods of dealing with theft

Most often the target of violations are self-service shops — there is almost no contact with the seller, crowded and everything can be touched.

There are a lot of ways to identify the thief, but among them we can distinguish traditional and more modern — let’s talk about them in detail.

Traditional tools to prevent theft

Retail outlets attempt to reduce theft through the following methods:

— Anti-theft devices:

— Entry/exit frames (triggers an alarm if unpaid goods are attempted to be taken out);

— Special labels and sensors that are attached to goods;

— Special mirrors that are placed in poor visibility areas

— Physical security-usually more effective in smaller outlets, in larger supermarkets this measure is not effective and is very costly in financial terms.

— A panic button helps prevent more serious, such as armed robberies. When pressed, the alarm button is signalled to the security company and a rapid response team arrives on the scene.

— Checkout control (re-weighing of goods, comparison of goods with the label).

Video surveillance

How do you catch shoplifters with cameras? Installing video surveillance as a method of combating shoplifting is very effective. It fulfils a large number of functions:

— helps to prevent theft;

— captures an already committed offence and helps to identify the suspect;

— reduces the cost of physical security;

— maintains general order at the point of sale;

— helps to collect statistical data on passability, efficiency of product display, etc.

Installation of cameras pays off quite quickly and allows you to judge more objectively about the conflict situation, as well as is proof of the guilt of the thief.

Very often the cameras are monitored live by a guard to prevent theft and detain the thief. However, the efficiency of such work is reduced due to the complexity of monitoring several cameras at once.

Video analytics

A more modern and reliable way to protect your property has started to be applied in retail outlets — it is not only video recording of everything that happens in the shop, but also live video analysis. Such possibilities can be provided by predictive analytics.

How does it work? The behaviour of an ordinary shopper and a shoplifter differs in terms of the sequence of actions, movements and behaviour in general. Special software has been developed. For example, the Predictive Analytics Company has developed the Shoplifter Prediction (SLP), which is able to detect the behaviour of a thief by analysing the video material captured by cameras in the shop in real time. If a person seems suspicious to the system, the guards or shop employees receive a signal about him (more details can be found on the company’s website antishoplifter.com).

This significantly increases the efficiency of the whole business. Let’s look at the advantages of using such security systems:

— help prevent theft;

— work on the basis of «machine vision» and human participation is not necessary;

— helps to assess the appropriateness of decisions on the zoning of the hall;

— analyses information about the lack of goods on the shelves, the length of the queue at the cash desk;

— help to control the work of employees.

The method of predictive video analytics in many ways helps not only to increase sales, but also to reduce the number of conflict situations in general-no one likes to stand in a queue for a long time or find an empty shelf where the necessary goods should be. Live video analysis can give hints to employees and improve the quality of service. All this leads to a more loyal attitude to the shop and positive evaluation from customers.

A clear advantage is that computer vision analytics can not only identify a potential thief, but also detect a person who has already been caught stealing. Based on the facial recognition system, video analytics capabilities allow comparing the appearance of the buyer with the existing database of criminals. This is signalled to employees.

We’ve covered how shoplifters are caught in retail shops and we hope this information will help you protect your business from the losses that shoplifters cause by their illegal attitude.