Video analytics for retail stores

Video surveillance systems have ceased to be just cameras monitored by security officers making sure that someone who ‘forgot’ to pay does not take something out of the store or a negligent cashier does not cheat the customer. Today, these platforms use big data and machine learning, neural networks, and much more.

What is video analytics? It’s far more than shoplifting prevention

Video analytics today is a complex layer of technologies based on computer vision and machine learning. And this technology is becoming increasingly commonplace. For instance, four out of every ten (41%) of England-based medium and large-sized businesses which are running CCTV systems have already deployed facial recognition analytics in their systems to capture human faces and compare images to databases with a view to identifying matches for access control, event security or for public safety purposes.

Retail is one of the business fields that makes successful use of video analytics systems and you can argue with conviction needs it the most. The pace of adoption in retail allows us to say with confidence that video analytics will become an integral part of our life and are likely to be ubiquitous within three or four years.

Many retail uses, many benefits

The main use of video analytics in retail is to combat loss and theft. In the US, retailers’ losses due to theft, fraud, and other causes totalled nearly $62 billion in 2019, up from nearly $51 billion the previous year said the National Retail Federation, the world’s largest retail trade association.

Video analytics helps not only to register a deliberate theft but also to identify a forgetful buyer, when a person accidentally passes by the cash register without paying for a purchase. A security officer stops him at the exit and asks him to pay. At this point, the forgetful customer gets watchlisted, this is a basic function in some video analytics systems. When this customer comes to the store again, security receives an alert monitors the customer closely.

The use of video analytics with overhead CCTV observation of the sales counter can be a real-time deterrent to incidents of internal shrink according to the National Retail Federation. Video analytics is the capability of automatically analyzing video to detect and determine if an anomaly has taken place based on a set of instructions built into the video software. However, its use can go far beyond identifying theft.

Keep customers coming back

For instance, modern algorithms can identify someone who forgot something in the store. The system sees when a customer enters, for example, with a bag, puts it down and leaves without it.

In video analytics systems, there are also «whitelists» that can be used to improve loyalty programs. The client uploads a photo for his account and at the checkout or the entrance, the system recognizes the customers and sends a notification to staff. VIP customers can be greeted by name and then offered tailored product recommendations.

In addition, video analytics systems enable the creation of an ID for personalized advertising in the area near the cash register for a loyalty program participant. Retail is not actively using this idea yet, but restaurants already have, for example, the CaliBurger chain in California allows registered customer to pay using their face. In a step further, face recognition also enables personalized offers and speeds up ordering.

Farewell to plastic

In the medium term, the use of video analytics will likely allow us to completely abandon plastic cards. In 2020, in Russia, a Koshelek application (which stores loyalty cards) led to over 300 million cards being transferred to this method. Plastic cards were willingly abandoned.

Video analytics systems also make it easier to keep track of employees’ working hours and also their location in a particular department, at lunch or other breaks. At a general level this may have slightly sinister connotations but for companies who suffer from poor productivity it can be a great leveller allowing the retailer to ‘reclaim’ its workforce. The data from this system can be combined with information from ERP platforms.

Queue no more

Video analytics is also useful when it comes to queues. For instance, a system can notify employees when people are unattended and stacking up at the checkout, in the fitting room, and so on. At the same time, it can collect information about queues, such as the number of people in the queue. This enables more effective resource management and can also increase average daily revenue by attending to people up were set to leave without making a purchase because of queues. A UK study by Honeywell revealed the queue prevention also increases customer loyalty by 35%.

Video analytics also helps deliver sophisticated, revenue-increasing management of products on the shelves. According to IHL Group, global retail loses approximately €900 billion a year because goods are not on shelves when customers are looking for them. A video analytics monitors shelves and sends notifications when products are running short, or a shelf has been emptied. In addition, the system also recognises when a product is in the wrong place.

Sophisticated planning

Another potential use is crowd analytics to create market reports to inform better management and planning based on data obtained from cameras. The system determines gender, age (with an accuracy of up to two years), calculates total number of visitors, including unique and returning ones, and helps to create a customer load schedule. It enables customer behavior tracking for movement through a store. This helps create store planning for customers. This idea can be scaled up for an entire shopping center.
IBM, in a study Video Analytics for Retail, spelt out clearly the benefits for retailers. It said store operations encompasses a wide variety of activities, many of which can be aided by video analytics, from planning store layouts based on customer path statistics to staff planning based on historical and instantaneous customer counts, at store entrances, departments and check-out queues. Merchandising activities can also be planned based on similar analytics choosing the location of a display based on customer paths, as well as measuring the effectiveness of a display based on customer counts coupled with sales figures.
A number of high profile retailers are already well down this route. Using audience analysis and advertising communication through strategically located media players they’ve increased sales substantially. Retail giant WalMart is going even further and building its own advertising platform to improve the user experience of customers, partly through video analytics. The company’s strategy includes media activity via TV sets in stores and outdoor screens, improving digital advertising in partnership with third-party agencies, and much more.

The roadblocks for retail

The main difficulties of using big data-powered analytics are related to the lack of the infrastructure for collecting information and the lack of historical data. For example, in video analytics, many retail projects were implemented during the COVID-19 pandemic, and the customer behavior model may change when retail returns to pre-pandemic levels of shopping.

Further incomplete or insufficient coverage of shopping areas due to the lack of cameras is an issue. That said, the cost of video cameras is set to decrease in the next five years, and solutions based on video analytics will become increasingly ubiquitous.

Of course, data protection is of supreme importance. It’s extremely important to ensure the security and confidentiality of customer data. As such camera information must remain on a store’s local server and be well protected. And face images should not be stored as images but rather as a digital description, that is, a type of code that corresponds to the image. Further, to comply with data regulations the system should be configured so that collected data is deleted every day and only summary reports are saved.
The introduction of video analytics into retail is growing, consumer trust in smart solutions and the spread of cameras and sensors are increasing, and the requisite back-office infrastructure is improving. Retailers are also recognizing that video analytics technology helps reduce the number of customers who leave a store without buying and when used for planning can not only reduce losses but also grow revenue. As such within the next five years expect to see retail video analytics platforms become the norm and not the exception.

3 tips for the introduction of video analytics in retail

  • Decide on the budget and key tasks you want video analytics to perform. A golden rule of thumb is that the bigger the feature set, the faster the return on investment especially when used to boost sales.
  • Consider the activity segments. In grocery chains it is important to work with queues, analyze visitor data and use loyalty programs at the checkout. In food only retailers there can be an acute problem with customers forgetting to pay for goods. Non-food retailers benefit from personalized offers, sales area analytics and automation of marketing tools.
  • Inform customers about the introduction of video analytics and ensure you are complying with the relevant data protection laws. The consumer has the right to know that a store, for example, is running a facial recognition system.
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