Artificial Intelligence and Machine Learning technologies are successfully applied to improve efficiency in various industries. Retail is one of the areas that has taken advantage of these tools. Let us tell you how smart solutions help to reduce costs, plan sales, and increase profit in retail.
Artificial Intelligence (AI) is the ability of computer programs to perform various functions by imitating human behavior. Machine Learning (ML) is a type of AI, the essence of which is to solve issues through training a smart algorithm or programming it to self-learn.
The main advantage of such technologies is their ability to analyze information thousands of times faster than humans. Algorithms allow businesses to trace the logic of actions and make more accurate decisions.
According to forecasts of McKinsey, 70% of all companies in the world will apply AI-based solutions by 2030. Even today one can see how such technologies are changing the retail industry.
Conventionally, the fields of application of AI and ML are divided into the five major groups:
Let’s consider each of these areas in more detail.
Customers’ demands and expectations are constantly becoming more complex and require prompt responses and non-trivial solutions from retailers. Personalization is one of them. Recommendation algorithms pick up and record customer behavior, memorizing their favorite goods, the frequency and time of visiting stores, the duration of shopping, etc. According to these and some other parameters, buyers are classified by similar customer behavior. This data is used, for example, to recommend them future purchases.
The research by the Boston Consulting Group has shown that brands applying new technologies to create personalized customer recommendations increase their profits by 6-10% two to three times faster than those not doing so.
Both large marketplaces like Amazon and small firms are turning to personalization. For example, the fashion company River Island applies AI for collecting and analyzing information about consumer tastes and preferences to create new collections.
In 2014, Starbucks launched a game for customers to increase their engagement and loyalty and convince them to try new drinks. Initially, the game was the same for everyone, but then the company began to apply AI and set up personalization. Now, actions are tailored to customers, based on their previous experience and orders. The development tripled Starbucks’s marketing campaign outcomes and increased brand loyalty rates.
Another possibility that AI and ML technologies provide for effective communication with customers is building loyalty systems. Retailers define customer profiles, which also requires personalization. Such methods are applied both in online and offline businesses. When a customer enters a store, the system recognizes them. It already knows what kind of coffee they prefer, whether they have children, own a car, and so on.
Based on this, buyers receive the very offers they are interested in. As a result, both clients and the company benefit because the latter didn’t spend money on impersonal advertising “for anyone,” which wouldn’t work.
We’ve looked at a few examples of how AI and ML can benefit external communications in retail. These technologies are also successfully applied to solve a company’s internal issues.
Let’s assume that a retailer acquires a smaller player. Employees of this firm are worried that they will be laid off. In fact, no layoffs are planned – vice versa, business owners are going to increase the staff.
In order to avoid losing valuable employees, the company’s managers turn to a solution that will help them identify informal leaders and those who influence the opinion of the majority. The aim is to communicate an accurate message about the company’s plans through these people and facilitate teamwork.
AI-based programs for “scanning” the staff are built into the corporate network, monitoring employees’ communications, correspondence, calendars, plans, etc. The system analyzes semantic tags, determining those who give orders, receive them, criticize the company, or speak for it.
After collecting all this information, the system draws up a corporate map of employees, where they are all given roles, and one can track their level of satisfaction and burnout. In this way, a retailer can identify key employees and opinion leaders who will help to raise the awareness of the staff and motivate them. As a result, staff turnover is reduced, and the internal processes of the company are improved.
Another example is the application of AI and ML to automate warehouses and reduce operating costs. AI algorithms help to accurately calculate the required amount of goods, optimize warehouse balances, and reduce storage costs.
These technologies are also applied to store and shift the goods within retail shops more effectively. Smart programs can replace personnel in a store, helping customers quickly find the products they need. In addition, the system controls the availability of all the SKUs on the shelves.
Accurate pricing is one of the most important aspects of effective retail operations. AI-based solutions provide the necessary information for building a pricing strategy.
Retailers adjust prices depending on seasonal trends, new product launches, competitors’ activities, and consumer demand. Smart algorithms make it much faster and easier to perform a full-fledged analysis based on all these parameters. In addition, AI-powered programs are able to compare large data arrays over a long period.
Retailers are actively improving stores, including using AI-based technologies. For example, smart scales are appearing in grocery stores. They determine what product is being weighed. This saves waiting time in queues, minimizes store losses due to seller mistakes, and increases customer loyalty. X5 Retail Group – the major Russian retail chain – is among those using this solution.
Another example of effective store management is the application of AI to prevent theft. With the help of ML, the system memorizes recurring patterns by which a potential thief can be recognized. There is a classification of shoplifters into seven types that have a certain pattern of behavior. The algorithm correlates this data with the actions of customers in a store. This eliminates the human factor and minimizes the likelihood of program errors with each iteration, as well as allows the retailer to prevent losses and save on security services.
Amazon successfully applies AI and ML in its Amazon Go stores based on Just Walk Out technology. There are no sellers in these stores. The stores are equipped with special cameras that record the actions of buyers. The system is triggered every time a visitor picks up a product from a shelf. In this case, payment occurs only when a buyer leaves the store. These technologies allow customers to spend less time shopping and retailers to save on staff.
Such systems also generate information that helps to perform analytics. For example, a buyer takes goods from one manufacturer but then changes their mind and chooses the same product from another manufacturer. The algorithm compares these actions and draws certain conclusions that can be useful to both the retailer and the brands that produce these products.
Today, retailers apply AI and ML to solve their issues. According to forecasts by McKinsey, AI will provide the global economy with additional income of about 1.2% per year over the next nine years ($13 trillion by 2030).
The advantage of new technologies for retail is their ability to stay ahead. Of course, AI and ML can’t predict the future, but they help companies look into it with more confidence.
If you are considering incorporating smart solutions into your processes, consult Andersen’s experts. We’ll provide you with the necessary information and help you make the right choice.
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