The rapid evolution of artificial intelligence (AI) has led to its widespread adoption across various industries, including retail. According to a Global Market Insights study, the market for retail AI solutions that was valued at $6 billion in 2022 is expected to grow by more than 30% per year from 2023 to 2032. This primarily points at the growing popularity of AI-based solutions as well as the retailers' critical need to keep up with the times and streamline processes to stay competitive in the marketplace. In this article, we will explore the benefits of AI technology in the retail sector and examine real-world examples of its successful implementation.
Artificial Intelligence and Its Impacts on the Retail Industry
Science fiction has formed a kind of mystical image of AI in people's minds. Imagination has given it the quality of superintelligence. However, what is called artificial intelligence today is nothing more than a set of algorithms of varying degrees of complexity. Some of these algorithms consist of components combined to produce calculations so complex that even data scientists have trouble explaining them. Others are so simple that any clever computer science student can enter them into a computer program to beat you at checkers. It would be incorrect to say that one is better than the other. They simply use different types of data and are applied to different problems.
That is why it might seem fair to say that retailers have no need for an AI solution. After all, they need to solve current problems and new industry challenges faster and optimally. In fact, AI in retail still acts as a complement to "old" technologies such as statistical analysis and rule-based heuristics in transforming processes. AI algorithms can provide valuable insights based on analyzing vast amounts of data and finding correlations, but retailers still require the human ability to prioritize and solve uncertain or unforeseen problems. Thus, contrary to all the pessimistic predictions of futurists and sci-fi experts, the synergy of “man and machine” still looks more likely than not.
Artificial intelligence has proven to be a valuable tool to help retailers build more profitable operations. Using AI technology and data-driven analytics, retailers can optimize processes, reduce costs, improve operational efficiency, and enhance customer experience.
7 Biggest Benefits of Using AI in Retail Business
Any retail business is designed to make a profit. To achieve business goals, owners and managers always strive to increase sales, margins, and bolster customer loyalty while reducing operating costs. Here are some examples of how AI contributes to cost-effective retailing and enhancing the customer experience.
1. More Accurate Retail Demand Forecasts
AI algorithms analyze historical sales data, market trends, and external factors to make accurate demand forecasts. This helps retailers optimize inventory levels and reduces the risk of overstocking or being out of stock. By aligning inventory with customer demand, retailers can minimize storage costs, reduce waste, and avoid unnecessary costs associated with excess inventory or lost sales.
2. Supply Chain Optimization
AI in retail business can improve supply chain management by providing real-time visibility into inventory levels, production schedules, and logistics operations. AI algorithms analyze data from various sources, such as sales forecasts, transportation data, and weather conditions, to optimize routing, minimize transportation costs and improve overall supply chain efficiency. By streamlining the supply chain, retailers are able to cut costs and achieve faster delivery times
3. Routine Tasks Automation and Workforce Optimization
AI-based algorithms analyze historical sales data, customer traffic, and other factors to predict future demand patterns. This allows retailers to accurately forecast staffing needs and optimize work schedules. By aligning employee schedules with expected demand, retailers manage to avoid understaffing or overstaffing, which leads to increased productivity and cost savings. In addition, AI can be used to automate repetitive and time-consuming tasks to free employees up to focus on more complex and useful activities. For example, AI-powered systems can manage inventory, process orders, and input data so that employees can devote their time to customer service, problem-solving, and strategic tasks that require personal creativity and acumen.
4. Intelligent and Dynamic Pricing
AI-powered pricing systems analyze market trends, competitor prices, and customer behavior to optimize pricing strategies. Dynamic pricing algorithms can adjust prices in real time based on factors such as demand, inventory levels, and prices of competitors. By implementing dynamic pricing, retailers can maximize revenues, improve profitability, and remain competitive in a dynamic market environment.
5. Personalized marketing and customer targeting
AI allows retailers to use data about customers and their behavior to conduct personalized marketing campaigns. And it's no longer just about analyzing search queries but also using computer vision. Computer vision allows shoppers to search for items using images rather than words. This technology has revolutionized the retail industry by enabling advanced visual analysis and recognition capabilities. Retailers use computer vision to identify and track customer demographics, emotions, and behavior, which allows them to provide relevant product recommendations and enhance the shopping experience. By analyzing customer preferences, purchase history, and browsing behavior, AI algorithms can tailor marketing messages to individual customers. This targeted approach helps improve marketing effectiveness, increase conversion rates, and reduce marketing costs associated with large-scale campaigns.
6. Shelf monitoring and layout control
Computer vision systems help retailers monitor store shelves through photo recognition or cameras in real time, detecting out-of-stock items, misplaced products, or gaps on shelves. This helps retailers ensure product availability, optimize inventory management, and improve the overall shopping experience.
7. Fraud detection and loss prevention
AI algorithms analyze vast amounts of data to identify patterns and anomalies that may indicate fraudulent activity, such as payment fraud or inventory shrinkage. Moreover, computer vision systems can analyze surveillance footage to detect suspicious activity, identify shoplifters or prevent fraud. This increases store security and reduces losses from theft. By proactively identifying and preventing fraud and theft, retailers can reduce financial losses and minimize the impact on the bottom line.
What Can AI Optimization Do for Your Retail Business?
Here are some other global advantages of artificial intelligence that we simply must mention:
Attracting and retaining customers
In a competitive retail environment where exciting shopping experiences abound, traditional retailers must engage customers with personalized and relevant interactions that are truly unique and inspiring every step of the way. With AI, there is no space for empty shelves or unmanaged promotion expectations.
Quick and informed decision-making based on data
It is a real challenge for retailers to transform the abundance of data from disparate sources across the supply chain, store, and all available consumer data into consumer-focused strategies. By effectively filtering out the noise, AI helps retailers gain valuable insights and shape consumer-centric approaches.
Creating agile logistics chains
Retailers need to rethink their traditional supply chains and implement adaptive and agile ecosystems to meet the changing needs of customers. This will allow them to respond quickly to changing consumer behavior and meet a broader range of their needs, however niche or mainstream they are.
Harmonizing offline and online retailing
Digital and physical shopping channels often work with different incentives and approaches, which creates challenges for shoppers seeking a seamless shopping experience. Retailers can eliminate operational inefficiencies and provide a frictionless shopping experience by treating these channels as a single whole using AI.
Exciting cases of artificial intelligence use in retail
As we have seen so far, the applicability of AI algorithms in retail is rather broad and AI can be used to solve a variety of different tasks. While some retailers are still hesitant to automate replenishment or predict demand using AI, global players are already making full use of the technologies of the future. What follows are inspiring and exciting cases of the use of AI and machine learning in retail.
The world's first automated supermarkets from Amazon
Amazon Go is a prime example of AI-powered technology transforming the retail experience. These cashier-less stores utilize a combination of computer vision, sensor fusion, and AI algorithms to enable customers to shop without the need for traditional checkout processes. The stores use cameras and sensors to track items picked up by customers and automatically charge them when they leave the store. This AI-driven system provides a seamless and frictionless shopping experience, enhancing convenience and efficiency for customers.
Walmart's Intelligent Store Assistants
Walmart, one of the world's largest retailers, has implemented AI-powered store assistants to improve operational efficiency and customer service. These intelligent assistants use machine learning algorithms to monitor inventory levels, track product availability, and identify areas that require restocking. They also assist customers by answering questions, providing product recommendations, and guiding them to specific store locations. By leveraging AI, Walmart aims to streamline operations, enhance the shopping experience, and optimize store performance.
Kroger's Smart Shelving and Pricing Optimization
Kroger, a major US grocery retailer, employs AI to optimize store shelves and pricing. Smart shelving systems use computer vision and AI algorithms to monitor product availability, identify out-of-stock items, and ensure proper shelf replenishment. AI-driven pricing optimization algorithms analyze various factors, including competitor prices, demand patterns, and seasonality, to determine optimal pricing strategies for different products. These AI-powered solutions help Kroger streamline operations, reduce stockouts, and maximize revenue through data-driven pricing decisions.
Artificial Intelligence in Retail: LEAFIO AI Software
We at LEAFIO are incredibly inspired by the cases of global retailers using AI technology. However, our mission and dream is to make technology available to businesses of any size and in any part of the world. That's why our AI-based solutions are flexible, user-friendly, and let retailers see the immediate results within the very first few months after implementation.
LEAFIO AI Demand Forecasting: the power of AI for accurate planning and strategic decision-making
LEAFIO's demand forecasting algorithms take into account many variables that affect demand – traffic, sales, seasonality, promotions, etc. That makes managers' work easier and greatly increases forecast accuracy. LEAFIO algorithms can calculate and determine the degree of influence of each factor on demand separately or in combination. In other words, the system considers only statistically significant factors and discards the unnecessary ones that would reduce the accuracy of the forecast.
The forecasting model makes it possible to catch seasonal spikes early and allows more accurate inventory planning, avoiding both under and oversupply. Owing to machine learning algorithms, forecasting accuracy is on the rise, ensuring the best fit of inventory with current business needs, reducing surplus, freeing up frozen assets, and reducing product write-offs, resulting in the efficient use of resources.
Using LEAFIO's AI-based demand forecasting increases forecast accuracy by up to 50%, reduces lost sales by 70% and surplus by 50% on average.
LEAFIO Fresh: fresh products customers and profit for retailers
The LEAFIO fresh produce algorithm helps retailers replenish stocks of perishable products at the right time and in the right volume for maximum availability with minimum write-offs. It accounts for fluctuations in demand according to the day of the week, which is especially important for products with limited shelf life and frequent deliveries. Using batch accounting principles (LIFO), the system accounts for the actual shelf life of products once they are placed on the store’s shelves, helping retailers effectively prevent expired products. In addition, the algorithm considers demand variability, lead times, packaging factors, supplier restrictions, buffer inventory, and display requirements. The retailer can set the desired level of availability for each item, minimizing write-offs and maintaining an optimal inventory balance. This comprehensive approach helps retailers keep customers happy by providing fresh products at all times and minimizes waste and markdowns.
Customers can typically observe a 10% reduction in excess inventory and a 10% increase in availability while reducing write-offs by 15% as a result of the use of the fresh produce inventory management algorithm in the first months after implementation.
LEAFIO Promotion Intelligence solution for building an end-to-end promotion process
The AI algorithms behind the sales promotion management solution are designed to provide the retailer with the necessary inventory before and during the promotion and to end it without overstocks or write-offs. The solution provides highly accurate inventory calculations based on performance analysis and demand throughout the product's sales history. When launching promotions for new SKUs, the promotions management software will use the sales figures of analogous or similar products to generate the calculations. This way, each promotion is not based on a hypothesis, but on predictable and manageable results. Machine learning algorithms will also improve the quality of predictions for each subsequent promotion, so that promotional offers are always available to your customers, and you can maximize profits without overstocking after the campaign is over.
Retailers see a 20% increase in promotional merchandise availability while reducing surplus levels by up to 50% as a result of using the AI-powered promotions management solution.
Conclusion
The range of AI applications in the retail business is incredibly broad and will most definitely continue to grow in the coming years. The introduction of technology is transforming businesses and providing additional opportunities to boost revenue. At the same time, it is important for a retailer to understand the way a particular technology fits into the short-term and long-term business strategy and the tasks and issues it is designed to solve. At LEAFIO we believe that technology implementation is not an end in itself, but that it is only an instrument for achieving specific financial targets of our clients. The bottom line is that profits do not drive technology adoption, technology adoption drives profits.
FAQ
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What should a retailer consider before getting into AI implementation?
Before embarking on implementing an innovative AI solution, retailers must clearly define their goals, assess their data readiness or at least data collection capabilities, evaluate the infrastructure and team skills, and include a return on investment (ROI) assessment. These factors are essential to the successful implementation of technology solutions in retail.
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What questions should you ask an AI-based solution vendor?
- What do you mean by an AI-driven solution?
- What type and amount of data is needed for your solution to work effectively?
- How does your algorithm improve on traditional approaches to solving inventory problems?
- Can you provide real-life examples that demonstrate the practical application of your solution?
- Which of your existing customers can tell us about the effects of the solution? -
How is AI used for retail predictions?
AI-based demand forecasting works by analyzing historical sales data, market trends, seasonality, and various other factors using advanced machine learning algorithms. These algorithms identify patterns, correlations, and dependencies within the data to generate accurate predictions of future demand. The models continuously learn and adapt to new data, improving their forecasting accuracy over time. This enables retailers to make informed decisions regarding inventory management, production planning, and supply chain optimization, leading to better customer satisfaction, and increased profitability.
