Your competition isn’t resting. According to the Lucidworks report, the retail industry is leading the way in AI adoption. If your business hasn’t embraced AI yet, it’s time to rethink your strategy. Artificial intelligence in retail is one of the best tools for improving customer service and personalizing the shopping experience.
At Intellias, we are actively expanding our innovation hub with R&D initiatives and competitive AI accelerators. Based on our experience, we want to share our key insights on how retailers can grow their business and increase profit with AI technology.
Artificial Intelligence and Machine Learning in Retail: Benefits
According to Statista, retailers who have adopted artificial intelligence (AI) and machine learning (ML) technologies have increased their sales and annual profit, outperforming those who haven’t implemented AI for retail.
This shows that retail companies are already reaping significant returns on their AI investments—and for good reason. Let’s explore the key benefits they’re experiencing.
Unified customer data
AI-powered systems provide a 360-degree view of your customers by consolidating all their interactions with your brand. This comprehensive understanding enables you to predict shopping behavior, deliver tailored offers, and forecast shifts in customer demand. With these insights into consumer behavior, the retail experience becomes more of a deep, personalized conversation rather than just general small talk.
Better customer service
AI chatbot assistants, trained on customer data, represent the next generation of hyper-personalized shopping experiences. Consumers want to find what they need quickly, no matter where they shop. AI chatbots, capable of processing text, voice, and image prompts, help customers find and purchase products in the blink of an eye.
Optimized logistics operations
In the next 5 years, AI could become a game-changer in last-mile delivery by analyzing real-time traffic data and suggesting optimal delivery routes. But that’s just the beginning. Cognitive robotics may revolutionize complex last-mile deliveries, with robots following voice commands and interacting directly with customers. For example, robotic warehousing and sorting, already used by giants like Amazon and FedEx, are expected to double by 2029.
Let’s take a closer look at the applications of AI in retail.
AI Applications in Retail
The applications of AI in retail are so diverse that it’s impossible to cover them all in one article. However, let’s explore some of the most common ones to get a bird’s-eye view of just how powerful AI can be.
Market trends prediction
The main benefit of artificial intelligence and machine learning in e-commerce and retail is the ability to process large volumes of data at speed. Provided with sales records, market information, and consumer data, AI can discover market trends, predict consumer behavior, forecast sales, minimize customer churn, optimize restocking, and more.
Based on consumer feedback, inventory state, and sales trends, AI can make specific business predictions that arm retailers with the information they need to enhance productivity, reduce waste, prevent errors, improve scalability, and boost marketing campaign success rates.
Consumer experience automation
According to a survey of 20,000 consumers in 26 countries, only 14% of people have a positive experience shopping online. This drops to just 9% for in-store shopping.
Artificial intelligence systems can provide customers with 24/7 support, allowing them to focus on other important tasks. Such support includes in-store assistance, virtual mirrors, cashless checkouts, mood tracking, customer recognition systems, etc. By automating tasks that have been traditionally reserved for humans, AI helps retailers boost customer satisfaction while reducing labor costs and preventing fraud.
Inventory design and space usage optimization
Artificial intelligence can also optimize inventory design and space usage. By considering consumer preferences, expiration dates, color schemes, locations, seasons, and weather, AI-driven robots can better design shelves and produce them faster than human beings. The result—a boost in sales and a reduction in space waste.
Data-driven personalization
In today’s competitive landscape, only those who truly understand their customers will succeed. Consumers expect companies to recognize them and provide a seamless experience across all touchpoints. AI algorithms and data analytics enable retailers to analyze customer data and offer personalized deals and product recommendations.
For example, AI can personalize a product search page by taking into account specific criteria such as color, size, material, budget, and delivery timeline. By leveraging this information, companies can tailor search results based on past interactions, enhancing the overall shopping experience.
Improved in-store operation
AI in retail stores enhances the customer in-store experience and optimizes inventory management through smart shelving, queue management, and in-store analytics.
Smart Shelving: Shelves equipped with sensors monitor product availability in real time and trigger alerts for restocking when needed.
Queue Management: AI systems analyze customer flow and service speed, providing insights to reduce wait times during peak hours.
Examples of AI Implementation from Top Retail Enterprises
Artificial intelligence and machine learning technologies have the potential to revolutionize the retail industry by adding efficiency, accuracy, and personalization capabilities. From what we see, it’s already happening. The examples below reveal how AI-powered technologies help retailers improve consumer experience, restocking and inventory management, delivery, and price adjustment.
Consumer experience
Building fruitful and long-term relationships with customers is a crucial part of the retail industry, and artificial intelligence offers endless possibilities in this respect. Here are a few examples:
- How about giving your customers a WOW experience? Sephora is leading the way with its Color IQ AI-powered tool, which uses facial recognition to scan consumers’ faces and provide personalized product recommendations
- Kroger, a major grocery chain, partners with an AI retail analytics company to enhance its product listings, use dynamic routing for last-mile delivery, and optimize store efficiency with digital twin-store simulation.
- Fashion company Stitch Fix is experimenting with GPT-3 and DALL-E 2 to deliver personalized recommendations. For example, if a customer asks for “high-rise, white, skinny jeans,” generative AI can produce an image of thie item, which a stylist can then match with a similar product from Stitch Fix’s inventory.
Restocking & inventory management
Ineffective inventory management can lead to missed sales opportunities, inaccuracies, and insufficient customer experience. Fortunately, AI systems are quite efficient in restocking. By crunching enormous volumes of data and identifying repeated customer behavior patterns based on trends, the state of inventory, sales history, weather, location, and other parameters, AI can minimize out-of-stock instances and avoid stocking up on items that won’t be popular with customers.
For example, IKEA is testing AI to analyze customer demand patterns across various channels and monitor preferences over time. This includes predicting products needed at different times of the year in over 450 IKEA stores and e-commerce sites across 54 markets.
Delivery
Amazon recently unveiled a new robotics system called Sequoia for its warehouses. This system can move up, grab, and handle items, transporting them to sorting stations. By reducing manual labor, it also decreases the risk of accidents and injuries.
Price adjustment & prediction
Retailers have increasingly realized artificial intelligence’s potential for price adjustment. Based on information about demand, seasonal trends, promotional activities, and other data, AI systems can predict the possible outcomes of different pricing strategies.
For example, Amazon employs an algorithmic pricing model that analyzes customer behavior, competitors’ prices, market data, and even weather conditions to optimize its pricing.
What technologies and solutions are used for AI in retail?
AI helps retailers automate decision-making processes with customer data readily available and enhances employee productivity, allowing them to focus on more strategic tasks.
- Deep learning algorithms: Convolutional Neural Networks (CNNs) & Recurrent Neural Networks (RNNs)
Deep learning, a subset of machine learning, uses neural networks to make predictions and extract deeper insights from large datasets. Convolutional Neural Networks (CNNs) are designed for grid-like data, such as images, and are commonly used in retail for visual search and product recognition. They help monitor shelf inventory, improve product placement, and track stock levels.
Recurrent Neural Networks (RNNs) excel at finding patterns in data sequences. Retailers use RNNs to analyze product sales, predict demand, and forecast revenue.
- Natural language processing (NLP)
Natural Language Processing (NLP) enables machines to understand and interpret human language. Retailers use NLP to better understand customer search queries and empower virtual assistants to handle more complex requests in a human-like manner. Additionally, NLP models can analyze all customer interactions with your brand — such as social media messages, reviews, and chatbot requests — to determine preferences, interests, and purchase intent.
- Predictive analytics
Predictive analytics combines historical data analysis, statistical models, and machine learning to recognize patterns and forecast future scenarios. Retailers use predictive analytics for product recommendations, dynamic pricing, and forecasting customer churn.
- Robotics and automation
In retail, robotic automation is employed for warehouse operations and inventory management. One application is shelf monitoring, where robots take photos of supermarket shelves. Using computer vision technology, these images are analyzed to generate reports with metrics on out-of-stock items and pricing insights.
Why you need AI in the retail industry
AI can transform three main pillars of retail: customer experience, supply and chain management, and in-store operations.
Customer experience
- Analyze customer data and provide hyper-targeted advertising and personalized offers
- Keep all the data about online and offline customer interactions with your brand in one place
- Respond faster with real-time inventory tracking
Supply and chain management
- Optimize supply chain to demand forecast and optimize inventory
- Monitor stock in real time and optimize the restocking process
- Improve pricing strategy by analyzing buying patterns and market state
In-store operations
- Reduce wait with the help of queue management
- Provide customers with interactive fitting rooms
- Implement self-checkout systems
Challenges of AI in retail
No innovation comes without challenges, and retail is no exception. Here are the main challenges retailers may face when introducing artificial intelligence in retail.
- Lack of training
Only about one-third of consumers who have used virtual assistants are satisfied with their experience, and nearly 20% do not wish to use them again. A major reason for this dissatisfaction is inadequate training; many virtual assistants lack the skills needed to answer customer questions or offer personalized service. To address this issue, it’s crucial to test virtual assistants across various scenarios and regularly conduct quality checks on all AI-handled processes.
- Privacy and security
As retail companies utilize large amounts of data to train models, it is crucial to ensure that data collection and processing comply with privacy and security regulations. Companies should clearly explain how they use customer data and obtain permission before tracking and analyzing customer behavior. Retailers must respect customers’ control over their data to maintain trust.
AI Solutions Implemented by Intellias
Retail and artificial intelligence work together to help retailers reduce costs, streamline decision-making, quickly adapt to market changes, and personalize customer experiences. However, many enterprises are still reluctant to utilize AI and ML in their processes. The main reason for this barrier is the lack of in-house experts specializing in AI technologies.
Luckily, Intellias has you covered. Combining tried-and-proven software development practices with the latest technologies, we help businesses grow with the power of AI. We’ll analyze your case and help you deploy the specific AI-driven solution for your business needs. Below are the solutions we’ve already implemented for our clients.
- Online context in-store: CV-powered mobile apps that recognize different in-store products and provide contextual information.
- Personalized experience: The facial recognition system which boosts customer loyalty levels by identifying regular customers.
- Shopper measurement: A computer-vision system that recognizes the gestures, poses, and emotions of customers to provide more insight regarding the footfall, pass-by traffic, and specific customer satisfaction.
- Inventory management: An item and planogram recognition systems that enable real-time, shelf-management, and inventory optimization.
- In-store theft prevention: CV technology that monitors cameras and alerts security staff if potential risks are identified.
- Checkout-less sales: LBS and CV technology that enables checkout-less sales to eliminate long queues, improve customer experience, and to free salesforce for other tasks.
- Robotization: The AI-driven camera that provides data to build an indoor navigation map for stores, and also enables object and edge detection for sorting arms.
- Delivery optimization: LBS- and CV-driven route optimization function for the transport management system.
- Content improving: Text and image content recognition technologies that identify incorrect product attributes, increasing the Product Information Management efficiency.
- Intelligent pricing: The AI-driven product pricing system which adjusts prices based on customer interests and activity, as well as stock availability forecasts.
- Demand planning: Generative AI models can analyze historical sales data and market trends to improve demand forecasting accuracy.
- Employee assistance tool: a smart bot for back-office automation.
- Electric vehicle fleet routing: a fleet management system for route planning, charging, maintenance, and remote fleet monitoring.
Future of AI in retail
If we had to describe the future of artificial intelligence in retail in two words, they would be personalization and automation. Predictive analytics will enhance personalization by forecasting customer needs, allowing retailers to gain deep insights into customer behavior and adjust their strategies accordingly.
AI will also transform traditional merchandise processes and automate routine tasks. With advancements in predictive inventory management, automated restocking, and shelf monitoring, retailers will streamline inventory flow and optimize workforce planning.
Conclusion
Artificial intelligence technologies span throughout every aspect of the retail industry. Leveraging the power of big data, machine learning, deep learning, and natural language processing, artificial intelligence allows retailers to effectively analyze massive data volumes, make timely decisions, build meaningful relationships with customers, improve supply chain, and much more.
Retail giants, like Amazon, Walmart, and eBay, already reap the benefits of these next-age technologies. Yet, over 70% of retailers have not discovered the power of AI. Is this the case for you? That’s where Intellias comes into play.
