The global artificial intelligence in the retail market exceeded over USD 2 billion in 2020 expected to register a CAGR of 35% in the forecast period (2021 – 2026).
Fortune Business Insights states that by the end of 2028, the market size of AI in the global retail sector will reach USD 31.1 billion
“Artificial Intelligence is an engine that is poised to drive the future of retail to all-new destinations”
Today’s dynamic retail industry is built on a new covenant of data-driven retail experiences and raised consumer anticipations. But delivering a personalized shopping experience at scale — relevant and valuable — is no easy feat for retailers.
As digital and physical purchasing channels blend together, the retailers that are able to innovate their retail channels will set themselves apart as market leaders.
Why You Need AI in Retail
Captivate Customers – With a plethora of innovative competitors delivering shoppers an immersive shopping experience, traditional retailers need to engage customers in a personalized and relevant manner that is uncommon and inspiring across all touchpoints.
Create Exciting Experience –Retailers need to continue to differentiate their products and offer consumers compelling services and experiences in order to keep consumers interested. Integrating predictive analytics can help retailers anticipate changes in the market and lead with innovation, rather than responding to them.
Extract Insights from Disparate Data – Faced with a continuous flow of information from all aspects of business, from the supply chain to stores to consumers, retailers are filtering the noise to transform these disparate data sources into consumer-first strategies using AI.
Synchronize Offline & Online Retail –Digital and physical shopping channels typically operate under different initiatives and approaches but treating these channels as separate business units can create friction for customers seeking a seamless shopping experience and lead to operational inefficiencies. AI helps in reducing this friction and creating a seamless Omnichannel Experience.
Uses of AI Retail
Retailers are investing in technologies that help customers in the shopping process and also help store staff in boosting their productivity.
America’s largest supermarket chain Kroger has rolled out smart shelving to 120 of its stores. The technology known as EDGE – which stands for Enhanced Display for Grocery Environment, offers a personalized, interactive shopping experience for Kroger customers.
Lowe’s autonomous in-store robot,‘ Lowebot’, helps customers find what they need in the store in different languages. At the same time, the system helps with inventory management thanks to real-time monitoring capabilities.
Visual search systems that use artificial intelligence allow customers to upload images and have the system find similar products based on colors, shapes, and patterns.
Cortexica’s image recognition technology has an average accuracy of 95%. Customers have given the Find Similar feature positive feedback, with 90% of them saying it was helpful.
American Eagle’s IR technology uses Visual Search, which helps shoppers get clothes that look similar and make suggestions about what would work well with them.
Supply Chain Management and Logistics
Retailers around the world each lose $1.1 trillion annually due to poor Supply Chain Management. AI can eliminate leftovers and out-of-stock situations in the retail supply chain to help calculate demand for products, based on historical sales data, location, weather, trends, promotions, and other factors.
This has been done successfully by Morrisons in 491 stores with the help of AI. This resulted in a significant reduction in the amount of space on store shelves that were empty.
Customer Behavior Prediction
Artificial Intelligence can help retailers understand how customers behave and personalize their approach for each customer. AI-driven Intelligent Incentive platform can analyze customers’ psychology and emotions in order to increase purchases. The algorithm can process customer emotions and behavior during previous shopping experiences and try to provide optimal pricing offers for a particular visitor.
Benefits of AI in Retail
Increased supply chain efficiencies
AI can play a role in retail by helping to automate some tasks and improve efficiency.
Robots can help employees pick and pack orders, freeing up employees to focus on other tasks. Retail companies which are using computer vision and product recognition software in warehouses, allow them to reduce the number of orders handled by employees.
Improved Customer Satisfaction
Artificial intelligence helps retailers provide better customer service. There are a number of ways in which improvements can be made to make shopping more efficient and personalized. Some options include automated checkouts, increased personalized discounts, and 24/7 customer service via chatbots.
Companies are creating checkout-free experiences, meaning no queues and ultimate convenience. Amazon introduced an AI-powered store through the innovative Amazon Go, which uses computer vision to identify when customers put an item in their baskets. Once done a customer can self-checkout and Amazon charges you automatically.
To determine the best price for each product, AI collects the following types of information:
- Prices of other products
- Promotional activities
- Sales figures
Based on the data, AI can come up with various pricing strategies for the same item. The retailer can test these strategies and define the best one.
Artificial intelligence (AI) is reinventing the retail landscape. From using computer vision to customize promotions in real time to applying machine learning for inventory management, retailers can harness AI to connect with their customers and operate more efficiently. With the advance
To compete today, retailers must respond to their customers like never before, all while eliminating waste and inefficiencies from their operations. Data can you get there, but making sense of the sheer volume of it takes serious intelligence. AI in retail—including machine learning and deep learning—is key to generating these insights.