AI (artificial intelligence) is disrupting all industries, and the retail industry is front and center.
Leading retailers are experiencing and engaging with an influx of AI in the retail market for some time now as it influences consumer shopping experiences and shapes retail operations.
Research tells us that global AI in retail market size is expected to hit USD 45.74 billion by 2032, with a compound annual growth rate of 18.45% from 2023 to 2032. (Precedence Research).
Let’s take a look at what AI in the retail market is all about.
What is artificial intelligence in the retail market?
Artificial intelligence in the retail market is a range of technologies, like AI-driven analytics, natural language processing, computer vision, and robotics, among others that have a likeness to human intelligence and mimic human actions. These capabilities are then applied to a variety of retail operations.
A close companion of AI is machine learning in retail.
Machine learning in retail is a subfield of AI
Machine learning is a type of artificial intelligence where algorithms are developed that continually learns and improves from data over time, without being programmed to do so.
AI and machine learning are often used together in retail to enable computer systems to automatically improve their performance.
How is AI in retail being used?
In the retail world, AI-driven analytics and machine learning are used in combination to:
-
- Analyze large amounts of data
- Identify patterns
- Make predictions
By using AI and machine learning in retail, business decisions are more informed with timely and accurate details.
As a result, AI in retail is transforming the way business is done, and is impacting the retail market in three major areas by:
-
- Improving the customer experience
- Increasing sales
- Reducing costs
We can see how AI and machine learning in retail use cases are making these improvements all around us and shaping today’s modern retail industry.
AI use cases in retail and examples
1. Demand forecasting
Predicting demand is so important for retailers because it informs all other retail planning functions.
The demand forecast gives the retailer how much inventory is needed, when it’s needed, and where it’s needed every day, and all year long based on what customers want and will want in the future.
Unfortunately, retailers making demand predictions who only use awkward, error-prone spreadsheets are limited in their ability to forecast demand because of overwhelming volumes of data and no way to analyze it in a timely, high-accuracy way.
However, leading retailers use modern, AI technology like AI-driven analytics to get the fastest and most accurate demand forecast possible.
AI retail use case: Simons
If we take a look at top fashion retailer Simons, we see how they use AI, specifically predictive analytics, to address their challenges of:
-
- Forecasting demand for products that had sporadic, slow sales and products with no sales history.
- Forecast accuracy for sized merchandise (fashion & apparel).
- Accounting for seasonality, weather, and demand spike events.
- Optimizing assortment depth vs diversity to maintain the right inventory levels while offering a good assortment.
- Generating store shipments required significant resources, time, and manual labor.
Once Simons implemented predictive analytics, they were able to intelligently analyze all the data that influence their demand forecasts such as seasonality, events, promotions, lead times, inventory levels, product cannibalization, and geodemographic diversity.
With this AI-driven analysis, their promotional forecast accuracy increased by 40% and significantly reduced the resources, time, and manual labor costs required to run purchasing, allocation, and replenishment processes.
2. Personalized shopping
AI-driven analytics and machine algorithms are being used to analyze customer data, such as purchase history and browsing behavior, to recommend products that the customer is most likely to be interested in.
AI retail use case: Amazon
Omnichannel retailer, Amazon customizes its homepage for every one of its customers based on AI-driven analytics and data collected on purchasing behavior, preferences, wishlist, and cart entries.
Amazon uses both historical and real-time data to learn about its customers and then uses hyper-personalization marketing campaigns to enhance customer experience and satisfaction levels.
3. Supply chain optimization
AI is also being used to optimize the supply chain, from production to delivery, to reduce costs and improve efficiency.
In fact, AI-powered forecasts in supply-chain management can reduce errors by up to 50%, reducing lost sales and product unavailability by up to 65%, according to Mckinsey & Company.
AI retail use case: Zara
Based on the demand forecasts generated by its AI algorithms, Zara adjusts its production schedules and quantities in its supply chain. By doing so, the retailer ensures it produces enough of its popular products and avoids overproduction of less in-demand items.
4. Inventory management
Retail inventory management is the process of ensuring you have enough inventory to meet customer demand, in all locations as well as online.
But retailers can’t have eyes everywhere, so they use the help of AI-powered technology like camera vision and sensors to help them see what needs to be replenished in real-time.
AI retail use case: Lowes
Home improvement retailer Lowes uses small cameras, mounted on shelves in high-touch departments. Those cameras stream real-time information on shelf-stock levels.
The AI technology detects when a hole appears on a shelf, like in the light bulb section for example. The system sends a real-time notification to the store’s devices so staff can quickly replace the supply on the shelf from the stock room.
5. Customer service
AI-powered chatbots provide 24/7 customer support–essential in today’s fast-paced retail environment.
A recent study also found that customer relationship management (CRM) is one of the top use cases for AI in retail, making up 21.5% of the overall market share in 2022; according to The Precedence.
AI retail use case: Sephora
Beauty product retailer Sephora has implemented extra features for its chatbot.
They are:
-
- Reservation Assistant
- Color Match
The two new bots for Messenger offer enhanced ways for clients to engage with Sephora by streamlining how they access relevant services.
Future of AI in retail
The future of AI in the retail industry will increase as collaboration between retailers and technology companies drive innovation and growth in the industry.
You can expect:
-
- More operations-focused and customer-facing AI applications
- Machine learning to be the fastest-growing segment of AI
- Increasingly digitized supply chain
It will be exciting to see the new use cases and applications that emerge in the retail industry in the future.
AI is transforming retail in the digital age
Shopping has clearly evolved as the retail industry is further shaped by the implementation of artificial intelligence and machine learning.
As the retail industry undergoes a digital transformation, AI and machine learning have impacted and improved the retail market in all areas from hardware, software, and services with applications in:
-
- Forecasting demand
- Hyper-personalization
- Supply chain logistics
- Inventory management
- CRM
AI is changing the retail market, and retailers who embrace AI will be at a significant advantage.
If you’re ready to learn more about how AI and machine learning can improve all aspects of your retailing business, our Team is ready to help, request a Demo
