Today, retailers are already experiencing the many benefits of using Artificial Intelligence (AI), which will only continue gaining more importance as the industry continues to innovate. As AI becomes more widely accepted, it’s also becoming more widely implemented.
Check out these use cases to see how AI in retail is changing the industry for the better.
Use Cases of AI in the Retail Industry
Retail is the epitome of how brick-and-mortar business engages with AI. Retailers use this technology to allow their customers to experience more personalized care and achieve greater interaction with their stores. AI is also changing how retailers conduct their business in general—tracking inventory and resource consumption, forecasting sales, showcasing new products, and offering them to potential customers.
Retailers use the technology to help retailers make better decisions, improve their business scale and profitability, and automate tedious or repetitive tasks.
Let’s take a look at some AI use cases in retail.
1. Chatbots for Better Customer Service
The prospect of employing chatbots for enhanced customer service is among the most popular developments in eCommerce. Offering this feature allows an online retail business to address inquiries from prospective buyers with a more personalized experience. Chatbots have been proving to be helpful in customer service jobs such as answering FAQs.
Integrating chatbots into a retail website or application is an excellent decision for businesses looking for ways to assist customers and increase their satisfaction. These chatbots will be able to handle most of your customer queries and, in the process, help improve retention and engagement. In addition, personalization, virtual assistants, and AI all present opportunities to overcome the slow performance of contact centers.
2. Cashier-Free Stores
AI technologies taking over traditional jobs is a huge trend. Retailers like IBM, Walmart, TJX, and Amazon are testing the tech in a new generation of cashier-free stores. This new approach to retail brings automation, efficiency, and convenience to customers and store owners—and no more waiting in lines. Cashier-free stores using AI to predict the need for help is the next big step in automation.
3. In-Store Assistance
Retailers have been investing in technologies that assist customers and staff in shopping. Kroger Edge has installed smart shelf tags that eliminate the need for paper price tags in their stores. This technology allows advertisers to provide visual ads, nutrition information, and promotions on the device displays. Lowebot, an autonomous robotic store helper device introduced in Lowe’s stores, is designed to speak in different languages to help customers easily find what they are looking for in a store. The robot’s inventory management capabilities have also expanded thanks to real-time monitoring.
Physical retailers usually aim to provide an extra hand during shopping. In-store retail robots could be the next big thing: a new automated system that helps customers find what they’re looking for while shopping.
4. Price Adjustment in Retail Stores
AI can also make price adjustments more accurate and human-free and, according to a study by Deloitte, can even help manage prices during uncertain times. ML can enable autonomous and efficient AI-based price adjustment in retail stores. The result? Consumers enjoy fairer prices, more intelligent product positioning, and a more favorable shopping experience.
Algorithm-based price adjustment happens when, after a product is purchased in a store, the price automatically changes following a certain pattern set by the owners. AI pricing can make a massive difference to the in-store experience. However, any price adjustment methodology should focus on what a customer is willing to buy at a given time, requiring the proper analytical resources to make it work right.
5. AI-Based Price Forecasting
As the pace of changes in the global economy continues to accelerate, disruptions in financial services run the risk of rendering current price forecasting models and methods largely irrelevant if a company wants to serve its customers more effectively—meaning the way they need and want to be served—it needs to figure out how it will continue to adapt to new, changing demands by adopting disruptive business models and new technologies.
Price forecasting is determining which products could be bought at a specific price point to meet the demand for those products. An AI-based price forecasting model is helpful if you want to know how your prices will change. With AI-based software, retailers can make better price decisions that generate more revenue.
6. Supply Chain Management and Logistics
The impact of AI on the supply chain, logistics, and the trucking industry is huge and becoming more well-known all the time. This space is full of struggling companies and exciting new technologies trying to solve some of the industry’s most significant pain points. Next-gen AI will break down the barriers to supply chain management and logistics by collating requirements, adding coverage, and tracking shipments.
Staying up-to-date on the latest innovations and continuing to rise to the challenge, innovative companies are combining cloud-based AI technology with increased communication (social media) to manage their supply chain better. Unfortunately, the increased communication has created a new challenge—data overload. Collecting and filtering through the data is vital for the effective operation of the business.
7. ML-Based Product Categorization
Machines are becoming smarter, retailers are turning to data, and online shoppers are demanding a personalized experience. All of this means that making sense of what’s going on at your store is trickier now than ever before. Machine learning using neural networks is widely used in the retail industry to automate product categorization tasks, such as search.
Item categorization has been an issue for retailers for decades. They can either do it manually or through experience learning from the data from their customers. Sales and marketing expectations have risen, and we see ML as a replacement for this manual process. Using ML, retailers can improve product categorization and reduce costs.
8. Customer Behavior Prediction
With increasing pressure on retailers to deliver relevant customer experiences, companies are using AI to predict how prospects will engage and personalize the shopping experience—ultimately transforming it and improving retention.
Furthermore, AI will help retailers identify customer needs more holistically. The ultimate objective is to provide companies with a comprehensive marketing-stage picture of prospects and a complete forecast of their future needs and actions. Other predicted behaviors include timing, loyalty, and sales conversion.
Machine learning and AI are no longer just futuristic buzzwords. Now, the technology is finding its way into several large industries, including retail, where it promises to deliver both cost-efficiency and improved customer experience. These two whales carry the shopping industry on their backs.