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Today, retailers are already experiencing the many benefits of using artificial intelligence (AI), which will only grow in importance as the industry continues to innovate. As AI becomes more widely accepted, so does its implementation.
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Use cases for AI in retail
Retail is the epitome of how brick-and-mortar businesses interact with AI. Retailers use this technology to personalize their customer experience and enable better interaction with their stores. AI is also changing the overall way retailers do business – tracking inventory and resource consumption, forecasting sales, showcasing new products and making them available to potential customers.
1. Chatbots for better customer service
The prospect of using chatbots to enhance customer service is one of the most popular developments in e-commerce. Providing this feature allows online retail businesses to resolve potential buyers' queries with a more personalized experience. Chatbots have proven to be helpful in customer service jobs like answering FAQs.
Integrating a chatbot into a retail website or app is an excellent decision for businesses that seek help with customers and ways to improve customer satisfaction. These chatbots will be able to handle most of your customer queries and help improve retention and engagement in the process. In addition, personalization, virtual assistants, and artificial intelligence all offer opportunities to overcome poor contact center performance.
2. No one cashier
It is a big trend for artificial intelligence technology to take over traditional jobs. Retailers such as IBM, Walmart, TJX and Amazon are testing the technology in a new generation of cashless stores. This new approach to retail brings automation, efficiency and convenience to customers and store owners – and no more waiting in line. Using AI to predict cashless stores that help demand is the next big step in automation.
3. In-store assistance
Retailers have been investing in technology that helps customers and employees shop. The Kroger Edge installed smart shelf labels, eliminating the need for paper price tags in stores.
The technology allows advertisers to provide visual advertising, nutritional information, and promotions on device displays. Lowebot is an autonomous robotic store assistant device launched by Lowe's stores designed to use different languages to help customers easily find the items they are looking for in the store. Thanks to real-time monitoring, the robot's inventory management capabilities have also been expanded.
Brick-and-mortar retailers often aim to provide extra help during the shopping process. 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. Retail store price adjustment
AI can also make price adjustments more accurate and unhuman, and according to a Deloitte study, AI can even help manage prices in uncertain times. ML enables autonomous, AI-based and efficient price adjustments in retail stores. Outcome? Consumers enjoy fairer prices, smarter product positioning and a better shopping experience.
An algorithm-based price adjustment occurs when the price changes automatically after the product is purchased in the store according to a specific pattern set by the owner. AI pricing can have a huge impact on the in-store experience. However, any price adjustment method should focus on what customers are willing to buy at a given time, requiring proper analytical resources to make it work.
5. AI-based price forecasting
As the pace of change in the global economy continues to accelerate, disruptions in financial services can cause current price forecasting models and methods to become largely irrelevant if a company wants to serve its customers more efficiently – the way they need and wants services – it needs to figure out how to continue to adapt to new, changing needs by adopting disruptive business models and new technologies.
Price forecasting is determining which products can be purchased at a particular price point to meet demand for those products. If you're wondering how prices will change, AI-based price prediction models can be helpful. With AI-based software, retailers can make better price decisions that can generate more revenue.
6. Supply chain management and logistics
The impact of AI on the supply chain, logistics and trucking industries is huge and increasingly known. This space is full of struggling companies and exciting new technologies that are trying to solve some of the industry's most important pain points. The next generation of AI will break down barriers to supply chain management and logistics by collating demand, increasing coverage, and tracking shipments.
To keep up with the latest innovations and continue to rise to the challenge, innovative companies are combining cloud-based AI technology with increased communication (social media) to better manage their supply chains. Unfortunately, the increase in communication brings with it a new challenge – data overload. Collecting and filtering data is essential for the effective operation of your business.
7. Machine learning-based product classification
Machines are getting smarter, retailers are turning to data, and online shoppers need personalized experiences. All of this means it's now harder than ever to make sense of what's going on in your store. Machine learning using neural networks is widely used in retail to automate product classification tasks, such as search.
Commodity assortment has been a problem for retailers for decades. They can be done manually or by learning from the customer's data. Sales and marketing expectations have risen, and we see ML as an alternative to this manual process. Using ML, retailers can improve product assortment and reduce costs.
8. Customer Behavior Prediction
As retailers grow under increasing pressure to deliver relevant customer experiences, companies are using AI to predict how potential customers will engage and personalize the shopping experience — ultimately changing it and improving retention.
In addition, AI will help retailers identify customer needs more comprehensively. The ultimate goal is to provide the company with a comprehensive picture of the marketing phase and a complete forecast of its future needs and actions. Other predictive behaviors include timing, loyalty, and sales conversion.
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epilogue
Machine learning and artificial intelligence are no longer just futuristic buzzwords. Now, the technology is making its way into several large industries, including retail, where it promises to provide cost-effectiveness and an improved customer experience.
Reference:
《Using AI In The Retail Industry: Use Cases In 2023》
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