The way we shop has changed dramatically over the past decade, with technology playing a more prominent role than ever before. According to a 2021 report by Hootsuite and We Are Social, the average internet user spends approximately 6 hours and 54 minutes online each day. This includes time spent on a variety of activities such as browsing websites, using social media, watching videos, and online shopping. We've gone from merely buying books and CDs online to purchasing everything from groceries to furniture through our phones. Now, AI is poised to change everything again, from how consumers find products and services to how retailers manage inventory, build relationships with customers, and streamline operations. It's a game-changer that will change the way you shop—and it's already happening!
With the help of AI-powered chatbots or digital humans, retailers are able to offer more personalised shopping experiences to their customers. These chatbots can assist with product recommendations, answer questions about inventory and pricing, and even handle customer service inquiries. It’s more than a customer service solution though - AI can analyse customer data and make recommendations based on a customer's past purchases, browsing history, demographics, location, and other factors.
A great example of this is the gift recommendation engine we built for The Warehouse Group, who wanted to provide customers with a seamless online shopping experience. To categorise the thousands of products listed, we used a Natural Language Processing solution with GPT-3, then built the customer-facing gift finder page on a scalable foundation, using Google Cloud to handle spikes in user traffic. The results showed improved conversion rates, basket size, and session duration. The Warehouse Gift Finder was a success during the Christmas period and was the highest online source of revenue generation. You can read more about the Gift Finder in our case study here.
As interest in incorporating ChatGPT into existing chatbots increases, customers will be able to interact with ChatGPT in a way that resembles a conversation with a knowledgeable salesperson. The conversational nature of ChatGPT results in a more personalised and human approach to recommending gifts and products by gaining further insight into customer interactions. Retaining this conversational context allows the assistants to pick up on the more nuanced details in their questions and requests to provide more relevant responses.
AI is also being used to improve product search and recommendation systems. These systems are designed to help customers find what they're looking for, when they're ready to buy it. This can be extremely helpful for online retailers who have a large number of products, but don't want their customers to have to sift through all of them manually. An AI-powered product search system can use customer data to recommend products they may need based on their browsing or purchase history as well as insights and trends drawn from similar demographics.
This not only improves the customer experience, but also saves retailers time and resources. A customer can use a conversational AI assistant to ask questions about a product, place an order, or track a shipment, without the need for a human customer service representative, freeing them up to handle more complex issues, such as resolving disputes or handling sensitive customer information. Additionally, the use of AI technology in customer service can also reduce errors, provide faster response times, and offer customers a convenient and accessible way to get the information they need, day or night.
The use of AI technology in retail is not limited to just customer service. The technology is also being used to optimise inventory management, reducing the chances of stock shortages and overstocking while also helping retailers make data-driven decisions about which products to stock and promote. For example, sometimes seasonal trends throw off an inventory plan—such as an influx in demand for winter coats if winter arrives earlier than usual, leading to a shift in the typical cycle of demand for winter coats. Retailers may experience an unexpected surge in sales as customers begin purchasing winter coats earlier in the season than they used to. Similarly, there may be changes to the usual demand for summer vacation products due to factors like travel restrictions or changes in consumer behaviour. AI technology helps solve these issues by using machine learning algorithms that can process large amounts of data at once (and even use historical data from past seasons). This allows retailers to make informed decisions and predict upcoming demand to determine which products need restocking based on past trends without having to manually check every single product's sales history first—which would be quite time intensive!
In the future, we can expect to see even more developments and applications of AI in retail. AI may be used to automate more aspects of the retail process, from inventory management to supply chain optimisation. It may also be used to improve the in-store shopping experience by integrating AI into an in-store kiosk, providing personalised recommendations and assistance to customers as they shop. It’s important for retailers and customers alike to stay informed about these developments. By understanding the role of AI in retail, we can better prepare for the future and take advantage of the opportunities that AI offers.