When thinking about which industries will benefit the most from adopting generative AI solutions, retail might not be the first sector that comes to mind.
However, a new report from Salesforce shows that 17% of buyers have already used generative AI for shopping inspiration!
Specifically, users are turning to large language models (LLMs), such as ChatGPT, to explore gadget ideas, get fashion advice, and develop personalized nutrition plans. And it's only been nine months since generative AI became mainstream!
In this article, we will explore how brick-and-mortar retailers can leverage this emerging technology to automate tasks, enhance the customer experience, and boost profits by optimizing supply chains and preventing fraud.
Exploring the Transformative Potential of Generative AI in Retail
Generative AI is a branch of artificial intelligence that can create new and unique content, such as text, visuals, audio, and video, based on the data it has been trained on.
Unlike most AI-based solutions that are designed for specific tasks (like recognizing characters in images and PDF files or detecting anomalous payment transactions), generative AI models can perform multiple tasks and produce various outputs as long as they align with the training datasets.
However, the noticeable differences between these two types of AI do not mean that they cannot coexist. On the contrary, these technologies complement each other’s strengths and weaknesses, empowering retail brands to make more informed business decisions and revamp their digital strategies.
Broadly, the use of generative AI in retail can be categorized into:
1. Synthetic Data Generation. Traditional AI systems rely heavily on large datasets for training. However, collecting this data can be a time-consuming and costly process that also raises privacy concerns. This is where generative AI comes in handy. Thanks to its versatility in generating different types of data, generative AI can help synthesize information for training traditional AI models. Moreover, it addresses data privacy and security issues, allowing retailers to optimize AI model performance in a risk-free environment.
2. Advanced Analytics. Traditional business intelligence (BI) systems are adept at processing and analyzing structured data, presenting insights in readable formats. AI-infused BI systems boast the ability to analyze structured, semi-structured, and unstructured data coming from various internal and external IT systems. Generative AI solutions for retail imitate the functionality of AI-powered data analytics tools. These solutions provide a user-friendly interface for employees without technical expertise and access to different types of data from various sources, such as customer reviews and social media mentions. Additionally, they can produce data similar to the information you already have to amplify your analytics efforts and simulate realistic scenarios reflecting current market trends and changes in customer behavior.
3. Smarter content creation. Generative AI’s ability to create content is unparalleled. That’s why the world’s leading eCommerce companies turn to generative AI to write SEO-friendly blog posts, landing pages, and product descriptions. In brick-and-mortar retail, the content-related applications of generative AI might not have such a transformative impact. However, physical stores can still leverage the technology to craft contextually relevant content, from flyers and personalized marketing messages in shopping apps to product videos running on interactive displays.
Top 5 Generative AI Use Cases in Retail
1. Providing personalized shopping guidance to customers. To personalize customer experience in brick-and-mortar stores, businesses can use foundation AI models to create digital shopping assistants trained on their corporate data. Living inside your brand app, such assistants may help shoppers find products in a store, arrange related products in bundles, create shopping lists, offer discounts based on past purchases and browsing data, and more. You can also harness the retail generative AI technology to develop dynamic, adaptive content for digital signage and kiosks. Some early examples of retail brands tapping into generative AI-driven personalization include Carrefour, a multinational retail and wholesale chain operating almost 14 thousand stores in 30 countries. Earlier this year, the company launched Hopla, a ChatGPT-powered chatbot that provides personalized shopping tips and even recipes to Carrefour customers taking into account their budget, past purchases, and dietary restrictions. Such chatbots can be a welcome addition to checkout-free shopping solutions, offering seamless assistance to tech-savvy customers.
2. Enhancing display design in physical stores. With generative AI models, retailers can design more appealing, efficient, and effective store layouts and product displays, boosting customer experience and sales. As we mentioned in the previous section, artificial intelligence helps boil miscellaneous customer data down to meaningful insights, establishing correlations between store layouts and buyer behavior. An example of this could be heat maps highlighting high-traffic areas in your store, which can be used for optimal product placement. Forward-thinking retailers may also utilize AI to craft displays that cater to specific customer segments or individual preferences and stimulate customer interactions with the designs using interactive screens, augmented reality (AR) apps, and proximity marketing solutions relying on Bluetooth technology. While some of these ideas might seem a sci-fi concept at first glance, sometimes the generative AI’s advice in retail may be as simple as putting up a point-of-purchase (POP) display, which alone could increase sales by up to 32%.
3. Assisting with inventory and supply chain management. Ever since the pandemic struck, the retail sector has been dealing with daunting supply chain challenges. These included closing borders and subsequent shipping delays, disrupted production caused by stringent lockdown rules in countries like China, and persistent overstocks and stockouts resulting from the massive changes in buying behavior. Tech-savvy businesses like H&M and Zara have long tapped into retail software development services to solve these problems with the help of integrated data ecosystems infused with AI capabilities. Zara, for instance, tracks all purchases using stock-keeping unit (SKU) numbers, analyzes sales trends for each of their physical shops, and adjusts manufacturing volumes based on actual demand. Similarly, H&M uses artificial intelligence to monitor sales in all of its 4,700 locations, anticipate sales volumes, and timely restock items. By using generative AI in retail supply chains, it is also possible to forecast demand, maintain optimum inventory levels, and optimize logistics operations. The question is, how does generative artificial intelligence compare to traditional AI — and what benefits does it bring to the table? Unlike traditional retail AI solutions, which rely on historical data to detect patterns in new information and deliver intelligent recommendations, generative AI retail systems can produce synthetic training data. Using this data, the smart algorithms simulate market conditions and scenarios and stress-test supply chain models. Such capabilities make generative AI a viable option for retailers lacking substantial amounts of sales and logistics data, empowering companies to take a more granular approach to inventory planning and optimize supply chain operations with complex variables.
4. Developing Competitive Pricing Strategies. Brick-and-mortar retailers can use generative AI to create dynamic pricing strategies. This involves collecting data on customer demographics, behavior, and purchase history, as well as competitors' pricing. Retail generative AI systems can interpret real-time data and make instant pricing decisions based on demand. They can also help create personalized pricing strategies based on customers' purchase histories.
5. Eliminating fraud. Generative AI can be instrumental in detecting and preventing fraudulent behavior in brick-and-mortar retail stores through various means. For instance, you can task generative AI with creating realistic synthetic data to train machine learning models when actual data is scarce or sensitive. This data can be used for teaching computer vision-powered security systems to spot shoplifting and sweethearting events; for more information about these AI applications in retail, check out our recent blog post about the supermarkets of the future. Generative AI can also create authentic transaction data that aids in detecting fraudulent activities, such as phony returns and purchases. This not only increases customer trust but also improves your overall financial performance. There is even an option to combine blockchain-based smart contracts with generative AI retail solutions to detect unauthorized sellers and counterfeit products in traditional retail supply chains! Your company could use blockchain smart contracts that automatically execute when certain conditions are met, while generative AI will analyze blockchain data in real time, identifying patterns and trends that human operators might miss. Some practical use cases for this combination include verifying products using unique QR codes or serial numbers and tapping into generative AI to predict fraudulent patterns associated with the generation of these codes. Furthermore, it’s technically possible to implement AI algorithms to analyze vendor information and transactions on the blockchain to identify unauthorized or fake sellers.
Although retail generative AI is still in its early stages, as a visionary leader, you should consider adding the technology to your digital toolbox ASAP.
With customers becoming more reliant on their smartphones and apps while shopping in physical stores, you could leverage generative AI to personalize your message, fine-tune your upselling and cross-selling strategies, and gain deeper insights into consumer behavior.
However, there are certain obstacles your organization might need to overcome when implementing any type of AI in business.
To help you sail through your AI pilot project, the ITRex team has written several practical guides:
An explanation of what an AI proof of concept (POC) is and why it is essential for your project’s success
A rundown of AI implementation challenges
The AI in Business handbook that provides step-by-step instructions for implementing artificial intelligence in your organization
Conclusion
As generative AI continues to revolutionize the retail industry, it offers immense potential to enhance customer experiences, streamline operations, and boost profitability. Whether you’re looking to personalize shopping, optimize your inventory, or develop competitive pricing strategies, adopting AI can give your business a significant edge.
At JustSoftLab, we are always ready to help you navigate the complexities of AI implementation. With our expertise in generative AI solutions, we can support you in integrating this cutting-edge technology into your retail operations, ensuring that you stay ahead of the competition. Feel free to reach out to us—we’re here to help you transform your business and achieve success in the ever-evolving retail landscape.