If you are considering which industries will benefit the most from adopting generative AI solutions, retail may not be the first sector that comes to mind. However, according to a new report from Salesforce, 17% of buyers have already used generative AI for shopping inspiration. Users rely on large language models (LLMs) like ChatGPT to research gadget ideas, get fashion inspirations, and develop personal nutrition plans. And all of this has happened within just 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 customer experience, and improve profit margins by optimizing supply chains and eliminating fraud.
Generative AI is a subset of artificial intelligence that has the capability to create new and unique content, such as text, visuals, audio, and video, using the information it has been trained on. Unlike most AI-based solutions that are designed for specific tasks, generative AI models can perform multiple tasks and produce various outputs, as long as they are similar to the training datasets. However, the differences between these two types of AI do not mean that they cannot coexist. In fact, they complement each other and help address each other’s shortcomings, empowering retail brands to make better-informed business decisions and transform their digital strategies.
Generative AI can be used in retail for various purposes:
1. Synthetic data generation: Traditional AI systems rely heavily on large datasets for training, which can be time-consuming, costly, and raise privacy concerns. Generative AI can assist in synthesizing information for traditional AI model training, thanks to its versatility in generating different types of data. It also addresses the obstacles related to data privacy and security, allowing retailers to optimize AI model performance in a risk-free way.
2. Advanced analytics: Traditional business intelligence (BI) systems excel at processing and analyzing structured data. AI-infused BI systems, on the other hand, can analyze structured, semi-structured, and unstructured data from various sources, such as customer reviews and social media mentions. Generative AI solutions for retail mimic the functionality of AI-powered data analytics tools, providing a user-friendly interface for employees without technical expertise. They can produce data similar to the existing information to amplify analytics efforts and simulate realistic scenarios reflecting current market trends and changes in customer behavior.
3. Smarter content creation: Generative AI has unparalleled content creation capabilities. Leading eCommerce companies use generative AI to write SEO-friendly blog posts, landing pages, and product descriptions. In brick-and-mortar retail, generative AI can be utilized to craft contextually relevant content, from flyers and personalized marketing messages in shopping apps to product videos running on interactive displays.
Now let’s explore the top five use cases of generative AI in retail:
1. Personalized shopping guidance: Retail businesses can use foundation AI models to create digital shopping assistants trained on their corporate data. These assistants can help shoppers find products, arrange related products in bundles, create shopping lists, offer discounts based on past purchases and browsing data, and more. Generative AI technology can also be used to develop dynamic, adaptive content for digital signage and kiosks, enhancing the overall shopping experience.
2. Enhanced display design: Generative AI models can help retailers design more appealing and effective store layouts and product displays. By analyzing customer data, AI can establish correlations between store layouts and buyer behavior. Retailers can 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.
3. Inventory and supply chain management: Generative AI can assist in forecasting demand, maintaining optimum inventory levels, and optimizing logistics operations. It can simulate market conditions and stress-test supply chain models, enabling retailers to take a more granular approach to inventory planning and optimize supply chain operations with complex variables.
4. Competitive pricing strategies: Generative AI can help retailers develop dynamic pricing strategies by analyzing customer demographics, behavior, and purchasing history. It can also consider factors like seasons, holidays, and recurring events. By interpreting real-time information, generative AI can make instant pricing decisions based on actual demand. It can also help develop personalized pricing strategies driven by a customer’s buying history.
5. Fraud detection and prevention: Generative AI can be instrumental in detecting and preventing fraudulent behavior in brick-and-mortar retail stores. It can create 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 fraudulent activities. Generative AI can also create authentic transaction data that aids in detecting fraudulent activities like phony returns and purchases. Combining generative AI with blockchain-based smart contracts can further enhance fraud detection in traditional retail supply chains.
By leveraging generative AI in these ways, brick-and-mortar retailers can automate tasks, enhance customer experience, optimize supply chains, develop competitive pricing strategies, and eliminate fraud.
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