AI gives retailers an opportunity to use data and act holistically, taking into account all five Ps (Product, Placement, Price, Promotion & People) sometimes in concert.

Retailers today have a once-in-a-generation opportunity. By embedding Generative AI into the enterprise digital core, they can transform their ability to:

• Predict inventory demand patterns.

• Streamline online shopping experience.

• Instantaneous customer feedback analysis.

• Personalize Shopping Experience.

• Automate routine customer inquiries.

Product Design

Generative AI can create product designs based on the analysis of current market trends and customer interactions, consumer preferences, and historic sales data. The AI model can generate multiple variations, allowing companies to shortlist the most appealing options. For instance, creating designs for clothing, furniture, or electronics can be an option.

Or personalizing the display options according to customer choice is another option.

Personalization

AI can generate personalized customer experiences through the marketing content for individual customers, such as emails or ads. These are produced on the basis of the customer data such as past purchasing behavior and preferences. AI can predict what kind of promotional content will most appeal to each customer, increasing the effectiveness of marketing campaigns.

Using generative models, AI can suggest new or alternative products to customers that they might be interested in, based on their buying history and preferences. It can also anticipate their future needs and preferences, thereby improving the shopping experience.

Customer Experience

Retailers can use AI to create descriptions for their products, promotional content for social media, blog posts, and other content that improves SEO and drives customer engagement.

Generative AI can power conversational virtual assistants that help customers in their shopping journey, generating responses to their queries and guiding them through the purchasing process.

The use of generative AI and contact center such as conversational AI, Large language models (LLMs), and chatbots can automate and increase the efficiency of human customer service representatives.

Supply Chain Optimization

AI can make a supply chain management system more effective by suggesting alternate shipping routes based on updated weather and traffic, and it can help identify and suggest contingencies around other risks, such as disruptions caused by natural disasters or geopolitical tensions.

Demand Forecasting - One of the most common use cases of AI analytics for supply chains is in manipulating and orchestrating demand and supply. For example, such analytics can manipulate demand by prescribing promotions, markdowns, and targeted offers in ways that also protect profit margins, all reflected in an AI-powered forecast. AI analytics could manipulate supply by intelligently rebalancing units across warehouses, distribution centers, and stores, based on that new AI-powered forecast.

Merchandising & Pricing

In a merchandising system, AI-based analytics can help make the difference between selling products at full price versus having to discount excess inventory, while ensuring that retailers with physical stores can compete on availability on a more equal footing with digital-only retailers. AI is the way to fight the infinite store shelf so you carry only the stuff you’re absolutely sure your demographic is going to crave.

AI helps retailers is by giving them a more thorough understanding of demand transference—the propensity of customers to accept a different product if the one they’re intending to buy isn’t available, or the likelihood that they’ll make a purchase on top of their original intent. Organizations can use this information with the planning of inventories, distribution channels, and procurement.

Price is another element where retailers can use AI software, for example, by detecting the latest changes in shopper buying patterns and suggest prices that will maximize profits. Such analyses consider price history, but they also factor in the latest data on inventory levels, supplier costs, offers from competitors, and other variables.