Retailers are increasingly using artificial intelligence to make smarter, faster decisions. After years of testing different AI tools, many now want to put consumer insights directly into everyday business choices. The goal is to move beyond just dashboards and reports to more interactive, real-time conversations that help teams act quickly.

Introducing Conversational AI in Retail

First Insight, a US-based analytics company, has developed a new AI tool called Ellis. After a three-month beta test, it is now available for brands and retailers to try. Ellis is designed as a chat-based interface that lets teams ask questions about products, pricing, and customer demand right within the platform. Instead of scrolling through reports, teams can get immediate answers by talking to the AI, which aims to cut decision times down to minutes.

This approach emphasizes dialogue over static dashboards. Retailers can ask things like which product assortment might perform better in a certain market or how changing materials could impact customer appeal. The answers are based on existing data models, helping teams make informed choices faster. The idea is to bridge the gap between data collection and action, making insights more accessible and useful during critical moments like product line reviews or early development stages.

The Power of Predictive Consumer Insights

Many retailers already use predictive analytics to understand customer behavior. Companies like Under Armour and Boden have shared how they use consumer data to refine their product assortments and pricing strategies. Under Armour uses predictive models to reduce markdown risks and improve full-price sales, while Boden balances trend items with core products based on customer feedback. These examples show how consumer insights are already embedded in some retail planning, even if the systems are proprietary.

Other big retailers like Walmart and Target also invest heavily in analytics and machine learning. They analyze regional demand patterns, test new concepts, and optimize pricing strategies. Such tools help them understand what customers want in different areas, making their decisions more data-driven. Industry reports suggest that these predictive tools can make a real difference in improving sales and reducing waste, especially when insights are accessible quickly and used in real time.

Overall, the move toward conversational AI like Ellis represents a natural evolution. As these tools become more common, retailers will be able to act on insights faster than ever before. This can lead to better product offerings, smarter pricing, and more responsive marketing—all driven by real-time conversations with AI that understands and responds to business questions with confidence.

Inspired by

• https://www.artificialintelligence-news.com/news/retailers-bring-conversational-ai-and-analytics-closer-to-the-user/

Sources

• mckinsey.com
• hbr.org