Retailers deliver conversational AI and analytics directly to users

After years of experimentation, retailers are now embedding AI-powered consumer insights directly into everyday commercial decisions. Analytics firm First Insight, which specializes in predictive consumer feedback, argues the next evolution of retail AI is defined by dialogue, not dashboards.

Following a beta program, First Insight has launched a new AI tool named Ellis for brands and retailers. Designed as a conversational interface, Ellis allows merchandising, planning, and pricing teams to ask direct questions about products, pricing, and demand within the platform, compressing decision cycles to minutes.

McKinsey research indicates that while large retailers collect vast customer data, many struggle to translate insights into action fast enough to impact product development. AI tools that shorten the gap between insight and execution are proving more valuable than traditional reporting systems.

From dashboards to dialogue

First Insight has partnered with retailers like Boden, Family Dollar, and Under Armour to predict demand, price sensitivity, and performance using surveys and predictive modeling, typically delivered via dashboards or reports.

Ellis enables users to query these insights conversationally. Teams can ask, for example, whether a six-item or nine-item assortment will perform better in a specific market, or how removing certain materials might affect appeal. The system provides answers based on the company's existing data models.

Industry analysis suggests this approach addresses a key bottleneck. A Harvard Business Review study of data-driven retailers found insights often lose value when not accessible quickly, particularly during critical phases like line reviews or early concept development.

Predictive insight already in operation

The core techniques behind First Insight are already deployed across retail. Under Armour has used consumer data and predictive modeling to refine assortments and pricing, helping reduce markdown risk and boost full-price sales.

Similarly, Boden has leveraged customer insight to balance trend-led and core items in its assortments. While proprietary system details are confidential, these cases demonstrate how predictive data is being integrated into commercial planning.

Comparable tools are widespread. Walmart, Target, and others invest in analytics and machine learning to understand regional demand, optimize pricing, and test concepts. A Deloitte study on retail AI links predictive consumer insight to improved forecast accuracy and lower inventory risk, especially when integrated early.

Pricing, assortments and competitive dynamics

Ellis is powered by what First Insight calls a predictive retail large language model, trained on consumer response data. It can answer questions on optimal pricing, predicted sales, ideal assortment size, and segment preferences.

This aligns with academic research identifying price optimization and assortment planning as high-value AI applications in retail. A Journal of Retailing study found data-driven pricing models often outperform traditional cost-plus methods, particularly when directly measuring consumer willingness-to-pay.

Competitive benchmarking is another key area. Bain & Company research indicates retailers who compare their products against competitors' are better able to differentiate on value and price. Tools that consolidate such analysis into a single layer are increasingly seen as the ideal.

Making insight more widely accessible

A core claim for First Insight is that Ellis makes consumer insight accessible beyond specialist analytics teams. Natural-language queries allow executives to engage with data directly, without waiting for analysis.

Democratizing analytics is a recurring industry theme. Gartner reports that broadening access to analytics tools increases adoption and ROI, though it cautions that governance is needed to ensure accurate interpretation and robust data sources.

First Insight maintains Ellis retains the methodological rigor of its platform while reducing decision-making friction. According to CEO Greg Petro, the goal is to integrate predictive insight at the exact moment decisions are made.

“For nearly 20 years, First Insight has helped retailers ground pricing, product success, and assortment decisions in real consumer feedback,” a spokesperson said. “Ellis brings that intelligence directly into line reviews, early concept development, and the boardroom, helping teams move faster with confidence.”

A crowded but growing market

First Insight is not alone in this space. Vendors like EDITED, DynamicAction, and RetailNext also offer AI tools for merchandising and pricing. Newer offerings differentiate by emphasizing usability and speed over pure model complexity.

A recent Forrester report noted conversational interfaces are being layered onto established analytics platforms, reflecting user demand for more intuitive data interaction. While these tools can lead to better decisions, their effectiveness depends on data quality and organizational discipline.

First Insight previewed Ellis at the National Retail Federation conference in New York, where AI-driven merchandising and pricing tools were prominent. As retailers navigate volatile demand, inflation, and shifting preferences, the ability to rapidly test scenarios remains crucial.

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