[AI]Retailers bring conversational AI and analytics closer to the user

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After years of experimentation with artificial intelligence, retailers are striving to embed consumer insight directly into everyday commercial decisions. First Insight, a US-based analytics company specialising in predictive consumer feedback, argues that the next phase of retail AI should be epitomised by dialogue, not dashboards.

Following a three-month beta programme, First Insight has made its new AI tool, Ellis, available to brands and retailers. Ellis is designed as a conversational interface that allows merchandising, pricing and planning teams to ask questions about products, pricing, and demand in the First Insight platform. The company says its approach is intended to compress decision times into minutes.

Research by

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that while most large retailers now collect volumes of customer data, some can’t translate insights into action quickly enough to influence product development decisions. It notes AI tools which shorten the distance between insight and execution are more likely to deliver measurable commercial value than reporting systems.

From dashboards to dialogue

First Insight has worked with retailers including Boden, Family Dollar, and Under Armour to predict consumer demand, price sensitivity, and performance using survey feedback and predictive modelling. Such insights are usually delivered on a dashboard or in a report.

Ellis lets users query insights conversationally. For example, teams can ask whether a six-item or nine-item assortment is likely to perform better in a specific market, or how removing certain materials might affect appeal. First Insight says the system returns answers grounded in its existing data models.

Industry evidence suggests that this method could help with a bottleneck in retail decision-making. A

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found insight often loses value when it cannot be accessed quickly, particularly during phases like line review or early concept development.

Predictive insight already in operation

The underlying techniques used by First Insight are deployed already across the retail sector. Under Armour has

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using consumer data and predictive modelling to refine product assortments and pricing strategies, stating the technology helps it reduce markdown risk and improve full-price selling.

Similarly, fashion retailer Boden has discussed the role of customer insight in guiding assortment decisions, particularly in balancing trend-led items with core items. While these companies do not disclose the details of their proprietary systems, such cases can show how predictive consumer data can be embedded into commercial planning.

Comparable tools are also in use elsewhere in the industry. Retailers including Walmart and Target have invested in analytics and machine learning to understand regional demand patterns, optimise pricing, and test new concepts. According to a

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on AI in retail, companies using predictive consumer insight report improved forecast accuracy and lower inventory risk, particularly when analytics are integrated early.

Pricing, assortments and competitive dynamics

Ellis is powered by what First Insight describes as a predictive retail large language model, one that’s trained on consumer response data. The company says this lets the system answer questions about optimal pricing, predicted sales rates, ideal assortment size, and likely segment preferences.

This focus aligns with academic research showing that price optimisation and assortment planning are among the highest-value AI use cases in retail. A study published in the

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found that data-driven pricing models can outperform traditional cost-plus approaches, particularly when consumer willingness-to-pay is measured directly.

Competitive benchmarking is another area where retailers can use analytics.

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indicates retailers able to compare their products with competitors’ are better positioned to differentiate on value as well as price. Tools that consolidate such comparisons into a single analytical layer can be considered the ideal, therefore.

Making insight more widely accessible

One of First Insight’s core claims is that Ellis makes consumer insight accessible outside of specialist analytics teams. Natural-language queries, the company argues, lets senior executives down engage with data with no waiting for analysis.

Democratisation of analytics is a recurring theme in a great deal of industry research.

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reports organisations which broaden access to analytics are more likely to see tool adoption and ROI. However, it cautions that systems should be governed to ensure outputs are interpreted correctly and stem from robust data.

First Insight maintains that Ellis retains the methodological rigour of its existing platform, while reducing friction at the point of decision. According to Greg Petro, the company’s chief executive, the goal is to bring predictive insight into the moment when decisions are actually made.

“For nearly 20 years, First Insight has helped retailers predict pricing, product success and assortment decisions by grounding them in real consumer feedback,” a company spokesperson said. “Ellis brings that intelligence directly into line review, early concept development and the boardroom, helping teams move faster without sacrificing confidence.”

A crowded but growing market

First Insight is not alone to target the space. Vendors such as EDITED, DynamicAction, and RetailNext offer AI tools aimed at merchandising and pricing. What differentiates newer offerings is the emphasis on usability and speed rather than model complexity.

A recent

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noted that conversational interfaces are being layered on top of established analytics platforms, reflecting a demand from users for more intuitive interaction with data. Such tools lead to better decisions, although are dependent on data quality and organisational discipline.

First Insight previewed Ellis at this year’s National Retail Federation conference in New York, where AI-driven merchandising and pricing tools featured prominently. As retailers face volatile demand, inflation, and changing consumer preferences, the ability to test scenarios remains valuable.

(Image source: “2008 first insight” by palmasco is licensed under CC BY-NC-ND 2.0.)

 

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