Fibre2Fashion Interviews Greg Petro
Ellis Brings Real Customer Insight Into Decisions Before Investments
First Insight is a global retail platform that uses native AI to convert real-time consumer feedback into actionable insights for over 600 brands and retailers, including Under Armour, Woolworths and Family Dollar. Its Voice of the Customer Platform assigns a Value Score to each product to predict demand and profitability, helping retailers make confident design, pricing and inventory decisions with measurable impact.
In conversation with Fibre2Fashion, CEO Greg Petro spoke about First Insight’s new AI growth tool, Ellis, now available to fashion brands and retailers and marking a shift in how go-to-market decisions are made in fashion and retail. He explained that Ellis replaces months of manual analysis with conversational AI that embeds real-time, consumer-validated insight directly into everyday workflows, helping teams move faster, price smarter, reduce waste and stay aligned with evolving demand.
What makes Ellis different from traditional analytics dashboards or BI tools used by fashion retailers today?
Ellis brings real customer feedback into the decision process before investments are made. During line reviews, assortment planning, pricing discussions or early concept development, teams can ask Ellis direct, natural-language questions and receive clear, actionable answers in minutes. Instead of navigating multiple tools or waiting on analysis, retailers get guidance instantly. By grounding decisions in how consumers are likely to respond rather than how products performed in the past, retailers can move faster, reduce risk, and stay aligned with evolving demand.Simply stated, traditional dashboards and BI tools used by retailers today rely on historical internal data such as sales performance, inventory levels, and past customer behaviour. This approach is like driving forward while looking only in the rearview mirror. Ellis, by contrast, is forward looking.
Why was a conversational AI approach critical to solving go to market decision delays in retail?
Conversational AI removes that lag between the first question and the final decision. Speed is the key. Insights are scattered across dashboards, files and internal systems that often require time and a data team to sort through and interpret. Doing so can take weeks or months, and by the time answers are surfaced, it may be too late. Ellis, specifically, addresses this challenge by embedding conversational AI directly into the workflows of merchandising, pricing and planning teams. Instead of waiting for reports or interpreting data in silos, teams can simply ask direct questions about products, prices, or demand and receive immediate, data-driven answers. This shared, real-time access to consumer-tested insight turns meetings from debate into alignment and reduces the go-to-market timeline by weeks.
How does Ellis help fashion brands determine optimal pricing while protecting margins across different markets?
Ellis helps brands price products based on what consumers are going to pay for products before they actually hit the shelves. Rather than relying on historical sales data, brands can understand what consumers in different regions are going to pay while there is still time to adjust. What is different is accuracy, and that difference matters.Value perception shifts by region, channel, and audience, and Ellis allows teams to model pricing scenarios in real time, see where demand drops off, and understand where margins are protected, so pricing decisions are proactive rather than corrected later through markdowns.
How does Ellis support cross functional teams from design and merchandising to finance and planning?
Ellis supports cross-functional teams by giving everyone, from design and merchandising to planning and finance, access to the same consumer information at the moment decisions are made. Instead of waiting for reports or routing questions through analysts, each team can ask questions and get clear, actionable answers right away. It creates a single, shared view of the consumer. This shared, conversational and collaborative approach helps teams test new concepts, refine assortments and model pricing scenarios. Designers can test new ideas; merchants can refine assortments and finance teams can model pricing and margin scenarios. And because everyone is working from the same ‘version of information,’ Ellis removes the back-and-forth that often slows decisions or creates misalignment.
How does AI driven competitive insight change how retailers respond to rivals like Amazon, Walmart, or fast fashion players?
AI-driven competitive insight shifts retailers from reacting after the fact to being able to ‘anticipate’ in real time. Instead of waiting weeks to understand why a competitor gained share or adjusted pricing, retailers can see how their products, prices and assortments stack up as consumer preferences shift.That visibility lets retailers test products and pricing before they commit. If AI shows that shoppers are responding to a specific price point, feature or assortment strategy from a fast fashion player or marketplace, teams can immediately explore their own options, whether that means adjusting pricing, refining assortments or repositioning products for different markets.
How can predictive consumer intelligence help reduce overproduction and waste in fashion and retail?
This anticipatory consumer intelligence helps fashion and retail brands understand what customers will want, at what price, and in what quantities early in the process, enabling them to avoid producing products that are unlikely to sell. Instead of relying on past seasons or assumptions, brands gather real time consumer feedback during concept testing and assortment planning. That insight allows them to narrow assortments, fine-tune quantities and validate pricing before committing to large production runs. This way, they can shift from reactive to “anticipatory” decision-making, so they can align production with actual demand, operate more sustainably and create supply chains that are both efficient and responsible.
What adoption challenges do legacy retail fashion brands face when integrating AI based decision platforms?
The biggest challenge for legacy fashion retailers is less about the technology and more about how decisions have historically been made. Most organisations are built on siloed systems with separate tools for design, merchandising, planning and finance, which makes it difficult to integrate AI platforms designed to work across functions. Data quality is another major obstacle. While established retailers have years of historical data, it is often fragmented, inconsistent or locked in legacy systems that were not designed for AI. Without clean, centralised and accessible data, even the most advanced AI tools struggle to deliver meaningful insight.
How do you envision AI transforming retail product development and merchandising over the next five years?
Over the next five years, AI will bring consumer insight into the product lifecycle. Instead of validating decisions after products launch, brands will use AI to test new concepts, designs and assortments earlier and more efficiently, when changes are cheaper and faster to make. Merchandising will also become more dynamic as AI will continuously evaluate performance, preferences and market shifts to help brands adjust assortment and pricing at a local and segment level rather than relying on one-size-fits-all plans. As these tools become more integrated into daily workflows, collaboration will improve and teams will move faster, waste less and bring products to market with greater confidence.
As consumer behaviour becomes more volatile, how do you see AI reshaping long term planning and forecasting models in mass retail?
AI is reshaping long-term planning by turning it into a continuous, adaptive process instead of a fixed, once-or-twice-a-year exercise. In a volatile consumer environment, AI enables planning teams to move from reacting after the season to making smarter, forward-looking decisions while there is still time to act.Rather than relying primarily on historical performance, AI blends real-time insight with past data to surface patterns and shifts as they are happening. What makes this powerful is that AI does not just pull or compile data, but it provides recommendations. It can identify early signals of changing price sensitivity, emerging preferences by segment or region, and potential demand swings that might be easy for human teams to miss when looking at spreadsheets or static reports. As a result, retailers can update forecasts more frequently, adjust assortments and pricing sooner, and manage risk in a far more responsive way.
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