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Shopper Insights: Category Growth Through Trends & Tech

By Kevin, Founder & CEO

The most valuable shopper insights rarely appear in purchase data alone. When Oatly expanded beyond coffee shops into retail, their research revealed something unexpected: buyers weren’t comparing oat milk to dairy alternatives—they were evaluating it against premium coffee experiences. This single insight reshaped their entire category strategy, positioning the product not in the milk aisle mindset but in the craft beverage space.

Traditional shopper research focuses on what people buy. Category growth demands understanding why shopping behaviors shift, what cultural forces drive new consumption patterns, and which technological changes create openings for expansion. The gap between these two approaches explains why some brands identify category opportunities years before competitors while others miss transformations happening in plain sight.

The Limitations of Purchase-Based Shopper Intelligence


Most shopper insights programs rely heavily on transaction data, basket analysis, and point-of-sale metrics. These sources answer critical tactical questions about current behavior but systematically miss the signals that predict category evolution. A 2023 McKinsey study found that 73% of successful category expansions were driven by insights invisible in purchase data—specifically, shifts in consumer language, emerging use cases, and changing competitive reference sets.

Consider the trajectory of energy drinks over the past decade. Purchase data showed steady growth in the category, but it couldn’t explain why growth accelerated dramatically after 2018. The answer emerged from conversation analysis: consumers stopped describing energy drinks as alternatives to coffee and started positioning them as performance enhancers comparable to supplements. This linguistic shift preceded a 34% category expansion as brands like Celsius capitalized on the wellness positioning that purchase data alone would never have surfaced.

The problem intensifies when brands attempt to identify white space opportunities. Transaction data reveals current category boundaries but provides limited insight into how shoppers might reorganize those boundaries. When shoppers begin solving problems differently—replacing purchased solutions with DIY alternatives, or combining products in unexpected ways—these behavioral shifts remain invisible until they reach scale. By then, first-mover advantage has evaporated.

Cultural Signals That Precede Category Shifts


Category growth opportunities typically emerge from cultural movements long before they appear in shopping behavior. The challenge lies in distinguishing meaningful cultural shifts from temporary trends. Research from the Harvard Business Review identified three characteristics that separate transformative cultural movements from passing fads: they change how people describe their problems, they create new social signaling opportunities, and they generate novel product combination behaviors.

The plant-based food category illustrates this pattern. Between 2016 and 2019, before major sales inflection points, consumer language underwent systematic transformation. Shoppers stopped using terms like

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Frequently Asked Questions

Purchase data only captures behavior that has already occurred, meaning it reflects where a category has been rather than where it's going. Cultural signals that precede category shifts — changing shopper vocabulary, emerging use occasions, new purchase motivations — appear in consumer language months or years before they register as measurable sales trends, making purchase data structurally late for growth planning.
When shoppers begin using new language to describe their needs — introducing terms from adjacent categories, dropping old vocabulary, or adding new attributes to their consideration set — those linguistic shifts indicate that their mental model of the category is evolving. Brands that track this language evolution can reposition or innovate to match where shoppers are heading, rather than where they currently are.
User Intuition's AI-moderated interviews continuously capture how shoppers describe their category needs, what new occasions or motivations are emerging, and which adjacent product spaces are entering their consideration set. Longitudinal tracking across a 4M+ panel in 50+ languages surfaces the vocabulary shifts and emerging themes that signal category evolution weeks or months before they appear in sales data.
Technology signals — the platforms, tools, and devices shoppers use to discover and evaluate products — shape which cultural signals become commercially relevant. A cultural trend toward sustainability that lives primarily in long-form editorial content reaches a different segment at a different pace than the same trend expressed through short-form social video. Tracking both the cultural signal and its technology vehicle identifies which category growth opportunities are genuinely actionable at scale versus limited to early-adopter segments.
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