The Next Trend Isn’t a Trend, It’s a Behavioral Signal

Over the years I’ve written articles on sunscreen trends as we ease into the summer season.

And each time I have found myself doing the same thing: downloading trend reports, reviewing consumer insight summaries, reading market forecasts, and trying to piece together where the category is headed next.

At first, it feels promising. There are always new ingredients, new claims, new formats, and new consumer priorities. One year it’s hybrid skincare. Another year its sustainability, skinification,  and so on (Niedziela, 2020, 2024, 2025). The reports are full of charts showing what consumers are searching for, what brands are launching, and what products are growing.

But after reading enough of them, I often find myself asking the same question: Now what?

Most trend reports do an excellent job telling us what is happening right now. They tell us which ingredients are gaining attention, which claims are resonating, and which products are selling. What they rarely tell us is where these trends are likely to go or what opportunities might emerge next.

For R&D and innovation teams, that’s the critical question.

Knowing that consumers are interested in mineral sunscreen is useful. Understanding why they are interested may be far more valuable. Are they seeking safety? Simplicity? Transparency? Environmental responsibility? Trust? These underlying motivations can reveal opportunities that extend far beyond any single ingredient or claim.

Working in behavioral science and using psychology and neuroscience for consumer insights, I’ve increasingly found myself less interested in the trends themselves and more interested in the human behaviors, tensions, and unmet needs that give rise to them. Because consumers rarely ask for the next innovation directly. Instead, they reveal it indirectly through their frustrations, workarounds, aspirations, and evolving habits.

A trend may tell us what is popular today. A behavioral pattern may help us understand what could become possible tomorrow.

Trend versus Direction

A recent discussion in the behavioral science community about the McNamara Fallacy  caught my attention (Yankelovich, 1971). The idea is simple: when we focus too heavily on what is easy to measure, we risk overlooking what is actually important. As AI gives us access to unprecedented volumes of consumer data, that warning feels increasingly relevant. We have become exceptionally good at measuring trends. The harder question is whether we are equally good at understanding them.

One of the most interesting challenges in consumer forecasting is distinguishing between a trend and a direction.

A trend is relatively easy to identify. With enough data, we can observe increases in search volume, social media mentions, product launches, or sales. Artificial intelligence is making this easier than ever. Today, organizations can analyze millions of consumer conversations, reviews, comments, and discussions, uncovering signals long before they appear in traditional market reports.

Yet more signals do not automatically create more insight.

In fact, the abundance of data can sometimes amplify the very problem the McNamara Fallacy warns against. We become increasingly focused on counting mentions, tracking sentiment, monitoring engagement, or identifying the fastest-growing topics. These metrics are valuable, but they remain observations of behavior rather than explanations of behavior. They tell us what consumers are discussing, purchasing, or searching for. They do not necessarily tell us why.

This challenge is closely related to Goodhart’s Law, the observation that when a measure becomes a target, it ceases to be a good measure (Goodhart, 1984). As organizations become increasingly focused on tracking mentions, sentiment, engagement, and emerging topics, there is a risk that the metrics themselves become the objective rather than a means of understanding consumers. Philosopher C. Thi Nguyen explores a related idea in The Score: How to Stop Playing Somebody Else’s Game (2024), arguing that people often become so focused on improving the score that they lose sight of the broader purpose the score was intended to represent. In consumer insights, search volume, social mentions, and trend reports can be valuable indicators, but they are still indicators. The challenge is ensuring that we do not mistake the score for the game.

Behavioral science may offer a different perspective. For decades, researchers have recognized that observable behaviors are often manifestations of deeper motivations, goals, habits, and tensions. Consumers rarely seek a product because of a specific ingredient alone. Products become meaningful or worth pursuing because they help consumers achieve desired outcomes, reduce uncertainty, express identities, solve problems, or fit into their daily lives (Kruglanski et al., 2002).

This distinction helps explain the difference between a trend and a direction.

Consider sunscreen. Over the years, trend reports have highlighted the growth of mineral formulations, reef-safe claims, blue-light protection, skinification, multifunctionality, and ingredient transparency. Viewed individually, these appear to be separate trends. Viewed behaviorally, however, they may point toward a broader direction: consumers increasingly seeking products that help them manage health, wellbeing, safety, and environmental responsibility while simplifying increasingly complex decisions.

The distinction matters because trends tend to change. But directions tend to persist.

A trend may tell us that consumers are interested in a particular ingredient. A direction reveals opportunities for entirely new product formats, sensory experiences, usage occasions, or communication strategies that have not yet emerged. Understanding the direction allows innovators to think beyond what consumers are asking for today and toward what they may need tomorrow.

Reading Between the Trends

Artificial intelligence is transforming consumer trend analysis. Today, AI can rapidly synthesize millions of product reviews, Reddit discussions, TikTok conversations, retailer feedback, and consumer interviews to identify emerging themes and patterns that would have been nearly impossible to detect manually. Trend platforms like Google Trends, Pinterest Predicts, and WGSN are increasingly complemented by AI-powered tools capable of analyzing consumer conversations at an unprecedented scale.

This ability to recognize patterns across massive datasets is perhaps AI’s greatest contribution to consumer insights. Rather than spending weeks manually searching for recurring themes, researchers can now spend more time asking a more interesting question: What do these patterns actually mean?

Imagine thousands of consumers discussing sunscreen. One conversation focuses on mineral formulations. Another on skin barrier protection. Others mention avoiding certain ingredients, wanting products that fit into busy routines, or looking for multifunctional skincare. On the surface, these appear to be separate trends. AI can help reveal that they frequently occur together. Modern LLM-based approaches can automatically identify recurring patterns, themes, and associations across thousands of documents, making it possible to discover relationships that may not be apparent through manual review (Plaue, 2023). But behavioral science helps explain why.

We’re seeing similar behavioral directions in cosmetics, where trends such as skinification, minimalist routines, multifunctional products, and ingredient transparency may all reflect consumers’ desire to simplify decision-making while maintaining confidence in their choices. In food, seemingly separate trends like high-protein snacks, GLP-1-friendly products, portion control, and satiety claims may all point toward a broader behavioral direction around intentional eating.

Perhaps they are all pointing toward something larger: consumers looking for products that reduce uncertainty, simplify decision-making, and support long-term wellbeing.

That shift in thinking changes the role of trend analysis. Instead of simply reporting what is growing, AI allows us to ask what direction consumer behavior may be moving.

For R&D teams, that distinction matters. Understanding the direction behind multiple emerging trends can reveal opportunities for entirely new product formats, sensory experiences, usage occasions, or communication strategies, often before consumers have articulated them directly.

But recognizing these broader behavioral directions is only valuable if they change how we innovate.

What This Means for R&D

Perhaps the biggest opportunity for R&D isn’t becoming better at spotting trends. It’s becoming better at asking different questions.

Instead of asking, What ingredient is trending? we might ask, What consumer problem is this ingredient helping solve?
Instead of asking, Which claim is growing? we might ask, What underlying uncertainty or aspiration is this claim addressing?

Instead of asking, What products are consumers buying? we might ask, What behavioral shift is creating demand for those products?

These questions move us beyond describing today’s market and toward identifying tomorrow’s opportunities.

This doesn’t mean replacing traditional trend reports or consumer research. Those remain incredibly valuable. Rather, it means adding another layer of interpretation, one that seeks to understand the motivations, tensions, habits, and unmet needs connecting seemingly unrelated consumer conversations.

This philosophy forms the foundation of an approach called Behavioral Topic Mapping. Behavioral Topic Mapping explores how seemingly separate consumer conversations reveal shared motivations, tensions, and behavioral directions that may point toward future innovation opportunities. Rather than treating consumer conversations as isolated topics to categorize, it explores how seemingly separate discussions may point toward shared behavioral directions. I’ve written more about this thinking on my blog for readers interested in exploring the concept further.

Whether you call it Behavioral Topic Mapping or simply behavioral interpretation, I believe the future of consumer insights lies in connecting the dots, not just counting them. The next breakthrough product may not emerge from the next trend report. It may emerge from understanding the human behavior that dozens of seemingly unrelated trends have been pointing toward all along.

References and notes

• emi518. (2026, April). Best sunscreen I tested (17+ review) [Online forum post]. Reddit. https://www.reddit.com/r/SkincareAddiction/comments/1szuqh1/best_sunscreen_i_tested_17_review/
• Goodhart, C. A. (1984). Problems of monetary management: the UK experience. In Monetary theory and practice: The UK experience (pp. 91-121). London: Macmillan Education UK.
• Kruglanski, Arie & Shah, James & Fishbach, Ayelet & Friedman, Ron & Chun, Woo Young & Sleeth-Keppler, David. (2002). A theory of goal systems. Advances in Experimental Social Psychology – ADVAN EXP SOC PSYCHOL. 34. 331-378. https://doi.org/10.1016/S0065-2601(02)80008-9
• Nguyen, C. T. (2024). The score: How to stop playing somebody else’s game. Random House.
• Niedziela, M. (2024). Hybrid suncare: The shift towards multifunctional buy less, buy better skincare. HPC Today. https://www.hpc-today.com/articles/hybrid-suncare-the-shift-towards-multifunctional-buy-less-buy-better-skincare/
• Niedziela, M. (2020). Sunny outlook: Sustainable sun care products. HPC Today, 15(3). Teknoscienze. https://www.teknoscienze.com/tks_article/sunny-outlook-sustainable-sun-care-products/
• Niedziela, M. (2025). Beyond the filter: Unraveling influencer-driven skinification trends. HPC Today. https://www.hpc-today.com/articles/beyond-the-filter-unraveling-influencer-driven-skinification-trends/
• Niedziela, M. M. (2026). Behavioral topic mapping. Nerdoscientist. https://www.nerdoscientist.com/blog/behavioraltopicmapping
  Plaue, M. (2023, June 6). *LLMs for innovation and technology intelligence: News categorization and trend signal detection*. Medium. https://medium.com/mapegy-tech/llms-for-innovation-and-technology-intelligence-news-categorization-and-trend-signal-detection-ec4171627937
• Reddit. (n.d.). r/Sunscreenreddit. https://www.reddit.com/r/Sunscreenreddit/
• Yankelovich, Daniel (October 15, 1971). The New Odds (PDF) (Speech). Marketing Strategy Conference of the Sales Executives Club of New York. https://ryanmadden.net/pdf/the_new_odds.pdf Retrieved April 4, 2026.