Beyond Trendspotting: Toward Continuous Category Simulation

In the volatile world of consumer goods, traditional trendspotting – the art of predicting "what's next" through market research reports and annual forecasts – is reaching its limits. The past few years have demonstrated that trends can emerge and fizzle in months or even weeks, upending the best-laid plans. Supply chain shocks, sudden shifts in consumer behavior (often driven by social media), and global crises have made linear forecasting incredibly difficult.

As a result, companies are finding that static trend forecasts quickly become obsolete in a fast-changing market. This has spurred interest in a more dynamic approach: continuous category simulation. Rather than betting big on a single trend prediction, forward-looking FMCG firms are beginning to use advanced simulation and modeling techniques to test multiple scenarios and adapt in near real-time.

This essay discusses why classic trendspotting falls short in today's environment and how simulation-driven category planning offers an alternative path to resilient innovation.

The Pitfalls of Traditional Trend Forecasting in Volatile Markets

Traditionally, companies relied on periodic trend reports (say, annual "flavor of the year" predictions or quarterly consumer sentiment surveys) to guide product development and marketing. They might identify that "plant-based protein" is a rising trend, then invest heavily in that area for the next 2-3 years. While this worked in relatively stable times, recent volatility has exposed the approach's weaknesses.

Trends now can be highly transient or abruptly altered by external events. A prime example: pre-2020, many trend reports wouldn't have highlighted "home baking" or "sanitizer" as explosive categories – yet the pandemic instantly made them huge. Likewise, a trend like "athleisure" in apparel might be cruising along and then a global event or a viral meme changes consumer priorities overnight.

Kantar's analytics experts noted that the complexity of interconnected market forces today makes traditional planning approaches inadequate. They pointed out that companies need to balance speed of insight with depth of understanding, as quarterly or annual planning cycles can't keep up with policy shocks or sudden consumer shifts. For instance, an unexpected tariff or regulatory change (like a sugar tax) might overnight alter pricing dynamics and consumer choices, rendering your year-old "trend forecast" moot.

The FMCG major in Kantar's case faced exactly this: tariff changes caused demand volatility and supply disruptions that outpaced their quarterly plans, leading to missed opportunities for those who reacted too slowly. The lesson is that forecasting based on yesterday's data in a straight-line manner fails when the rules of the game change faster than our planning cycle.

The Herd Mentality Problem

Another issue with traditional trendspotting is the herd mentality and overreliance on consensus. Many companies read the same reports from the likes of Nielsen, Mintel, or McKinsey. If all competitors see "functional beverages" as the next big thing, the market can quickly become saturated. A forecast might say "CBD-infused drinks will grow 5x by 2025" – but if 50 brands jump in, the reality could be a fragmented market with winners and losers, not universal success.

The forecast doesn't account for competitive over-crowding or consumer fatigue. We've seen this with things like low-carb or keto trends – a few brands captured the momentum, then an overflow of similar launches diluted consumer interest. Static forecasts also often fail to predict backlash or counter-trends. For example, trendspotters predicted a relentless rise in digital everything, but we now see micro-trends of "digital detox" and a resurgence of analog experiences.

Traditional reports seldom capture these countercurrents until they're obvious. In short, relying solely on periodic trendspotting can be like trying to steer a ship by looking at last year's star map. It might put you in the general direction, but it won't account for the storms and currents you encounter on the way. That's why companies are looking to continuous, adaptive planning frameworks.

Simulation-Driven Planning: "What If" as a Constant Exercise

Enter continuous category simulation. This approach borrows from techniques used in fields like finance (stress testing) and military strategy (war gaming) but applies them to market and consumer dynamics. Instead of asking "What's the trend for next year?", companies ask "What are multiple plausible futures for our category, and how would we respond to each?" and do this on an ongoing basis.

Modern simulation tools allow firms to create digital twins of their market or category – basically, virtual models that include factors like consumer segments, competitive products, price elasticity, marketing spend, supply constraints, etc. These models can be subjected to various shocks or trend assumptions. For example, what if Gen Z adoption of our product doubles? What if a new competitor with a low-price model enters? What if an ingredient shortage drives costs up 20%?

By simulating these scenarios, teams can see outcomes in terms of market share, revenue, or profit in the model world, without having to risk it all in the real world.

Real-World Implementation

Kantar's advanced forecasting solution "PrediKtor" (as described in 2025) illustrates this new paradigm. It forecasts brand performance 2–5 years out and simulates market scenarios under various assumptions (pricing changes, distribution shifts, economic conditions). In one use case, it integrated macro indicators (consumer confidence, volatility indices) and ran tailored scenarios to model tariff impacts.

This let their client answer: "If tariffs of X% hit, what happens to our sales and what can we do (raise price, adjust inventory) to mitigate?" Essentially, scenario simulation provides a safe sandbox to test strategies against multiple "futures."

Another example comes from marketing analytics: Analytic Partners notes that scenario planning can "forecast key outcomes such as sales, ROI, and market share" for various what-ifs, enabling brands to allocate budgets more effectively. They advocate using scenarios to test media spend changes, promotion intensity, or even external factors like interest rates.

By continuously doing this (not just as a one-off annual scenario plan), a company stays prepared. If they simulate quarterly, they might catch that a slight economic downturn scenario shows a big impact on premium product sales – so they have a contingency promotion plan ready just in case.

From Prediction to Preparation

Continuous simulation goes beyond trendspotting by embracing uncertainty. Instead of claiming "This will be the trend", it says "These could be the trajectories, and here's how we'd win in each case." It's a mindset shift from trying to be right about the future to trying to be ready for whatever future comes.

The goal is resilient innovation: you innovate in ways that are robust under many scenarios, not just a single forecast. For example, instead of launching a product line that only appeals if eco-consciousness skyrockets, you might design it to have both an eco-appeal and a price appeal, covering you in both a green-boom scenario and a recession scenario.

Case Study: From Lean Supply to Agile Simulation

One might ask, how is simulation-driven planning different from just doing frequent re-forecasts? The difference is in breadth (exploring multiple diverging scenarios, not just tweaking one forecast) and depth (incorporating system dynamics, not linear extrapolation).

Let's consider a category like plant-based dairy alternatives. Traditional trendspotting might say "almond milk will grow 10% CAGR next 5 years". A simulation-driven approach would consider various worlds: in one, a new study claims almond farming is unsustainable (so maybe oat milk surges); in another, inflation makes all alt-milks pricey so dairy rebounds; in a third, a tech breakthrough makes lab-grown milk cheap.

It would simulate consumer adoption in each of those and guide decisions like "invest in oat capacity as hedge", "develop a budget alt-milk", or "lobby for sustainability in almond farming".

COVID-19: A Natural Experiment

The benefits of simulation were evident during COVID-19. Some FMCG companies that fared better were those that rapidly simulated demand scenarios (lockdown vs. no lockdown, pantry-stocking waves, etc.) and adjusted production accordingly. Traditional forecasts failed spectacularly in early 2020 for items like toilet paper or yeast – nobody's 2019 trend report said "yeast sales will quadruple next spring."

But companies using continuous modeling could adjust quicker. One global manufacturer used scenario planning tools to simulate regional lockdown impacts on each product line, guiding them to reallocate resources to high-demand items (cleaning supplies, packaged foods) and away from slow movers like cosmetics. This resilience through simulation meant they could meet demand surges better than competitors who were stuck with pre-pandemic plans.

Implementing Continuous Simulation for Innovation

Moving to continuous category simulation requires changes in process and mindset. Companies need to invest in data infrastructure and analytical talent to build and run these models regularly. It's notable that 25% of consumer goods companies are now experimenting with AI-led design modeling and digital twin simulations according to Bain research.

The best companies are integrating these simulations not just in supply chain (where digital twins are more common) but also in sales and innovation planning. Bain specifically notes "the best companies scale their use of digital twin simulations in R&D and supply chains" – indicating that creating virtual models of consumers or production lines can greatly speed up and stress-test product development.

Cultural and Organizational Shifts

Continuous simulation also implies a cultural shift: an acceptance that strategy is iterative and conditional, not set in stone. Leadership has to be comfortable with "if-then" planning: "We'll pursue Plan A, but if by mid-year scenario X seems to be happening, we switch to Plan B."

This agile planning approach can conflict with old annual budgeting cycles. However, as Analytic Partners data shows, companies that use advanced measurement and scenario planning reallocate budgets 2–3x more effectively than those with basic methods, and can realize significant ROI gains by quick adjustments. In practice, this might mean setting aside a flexible investment fund that can be directed to whichever simulation scenario is emerging as reality.

Cross-Functional Integration

Another aspect is cross-functional collaboration. Simulations often reveal trade-offs across departments (e.g., a scenario might be good for sales but bad for supply chain, or vice versa). Having integrated planning teams ensures that simulations consider all angles and that responses are coordinated. In scenario A, marketing might lead the response; in scenario B, supply chain might. Continuous simulation thus breaks silos because it requires a holistic view of the business system.

Finally, continuous simulation feeds a learning loop. Each time a scenario is run or reality plays out differently than a model predicted, the company learns and updates the models. Over time, this can even incorporate machine learning – the simulations get more accurate in predicting what actions will achieve which results. The organization becomes more adept at recognizing early signals that correlate with certain scenarios, effectively "nowcasting" trends as they form, rather than forecasting long in advance.

Conclusion

The volatile, unpredictable nature of today's markets has rendered one-off trendspotting insufficient for strategic planning. The alternative – continuous category simulation – offers a way to stay ahead by staying flexible. By constantly exploring many possible futures, FMCG firms can innovate with resilience, knowing they have playbooks for different conditions.

This approach is akin to having multiple moves planned in a chess game, rather than banking on a single gambit. It doesn't mean intuition and creativity are thrown out; on the contrary, they're applied more frequently and in a targeted way when a scenario calls for it.

Continuous simulation, backed by real-time data and AI, turns planning into an ongoing game rather than a seasonal event. It acknowledges that in a chaotic environment, the winners will not be those who predict the future perfectly, but those who can rapidly adapt to whatever the future holds.

As one whitepaper put it, forecasts in volatile markets should be "directional, not declarative – guides for decision-making, not guarantees of outcomes". Simulation embodies that philosophy by guiding decisions under uncertainty.

In summary, moving beyond trendspotting doesn't mean ignoring trends; it means not becoming rigidly fixed on one expected trend. Instead, companies simulate and prepare for many trends, big and small, and pivot with confidence as the real world unfolds. This continuous approach to category planning will be a hallmark of the most innovative and resilient FMCG players in the years to come.