You've collected dozens of trends. Your team has flagged signals across technology, regulation, and consumer behaviour. But when you sit down to make sense of it all, you're staring at a sprawling list that resists interpretation.
The problem isn't the trends themselves — it's the lack of structure. Without a clear classification system, trend data remains noise rather than intelligence.
A well-designed trend taxonomy transforms scattered observations into organised insight. It helps you spot patterns, identify gaps in your scanning, and communicate change to stakeholders who need clarity, not complexity.
In this guide, I'll walk you through how to build and apply trend taxonomies that make your foresight work more rigorous and more useful. Whether you're managing intelligence for one organisation or multiple clients, these principles will help you classify change in ways that drive better strategic decisions.
A trend taxonomy is a structured classification system that organises trends into meaningful categories. Think of it as the filing system for your strategic intelligence — a framework that determines where each trend belongs and how it relates to others.
Unlike a simple list or tag system, a taxonomy provides hierarchy and logic. It typically includes:
Domains: Broad categories of change (e.g., Technology, Society, Economy, Environment, Politics) Subthemes: More specific areas within each domain (e.g., under Technology: Artificial Intelligence, Biotechnology, Materials Science) Cross-links: Connections between trends that span multiple domains
The key distinction between a taxonomy and a tag system is structure. Tags are flat and arbitrary — you can add "digital" to anything. Taxonomies are hierarchical and principled — they reflect how categories of change actually relate to each other.
For example, "algorithmic decision-making" might sit under Technology > Artificial Intelligence, but also connect to Society > Trust & Transparency. A good taxonomy captures both the primary classification and these cross-domain relationships.
Without classification, trend analysis becomes an exercise in pattern-matching against your own biases. You notice what's familiar and miss what's outside your usual frame.
Strategic gaps become visible. When trends are organised by domain, you can quickly spot where your scanning is rich and where it's thin. If your Technology domain has forty trends and Environment has three, that's a signal — either about where change is concentrated or where your attention isn't.
Communication becomes clearer. Stakeholders don't want to wade through undifferentiated lists. A taxonomy lets you present trends by relevance: "Here's what's shifting in your regulatory environment. Here's what's emerging in customer expectations." Structure creates focus.
Pattern recognition improves. When related trends sit together, convergences become obvious. Three separate signals in Biotechnology, Materials Science, and Sustainability might reveal an emerging cluster around bio-based manufacturing — something invisible when trends are scattered.
Institutional memory develops. Taxonomies persist across projects and time. You build a cumulative intelligence asset rather than starting fresh with each strategic exercise.
Don't invent categories from scratch. Begin with proven foresight frameworks and adapt them to your context.
STEEP/PESTLE (Social, Technological, Economic, Environmental, Political, Legal) is the most common starting point. It provides domain-level categories that most organisations recognise.
Tip: These frameworks are starting points, not destinations. Most organisations need to customise based on their industry and strategic focus.
Choose 5-7 top-level domains that reflect the change landscape relevant to your work. Too few domains and everything becomes a catch-all. Too many and you're splitting hairs.
For a technology company, domains might include: Technology & Infrastructure Market & Competition Regulation & Policy Society & Culture Talent & Workforce Environment & Resources
Example: A healthcare consultancy might use: Clinical Innovation, Health Systems, Regulatory & Policy, Patient Experience, Workforce, and Digital Health.
Each domain needs 4-8 subthemes that capture distinct areas of change. Subthemes should be:
Mutually exclusive (a trend shouldn't fit equally in two subthemes within the same domain) Collectively exhaustive (together, they cover the domain) Actionable (specific enough to guide strategic thinking)
Example subthemes for Technology & Infrastructure: Artificial Intelligence & Automation Data & Analytics Connectivity & Networks Cybersecurity & Privacy Materials & Manufacturing Energy & Power Systems
Create clear rules for where trends belong. Without criteria, classification becomes arbitrary and inconsistent across team members.
Useful criteria include: Primary driver: What's the fundamental force creating this trend? Impact domain: Where will effects be felt most directly? Time horizon: Is this emerging, maturing, or transforming?
Tip: When a trend spans domains, classify by primary driver and add secondary tags. "Remote work" is primarily Workforce, with secondary connections to Technology and Real Estate.
The most valuable insights often emerge at intersections. Your taxonomy should support capturing relationships between trends in different domains.
Common cross-link types: Enablers: One trend accelerates another (AI enables personalised medicine) Constraints: One trend limits another (talent shortages constrain automation deployment) Convergences: Multiple trends combining into something new
In Portage's Trend Database, structured domains and subthemes support these connections through AI cross-linking, making it easier to surface relationships without manual mapping.
Classify 20-30 trends against your taxonomy. Note where you hesitate, where trends don't fit cleanly, and where categories feel too broad or narrow.
Questions to ask: Are any categories consistently empty or overcrowded? Do team members classify the same trend consistently? Are important distinctions getting lost?
Refine based on what you learn. Taxonomies should evolve as your understanding deepens.
Write clear descriptions for each domain and subtheme. Include examples of what belongs and what doesn't.
This documentation becomes essential when: Onboarding new team members Working with clients who need to understand your framework Ensuring consistency across multiple workspaces or projects
A retail consultancy developed a taxonomy optimised for their client base:
Domains: Consumer Behaviour, Technology & Channels, Supply Chain, Regulatory, Workforce, Sustainability
Consumer Behaviour subthemes: Purchase Patterns, Values & Preferences, Demographics, Experience Expectations
This structure helped them quickly segment trends by strategic relevance. When a client asked about "what's changing for urban millennials," they could pull relevant trends from Consumer Behaviour > Demographics and Consumer Behaviour > Values & Preferences, cross-referenced with Technology & Channels > Digital Experience.
An independent consultant working across energy, healthcare, and financial services uses a hybrid approach:
Core taxonomy with universal domains (Technology, Regulation, Society, Economy, Environment) Client-specific subthemes within each workspace Shared signals that feed into multiple taxonomies
This lets them maintain one scanning practice while delivering contextualised intelligence to different stakeholders.
With trends properly classified, generating focused reports becomes straightforward. Instead of manually curating, you can pull all trends within "Regulation & Policy > Data Privacy" to brief a leadership team on compliance implications.
Portage's Trend Reports feature works this way — configurable to meet stakeholder needs because the underlying taxonomy provides structure for targeted intelligence delivery.
1. Keep it practical, not academic. Your taxonomy should help people make decisions, not demonstrate intellectual comprehensiveness. If a category never gets used, remove it.
2. Review quarterly. The change landscape shifts. New domains emerge (five years ago, few taxonomies included "AI Ethics" as a subtheme). Schedule reviews to keep your framework current.
3. Train your team. A taxonomy only works if everyone uses it consistently. Invest in alignment on definitions and classification logic.
4. Use progressive detail. Not every trend needs deep classification. Use full taxonomy depth for high-priority trends; simpler domain-level classification for weaker signals.
5. Avoid over-fragmentation. The goal is useful groupings, not perfect precision. If you're debating whether something is "Consumer Technology" or "Digital Infrastructure," your categories might be too granular.
6. Document the "why" behind classifications. When you revisit trends months later, you'll want to understand the reasoning that placed them in specific categories.
A well-designed taxonomy supports every stage of trend intelligence work. Here's how this connects to related practices:
Signals vs Trends: A Modern Foresight Framework — Understand what you're classifying before you classify it. How to Build a Trend Radar: A Complete Guide — Use your taxonomy to structure radar visualisation. Trend Clustering Techniques (With Examples) — Group trends within and across taxonomy categories. How to Evaluate Trend Impact — Apply impact assessment to classified trends. Trend Report Template — See how taxonomies structure report outputs.
Return to pillar: Signals, Trends & Strategic Intelligence: Making Sense of Change
Start with your existing trend collection. Map 15-20 trends against the STEEP framework, noting where the fit feels wrong or forced. Those friction points reveal where you need custom categories.
If you're building intelligence for multiple teams or clients, Portage's Trend Database offers structured domains and subthemes with AI summarisation and cross-linking — giving you taxonomy infrastructure without starting from scratch.
Generate your first Trend Radar to see how classified trends translate into visual intelligence your stakeholders can act on.
Taxonomies transform lists into intelligence. Structure reveals patterns, gaps, and relationships that flat lists hide. Start with established frameworks, then customise. STEEP/PESTLE provides foundation; your context determines the detail. Classification criteria matter. Clear rules ensure consistency across team members and time. Cross-links capture the most valuable insights. Trends rarely respect category boundaries; your taxonomy shouldn't ignore that. Iterate based on use. Test your taxonomy against real trends and refine what doesn't work. Document everything. Definitions ensure your taxonomy remains useful as your team and intelligence needs grow.