Why categories are a product decision, not a navigation decision
Categories are not just navigation labels. They shape how users discover markets, how liquidity clusters, how search engines understand the site, and ultimately how much your platform earns from each visitor. A category system that mirrors user intent can multiply both engagement and signal quality. A category system that mirrors internal team structure does the opposite — it scatters related markets across pages and leaves money on the table.
This article walks through the full category design framework: how to choose the right taxonomy, how categories interact with liquidity, how to optimise them for SEO, and how editorial copy on category pages turns a "navigation element" into a content asset. It complements binary vs multiple outcome markets (which covers market-level design) and our introduction to prediction markets (the foundational concepts).
The three jobs a good category does
Categories serve three audiences simultaneously: users, search engines, and the liquidity-providing market makers. A great category page does all three jobs at once.
| Audience | What they need | What that looks like on the page |
|---|---|---|
| Users | A coherent topic map and clear navigation | Editorial intro, featured markets, related categories |
| Search engines | A linkable hub with unique, indexable copy | 500+ word category description, internal links, schema.org markup |
| Market makers | Volume aggregation around predictable themes | Consistent naming, related-markets links, theme tags |
The single biggest mistake teams make is optimising for one audience at the expense of the others. Pure SEO content with no editorial voice does not retain users. Pure UX with no SEO content does not get traffic. Pure liquidity tooling with no editorial framing does not build trust. Great categories serve all three.
Step 1 — Choose your taxonomy
The first design decision is whether categories are broad or narrow, flat or hierarchical, and whether you use formal taxonomy or tag-based discovery.
Broad categories (Politics, Sports, Economy) are easy to maintain but lose context. "Politics" includes US presidential elections, UK general elections, EU parliamentary votes, and Brazilian municipal results — markets that share almost nothing in common other than the word "politics".
Narrow categories (US Presidential 2028, Fed Meetings, English Premier League) provide context but multiply maintenance. Every event requires a new category, and the category pages become thin.
Hierarchical categories (Politics → US → Federal → Elections → 2028 Presidential) provide context at multiple levels but require careful curation. Hierarchies that look like database schemas to internal teams often confuse users who expect simpler, flatter navigation.
The practical sweet spot for most platforms:
- Top-level categories: 4–8 broad categories that cover the platform's full scope.
- Sub-categories: 3–6 sub-categories per top-level category, each with sustained content.
- Tags: Free-form tags for cross-cutting themes that do not fit cleanly into the hierarchy.
This produces 12–48 sub-category pages — enough to provide context, few enough to maintain.
For inspiration, look at how Polymarket and Kalshi structure their navigation. Both use 5–8 top-level categories with sub-categories that mostly mirror the major thematic clusters their audience cares about.
Step 2 — Match categories to user intent
A category that matches user intent attracts repeat users. A category that fights user intent attracts confused visitors who do not convert. The simplest way to validate intent is to look at the search queries your existing users type into the platform's internal search bar.
Common intent patterns:
| User intent | Implied category structure |
|---|---|
| "Will the Fed cut rates?" | A "Fed meetings" sub-category under Economy |
| "Crypto 2026 outlook" | A "Crypto" top-level category with sub-categories per asset |
| "World Cup 2026 winner" | A "World Cup" sub-category under Sports, with sub-pages per match |
| "Will AI replace lawyers?" | An "AI" sub-category under Tech with sub-pages per profession |
If your category names do not match the language users type, you have a friction problem. The fix is to rename, or — better — to support both internal-team names (for ops clarity) and user-facing names (for discovery).
You can also use Google search data via the Google Search Console and the Google Trends APIs to validate which category names attract organic traffic. The data is free and the signal is real.
Step 3 — Use categories to cluster liquidity
Liquidity clusters around context. A user who lands on a "Fed meetings" page is more likely to trade multiple Fed-related markets than the same user landing on a homepage scattered with politics, sports, and crypto markets. Category pages are liquidity tools.
This insight has direct product implications:
- Surface 5–10 related markets on each category page. Users explore by theme, not by random catalog.
- Cross-link related markets within the same category. A market on "Fed cut at September meeting" should link to "Fed cut at November meeting" and "Inflation above 3% in Q3".
- Show a leaderboard of the most-traded markets in the category. Users follow volume.
- Highlight recent resolutions for credibility. Track records build trust in the category as a forecasting tool.
The aggregate effect is measurable: well-designed category pages typically lift trading volume on their underlying markets by 20–40% versus catalogues where the same markets sit unconnected. For the AMM math behind why concentrated liquidity matters, see comparing AMM liquidity models and our LSAMM deep dive.
Step 4 — Write category copy that earns SEO traffic
Most prediction platform category pages have either no copy or a single sentence of placeholder text. That is missed traffic. A category page that ranks for "Fed meeting prediction market" is essentially free customer acquisition for every market in that category.
A category copy template that consistently earns search traffic:
- Headline (H1): The category name framed as a search query. "Fed Meeting Prediction Markets" beats "Fed Meetings".
- Intro paragraph (200–300 words): What the category covers, who it is for, and why it matters. Use natural keyword variations.
- What you will find here (H2): A list of the kinds of markets in the category, with internal links.
- How to read these markets (H2): Editorial guidance on interpreting the prices in this category.
- Recent resolutions (H2): A short list of recently resolved markets with their actual outcomes.
- Related categories (H2): Internal links to adjacent categories.
A 600–800 word category page following this template gives Google enough content to rank, gives users enough context to navigate, and gives market makers enough information to size liquidity.
Beyond copy, also implement:
- Open Graph and Twitter cards for shareable previews.
- Breadcrumb schema to help search engines understand the hierarchy.
- CollectionPage schema (via schema.org/CollectionPage) for rich category indexing.
- Hreflang tags if the platform serves multiple languages.
Step 5 — Manage the long tail of sub-categories
Most categories have a Pareto distribution: a few sub-categories drive most of the volume, while the long tail of sub-categories has thin trading. Two strategies handle this well.
Curate the head, automate the tail. Hand-write the editorial copy for the top 10–20% of sub-categories. Use templates with light personalisation for the remaining 80%. Most users only see the head categories, so investing editorial time there is the highest-ROI move.
Prune ruthlessly. Sub-categories with no volume after 60 days should be merged or deleted. A category page with three thin markets and no editorial copy actively hurts your search positioning. The empty pages signal a thin site to search algorithms.
The data discipline matches the editorial discipline: track per-category engagement (page views, time on page, trades initiated) and feed it into a quarterly review process. The categories that are pulling weight get more investment; the ones that are not get cut.
Step 6 — Coordinate categories with content marketing
Categories on a prediction market platform are not separate from blog content — they are part of the same content graph. A blog post about "How Fed meetings affect bond markets" should link to the Fed meetings category page, and the Fed meetings category page should link back to the relevant blog posts.
This is the topic cluster model that has dominated modern SEO since HubSpot popularised it in 2017. A pillar page (the category) is supported by satellite content (blog posts), and the cluster signals topical authority to search engines.
Concretely:
- Every blog post about prediction markets should link to at least one category page.
- Every category page should link to at least one blog post that explains the category's theme.
- The internal-linking structure should be intentional, not accidental.
Our category pages link to and from our introduction to prediction markets, LSAMM algorithm explained, and market microstructure for forecasting posts — the foundational pillar pieces that establish topical authority for the whole site.
Common category design mistakes
Three patterns I see repeatedly.
Categories that mirror the internal team structure. "Markets ops" is a team, not a category. "AI-assisted markets" is a category if users search for it, otherwise it is internal jargon.
Too many empty categories. A platform with 60 categories where 40 are empty signals chaos to users and to search engines. Fewer, fuller categories beat more, emptier ones.
No editorial copy on category pages. A page with only a market grid and no text cannot rank in search and cannot retain low-intent visitors. Even 300 words of category description beats none.
Reorganising too often. Category structure changes break inbound links. Once you have committed to a taxonomy, hold it for at least 12 months before reorganising — and use 301 redirects when you do reorganise.
Ignoring multi-language users. Categories serve the same purpose in every language. Translating only the market titles and leaving the categories in English is a missed opportunity. Our discussion of category translation in the introduction to prediction markets covers the architectural pattern.
Practical implications
For builders, the lesson is to treat market design, content, and liquidity as one system. A category page, a resolution rule, and an AMM parameter all influence whether users trust the probability they see. When these pieces are aligned, the market becomes easier to discover, easier to trade, and easier to explain. See our comparing AMM liquidity models for the liquidity-side decisions and binary vs multiple outcome markets for the market-level structure.
For traders, the practical implication is to learn the taxonomy of the platforms you use. The categories tell you where the platform has invested liquidity and editorial attention — both signals of where the price quality is best. A platform with a deep "Fed meetings" category and a thin "obscure sports" category is telling you exactly where the trades worth taking live.
What to watch next
The next generation of prediction markets will reward teams that combine strong infrastructure with clear editorial context. The winners will not only launch more markets — they will help users understand why each market exists, what it measures, and how to act on the signal without taking unnecessary risk. Our outlook on the future of prediction markets in 2026 covers the industry trends.
Two specific category-design trends to watch:
- AI-assisted category curation. Platforms are starting to use LLMs to auto-suggest categories based on incoming market submissions. The best implementations keep a human in the loop for editorial voice; the worst produce a fragmented catalog.
- Cross-platform category aggregation. Aggregators that show the same category (e.g., "Fed meetings") across multiple platforms are emerging. Platforms with strong category architecture will be more discoverable in these aggregators.
Frequently Asked Questions
How many categories should a prediction market platform have?
A practical answer is 4–8 top-level categories and 12–48 sub-categories. Below that range you sacrifice context; above it you fragment your liquidity and editorial budget. The exact number depends on your audience size and the breadth of the markets you want to host.
Should categories be broad or narrow?
Top-level categories should be broad enough to evergreen (Politics, Economy, Sports, Tech, Culture). Sub-categories should be narrow enough to give context (Fed Meetings, NBA Finals, AI Releases). The sweet spot is a two-level hierarchy: 4–8 top-level, 3–6 sub-categories under each.
How do categories affect SEO?
Categories act as pillar pages in a topic cluster. A well-written category page can rank for high-value queries like "X prediction market" or "X event probability". The internal links from category pages to individual markets distribute search authority and improve the ranking of every market in the category.
What is a topic cluster and why does it matter for prediction platforms?
A topic cluster is a content architecture where a pillar page (the category) is supported by satellite content (blog posts, individual markets). Search engines reward topical authority — the depth and breadth of coverage on a specific topic — and topic clusters are the standard way to build it. For prediction markets, the categories are the natural pillar pages.
Should I use tags or categories?
Both, for different purposes. Categories form the primary hierarchy and define the site's information architecture. Tags are cross-cutting themes that span multiple categories. "Election volatility" might be a tag that appears across Politics, Crypto, and Macro categories. Tags are for discovery; categories are for navigation.
How often should I reorganise my category structure?
As rarely as possible. Each reorganisation breaks inbound links and confuses users who learned the old structure. Commit to a taxonomy for at least 12 months. When you do reorganise, use 301 redirects from old to new URLs and announce the changes to power users.
What is the minimum editorial copy a category page needs?
300–500 words of unique, non-templated copy at minimum. The copy should explain the category's scope, the kinds of markets it contains, and how to interpret the prices. Without this copy, the page cannot rank in search and cannot retain low-intent visitors.
How do I measure category page performance?
Three primary metrics: organic search traffic to the category page, on-platform engagement (time on page, clicks to underlying markets), and trades initiated from the category page. Combine the three into a per-category dashboard and use it to prioritise editorial investment.
Where to go next
Category design is the bridge between content strategy and liquidity strategy. The natural follow-ups:
- For the market-level design decisions that sit inside categories, see binary vs multiple outcome markets.
- For the AMM math that shapes the liquidity behind the categories, see comparing AMM liquidity models and the LSAMM algorithm explained.
- For the trader perspective on reading category structure, see market microstructure for forecasting.
- For platform context, introduction to prediction markets ties everything together.
Categories are the easiest part of a prediction market platform to underinvest in and the part with the most compound value. A great category architecture, written carefully and maintained patiently, becomes a moat that scales with every new market you launch.
