Topic clustering
Grouped queries into coherent themes while preserving distinct audience intent.
National advocacy website · Search and content strategy · 2025–2026
Connecting Search DemandTo a Clearer Content System
I organized a large search-query set into meaningful topic clusters, connected those clusters to existing content and identified where the website needed stronger or more distinct coverage.
01 · The challenge
A large search dataset is only useful when it becomes a content decision.
1,000
Priority queries to organize
The website appeared for a broad mix of policy, educational and informational searches. Raw query exports showed demand but did not explain how topics related, where pages overlapped or which gaps deserved attention.
The strategy needed to preserve subject-matter nuance while remaining practical for content planning.
02 · Scope
From query data to content architecture
Content mapping
Connected search clusters to current landing pages and content areas.
Gap identification
Highlighted missing coverage, weak matches and areas of internal competition.
03 · Campaign process
Turn search behavior into an editorial map.
Prepare the query set
Clean and prioritize search terms for analysis.
Cluster by subject and intent
Group related searches without collapsing meaningful differences.
Map clusters to pages
Identify which content currently served each area of demand.
Prioritize action
Translate gaps and overlaps into content recommendations.
04 · Results
Search demand became a structured planning system.
1,000
Priority queries reviewed
50+
Topic clusters created
15
Content opportunities identified
Use only approved aggregate counts. Do not publish the underlying keyword dataset or client content map.
05 · Reflection
Clustering is the beginning of strategy, not the output.
The useful step was connecting search behavior to the website: what each audience needed, which page should respond and where the current structure created confusion.