SEO Search Intent Mapping
Map search queries to intent and suitable page types without turning model output directly into publishing decisions.
An editorial planning workflow for classifying SEO queries and selecting a collection, product, guide, comparison, or blog-page hypothesis for review.
- Provider
- Type Safe Workflow
- Reviewed
- September 23, 2026
- Project status
- Community
- Verification
- Needs review
- Categories
- content, analytics
- Compatibility
- JEV, Laya, Bespoke Nimble, CSV
Inputs
- ✓Search query
- ✓SERP notes
- ✓Site inventory
- ✓Intent rubric
Outputs
- ✓Intent
- ✓Suggested page type
- ✓Confidence
- ✓Review state
Workflow steps
- 1Create a labelled sample that reflects the site and target market.
- 2Define intent labels and page-type criteria with examples and exclusions.
- 3Classify each query with only the context needed for the decision.
- 4Review mixed intent, low confidence, and high-value topics manually.
- 5Cluster only after intent review so similarity does not override user need.
- 6Map approved clusters to existing pages or a new-page brief.
Decision tasks
- ✓Classify
Workflow implementation guide
This editorial pattern requires local evaluation before operational use. Empty tested-version fields mean the directory has not run it against a pinned model release.
Business problem
Keyword lists often mix informational, commercial, navigational, and transactional needs that require different page formats.
Who this is for
SEO strategists and content teams planning site architecture or editorial backlogs.
Model selection notes
Use the model that performs best on your labelled query sample. Multilingual sites should test each target language separately.
Tested model versions
Unknown
- Industry
- SEO, Content
- Difficulty
- beginner
- Estimated setup time
- 1 to 3 hours for a reviewed sample
- Compatible model candidates
- jev, laya, bespoke-nimble
- Required integrations
- Keyword CSV, Content inventory
- Recommended model
- No single model recommended before local evaluation
Detailed execution steps
- 1Create a labelled sample that reflects the site and target market.
- 2Define intent labels and page-type criteria with examples and exclusions.
- 3Classify each query with only the context needed for the decision.
- 4Review mixed intent, low confidence, and high-value topics manually.
- 5Cluster only after intent review so similarity does not override user need.
- 6Map approved clusters to existing pages or a new-page brief.
Confidence policy
Choose a threshold from labelled queries and monitor it by language and intent class. Mixed-intent queries always require review.
Human review policy
An SEO reviewer checks page-type recommendations against the live search results, site inventory, and cannibalization risk.
Privacy notes
Keyword lists are usually low sensitivity, but private Search Console exports can contain internal page and business performance data.
Cost notes
Batch and deduplicate queries before scoring. Reviewer time usually matters more than per-query inference for an early pilot.
Common failure modes
- ✓The model predicts page types from wording while ignoring current search results.
- ✓One English threshold is reused for languages with different performance.
Troubleshooting
- ✓Add reviewed SERP evidence for ambiguous high-value topics.
- ✓Split broad labels when reviewer disagreement stays concentrated in one class.
Structured request example
{"query":"best typed decision model","intent_options":["informational","commercial","navigational","transactional"],"page_types":["guide","comparison","directory","product"]}Keep uncertain queries out of automatic clustering
typescript · TypeScript 5.9
export function mapQuery(result: {
query: string;
intent: string;
pageType: string;
confidence: number;
}) {
return result.confidence >= 0.82
? { queue: 'approved-clustering', ...result }
: { queue: 'editor-review', ...result };
}Implementation guidance
Keep the declared inputs and outputs visible in code review. Validate every model response before it reaches a database, API, or customer-facing action.