Overview
A repeatable method for finding, sizing and closing the gap between what your audience asks and what you answer well.
Business problem
Content decisions are made from keyword lists, not from the real questions buyers put to search and AI engines.
Decision supported
Which query gaps are worth closing next, and with a new page or an edit to an existing one.
Inputs & outputs
Inputs
- Query demand data (search + AI)
- Current URL-to-query coverage
- Competitor coverage
- Business value per query
Outputs
- Ranked list of query gaps
- Query Gap Score per topic
- New-vs-refresh recommendation per gap
Step-by-step process
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1
Map demand
Assemble the real questions people ask search and AI engines in your category, including long-tail and decision-stage phrasings.
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2
Map coverage
Match each query to the page (if any) that answers it and grade how well.
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3
Compute the gap
Score each query on demand × value × coverage weakness to get a Query Gap Score.
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4
Decide the response
Close via a new page when no asset fits, or an edit when one is close.
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5
Prioritise and ship
Order by Priority Score, close the top gaps, and re-measure.
Maturity model
-
L1
Ad hoc
Gaps found by anecdote and competitor envy.
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L2
Listed
Gaps documented but not scored.
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L3
Scored
Every gap carries a Query Gap Score and a decision.
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L4
Systematised
Gap detection runs continuously and feeds the roadmap.
KPIs
- Query coverage %
- Query Gap Score trend
- Win rate on closed gaps
Common mistakes
- Chasing head terms you cannot win
- Treating volume as value
- Creating a new page when an existing one is one edit from ranking
SearchScore insight
Recommended next steps
Where this fits - and what's next
The SearchScore path from a problem you feel to visibility you can measure.