Knowledge Coverage Model

Overview

Measures how completely your content covers a topic's questions, sub-topics and decision points.

Business problem

Coverage looks broad but has holes that let competitors own sub-topics and AI engines cite them instead.

Decision supported

Where a topic cluster is incomplete and what to add to complete it.

Inputs & outputs

Step-by-step process

1. 1
   Enumerate

   List every question and sub-topic a complete resource would cover.

2. 2
   Match

   Map existing content to each.

3. 3
   Score

   Coverage = answered ÷ total, weighted by importance.

4. 4
   Fill

   Close the highest-value holes first.

Maturity model

1. L1
   Assumed

   Coverage taken on faith.

2. L2
   Mapped

   Questions and sub-topics enumerated.

3. L3
   Scored

   Coverage Score per cluster.

4. L4
   Complete

   Clusters maintained at full coverage.

KPIs

• Knowledge Coverage Score
• Sub-topics owned

Common mistakes

• Confusing word count with coverage
• Ignoring the boring sub-topics competitors own
• Never re-checking as the topic evolves

SearchScore insight