How to run a query gap analysis that finds the pages you are missing

Executive summary

A query gap analysis maps the demand in your category against your current coverage so you can prioritise the content that will move the most traffic. It replaces topic brainstorming with evidence, and it feeds directly into your editorial calendar.

What this helps you decide

Whether a missing query justifies a new page, an addition to an existing page, or nothing at all.

Business problem

Most sites rank for a fraction of the queries their category generates, leaking demand to competitors. Teams guess at new topics instead of measuring the gap systematically.

Step-by-step process

1. 1
   Assemble the demand set

   Pull queries from Search Console, keyword tools, autocomplete, People Also Ask, and real customer questions. Deduplicate and cluster near-identical phrasings into single intents.

2. 2
   Map current coverage

   Crawl your site and tag each URL with the primary intent it serves. Match every demand cluster to a URL, a weak URL, or nothing.

3. 3
   Classify each gap

   Label clusters as uncovered, partially covered, or cannibalised. Uncovered clusters with commercial or decision-stage intent rank highest.

4. 4
   Score the opportunity

   Weight each gap by search volume, business value, and how realistically you can rank given your current authority. Use the Content Opportunity Matrix to sort quick wins from long plays.

5. 5
   Decide the vehicle

   For each priority gap decide: new standalone page, section on an existing page, or FAQ entry. Avoid creating a thin page where a paragraph would do.

6. 6
   Sequence and brief

   Turn the top gaps into a ranked backlog and write a content brief for each so writers know the exact intent to satisfy.

Worked example

Checklist

• Demand set pulled from at least three independent sources
• Every demand cluster mapped to a URL or marked uncovered
• Gaps scored by volume, value and winnability
• Each priority gap assigned a vehicle (new page vs addition)
• Backlog ranked and top items briefed

Common mistakes

• Treating every keyword variation as a separate gap instead of clustering by intent
• Prioritising by search volume alone and ignoring business value or winnability
• Creating a new thin page for a query that belonged inside an existing article

30-minute experiment

KPIs to track

• Number of priority gaps closed per quarter
• Impressions gained on newly covered clusters
• Share of category queries with a matching page

FAQs