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Striking Distance Query Analysis

Most query analysis sorts by clicks, which just tells you what already works. The queries that move fastest are ones Google already considers you relevant for but ranks below the fold. The obvious approach - chasing high-volume head terms you rank 40th for - fails because those need links and authority, not a title tag rewrite.

CATEGORY
Analytics & reporting
FORMAT
striking-distance-query-analysis.md
STEPS
6
PRICE
Free - no account
WHEN TO REACH FOR THIS

Use when you have Search Console data and need to decide which queries are worth working on this month.

The skill file

striking-distance-query-analysis.md 
---
name: striking-distance-query-analysis
description: Use when you have Search Console data and need to decide which queries are worth working on this month.
---

# Striking Distance Query Analysis

Most query analysis sorts by clicks, which just tells you what already works. The queries that move fastest are ones Google already considers you relevant for but ranks below the fold. The obvious approach - chasing high-volume head terms you rank 40th for - fails because those need links and authority, not a title tag rewrite.

## What you need first

- Search Console query export, 3 months minimum, page-level not site-level
- the URL each query currently ranks with
- current title and H1 for those URLs

## Method

1. Export queries with page dimension attached. Site-level query data hides the case where three pages compete for one query, which is the single most common cause of a stuck position 11.
2. Filter to average position between 8 and 20. Below 8 you are fighting for small CTR gains, above 20 the gap is usually authority, not on-page.
3. Within that band, sort by impressions descending. Impressions are Google telling you it already thinks the page belongs in the result set.
4. Check each query for cannibalisation: if two or more URLs have impressions for it, fix that first and re-measure before touching anything else.
5. For the survivors, check whether the exact query phrasing appears in the title, H1 and first 100 words. Position 11-15 with the phrase missing from the title is the highest-yield fix in SEO.
6. Cap the batch at 10-15 URLs so you can attribute movement. Change 60 pages at once and you will never know which edit worked.

## What this produces

A ranked shortlist of 10-15 URLs with the specific query each should target and the on-page change to make.

## Where this goes wrong

- Using average position as if it were a real rank - it is an impression-weighted mean across devices and countries, so a UK page with stray US impressions looks worse than it is
- Working the 3-month average when the last 28 days tell a different story, so you optimize a query that already died
- Ignoring that Search Console truncates the query list, so long-tail volume is systematically understated

---

From the QuQi skill library - https://www.quqi.io/skills/striking-distance-query-analysis
Free to download · no account, no email

What you need first

  • Search Console query export, 3 months minimum, page-level not site-level
  • the URL each query currently ranks with
  • current title and H1 for those URLs

Method

  1. 01 Export queries with page dimension attached. Site-level query data hides the case where three pages compete for one query, which is the single most common cause of a stuck position 11.
  2. 02 Filter to average position between 8 and 20. Below 8 you are fighting for small CTR gains, above 20 the gap is usually authority, not on-page.
  3. 03 Within that band, sort by impressions descending. Impressions are Google telling you it already thinks the page belongs in the result set.
  4. 04 Check each query for cannibalisation: if two or more URLs have impressions for it, fix that first and re-measure before touching anything else.
  5. 05 For the survivors, check whether the exact query phrasing appears in the title, H1 and first 100 words. Position 11-15 with the phrase missing from the title is the highest-yield fix in SEO.
  6. 06 Cap the batch at 10-15 URLs so you can attribute movement. Change 60 pages at once and you will never know which edit worked.

What this produces

A ranked shortlist of 10-15 URLs with the specific query each should target and the on-page change to make.

Where this goes wrong

  • Using average position as if it were a real rank - it is an impression-weighted mean across devices and countries, so a UK page with stray US impressions looks worse than it is
  • Working the 3-month average when the last 28 days tell a different story, so you optimize a query that already died
  • Ignoring that Search Console truncates the query list, so long-tail volume is systematically understated

Use this skill in your own AI

The download is a plain markdown file with the name and trigger in its frontmatter. Where an assistant supports skills it can load itself, that frontmatter is what it reads to decide this one applies.

Claude Code Save it as ~/.claude/skills/striking-distance-query-analysis/SKILL.md and Claude loads it on its own when what you are doing matches the trigger line. Put it in .claude/skills inside a project instead if the whole team should have it.
Claude Upload the file in the skills section of your settings. Once it is there it applies itself in any conversation where the trigger fits, so you do not have to remember it exists.
ChatGPT There is no skills format to install into, so paste the file contents into a Project instruction or a Custom GPT instead. It then applies to every chat in that project rather than only the one you paste it into.
Anything else Paste the markdown into the chat before your question. It works in any assistant, it just has to be pasted again each time.

Questions about this skill

When is striking distance analysis the right method rather than sorting queries by clicks?

When Search Console already shows impressions and you have to choose what to work on this month. Sorting by clicks only describes what already works, and chasing a head term you rank fortieth for needs links and authority rather than a title rewrite. The 8 to 20 band is where on-page wording still decides the position, which is the part you can change now.

What do I need in hand before starting, and what happens if I start without it?

Three months of queries exported with the page dimension attached, plus the current title and H1 for each ranking URL. Site-level query data hides the case where three of your pages compete for one query, which is the most common reason a position sits at eleven and will not move. Start without the page dimension and you will rewrite titles on pages that were never the problem.

What do I end up with, and which part of it actually gets used?

A shortlist of ten to fifteen URLs, each with the query it should target and the specific on-page change to make. The batch cap is the part that does the work: it keeps the change set small enough that you can attribute movement six weeks later. Sixty pages edited in one week gives you a graph nobody can read and no repeatable finding.

What is the mistake that most often ruins this, and what does it cost?

Reading average position as a rank. It is an impression-weighted mean across devices and countries, so a UK page picking up stray US impressions looks worse than it is and gets work it never needed. Filter to one country and one device first. The related waste is working a three month average on a query that died four weeks ago.

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