How to Find Content Gaps Using Clarity and Google Search Console

Most content decisions are guesses. You write about topics you think matter. Without data, you do not know if visitors engage with your posts, or what they search for but do not find. This guide shows you how to combine two data sources: Microsoft Clarity (real user behavior) and Google Search Console (search intent). Together they build a data-backed content strategy. Claude automates the analysis. It surfaces gaps, quick wins, and problems worth fixing.

What you need before starting

• A website with Clarity installed (Microsoft Clarity MCP connected to Claude)
• Google Search Console access to your site (optional but recommended)
• Access to Claude (via claude.ai with Clarity MCP enabled, or Claude Desktop if using alternative data sources)
• 30 minutes to set up; 15 minutes per analysis run
• A Google Sheet or spreadsheet to track content recommendations over time

Platform note: Clarity MCP works only in the cloud and works on claude.ai. If you use Claude Desktop, you can still run this workflow with exported data from Clarity and GSC CSV files. The analysis is then manual, not automatic.

Why these two data sources matter

Clarity shows user behavior. It shows which pages people visit, how far they scroll, where they click, and what frustrates them (rage clicks, dead clicks). It answers: “Do people engage?”

Google Search Console shows search intent. It shows what people search for to find you, which searches bring impressions but no clicks (a title or meta problem), and which queries you miss entirely. It answers: “What do people look for?”

Cross-reference these two sources to find:

• Posts with high traffic but low engagement (fix first)
• Search queries that get impressions but no clicks (a meta description or title issue)
• Search intent you capture traffic for but do not serve well
• Topics people search for that you have no content for yet (write next)

Instead of reading raw data for hours, Claude combines both sources. It surfaces recommendations ranked by impact.

The workflow: three data streams

Step 1: Connect Clarity and Google Search Console

Clarity setup:

1. Install Clarity on your website. Copy the tracking code into your header.
2. Give Claude access to Clarity MCP in your Claude.ai integrations.
3. Verify that it works. You must see session data within 1 to 2 hours of traffic.

Clarity MCP pulls:

• Top pages by session count
• Scroll depth (how far visitors scroll on each page)
• Rage clicks and dead clicks (frustration signals)
• Traffic sources (direct, organic, referral, and other sources)

Google Search Console setup:

1. Connect your site. Verify ownership with DNS, an HTML tag, or Google Analytics.
2. Wait 24 to 48 hours for search data to appear.
3. Give Claude access with the Google Search Console tool.

GSC pulls:

• Top 20 queries by impressions and clicks
• Click-through rate (CTR) by query
• Pages with high impressions but low CTR (below 3%): quick wins for title or meta fixes
• Search position (average ranking for each query)

Data freshness: Clarity updates every few hours. GSC data lags by 1 to 3 days. Run this analysis every week or every two weeks for best results.

Step 2: Filter for signal, not noise

Both tools can overwhelm you with data. Claude filters this data down to the signal.

From Clarity:

• Sessions of 20 seconds or more (longer sessions mean more engagement, shorter sessions mean accidental clicks)
• Pages with more than 100 sessions (enough data to show a pattern, not noise)
• Scroll depth below 40% on high-traffic pages (an engagement problem worth an investigation)
• Rage click rate above 5% or dead click rate above 10% (a UX problem signal)

From GSC:

• Top 20 queries by impressions (what people search for most)
• Top 20 queries by clicks (what actually drives traffic)
• Impressions above 10 but CTR below 3% (a clear title or meta improvement opportunity)
• Queries with no matching page slug (a content gap signal)

This filter removes about 80% of the noise. Only insights worth an action remain.

Step 3: Cross-reference against your content library

Claude does not just surface data. It cross-references the signals against your existing content to avoid duplicate recommendations.

Claude maintains a content library of:

• All published posts (title, slug, keywords, publication date, category)
• All pages, guides, case studies, and portfolios (everything on your site)
• An archive of past recommendations (to avoid repeated suggestions)

When Claude finds a GSC query with no matching post, it checks two things. First, does a post on this topic already exist under a different slug? Second, is it too similar to something published last month? This check prevents wasted recommendations and duplicate work.

Three types of recommendations

Fix First: Posts with traffic but low engagement

These are quick wins. You have visitor attention. Your post only needs a restructure or better framing.

Example from placeholder data:

Why fix first: You already have the audience. A restructure or UX fix takes 30 minutes and can improve engagement by 40 to 50%.

Write Next: Content gaps with data backing

These are topics people search for but you do not rank for yet. They also include topics adjacent to your high-engagement posts.

Example:

1. “Comparing ERC and Horizon Europe grants for materials scientists”

   • Why: GSC shows that “ERC vs Horizon Europe” gets 28 impressions a month and 0 clicks. You rank, but visitors do not click your link. This points to a title or meta issue, plus topic confusion.
   • Angle: A side-by-side comparison with a decision tree (which program fits your goals?)
   • Data-backed alternative: Your top traffic page is the ERC guide. A comparison post clusters related search intent.

2. “How to write a compelling research impact statement”

   • Why: GSC shows that “research impact statement examples” gets 34 impressions and 0 clicks. You do not rank yet. Search intent is clear: people want examples. Your grant-writing posts mention impact but do not cover it in depth.
   • Angle: A template, examples, and common mistakes
   • Avoids duplicate: The nearest content is “Grant Writing Fundamentals” (published 60 days ago). This topic is distinct because it focuses on impact specifically.

3. “Funding for interdisciplinary research in Europe”

   • Why: Clarity shows that your “Materials Science Funding” post ranks in the top 5 by sessions (847) but has only 28% scroll depth. Visitors want cross-disciplinary funding, but your post covers materials science only. A new post serves this adjacent audience.
   • Angle: European funders that accept interdisciplinary teams (EIC, SNF, NWO, Villum)
   • Avoids duplicate: Distinct from the materials-only scope.

The pattern: these recommendations come from real user behavior and real search data, not guesswork.

Remove or Archive: Pages draining resources with no traffic

This case is less common but still important. If a page gets fewer than 10 sessions a month, ranks nowhere in GSC, and has had no update in 12 or more months, consider two options. Archive it (keep it live but lower its priority), or redirect it to a more relevant post.

The clever architecture: approval layers

Claude does not just list recommendations. It combines three layers:

1. Behavior layer (Clarity): What works, what frustrates users, where engagement drops
2. Intent layer (GSC): What people search for, how well your titles match their queries, where you miss opportunities
3. Content layer: What you have already published, when you published it, how similar new topics are, what is overdue for a refresh

Claude surfaces a recommendation only when all three layers align. This alignment filters out false signals and noise.

Example: “Write a post about X” appears only if all three checks pass.

• Clarity shows that adjacent content is popular (behavior signal)
• GSC shows that people search for X (intent signal)
• Your content library shows no existing post that covers X (duplication check)

Making this actionable

Once Claude generates recommendations, sort them by effort and impact.

High impact, low effort (do first):

• Fix the title or meta on high-impression, low-CTR posts (30 minutes per post)
• Restructure low-scroll-depth posts (1 to 2 hours per post)
• Create a simple comparison post when two related topics get search traffic (2 to 3 hours)

High impact, high effort (do next):

• Write a new guide that covers a clear content gap (4 to 6 hours)
• Cluster and expand a category with multiple quick-reference posts (5 to 8 hours per cluster)

Low impact or low urgency (do later or skip):

• Archive pages with fewer than 10 sessions a month
• Update dated posts that still perform (maintenance, not new creation)

Troubleshooting

Clarity shows sessions, but GSC shows no impressions for those pages
Clarity tracks all traffic (direct, apps, internal links). GSC tracks only organic search traffic. This gap is normal. Focus GSC recommendations on search-driven pages. Focus Clarity recommendations on behavioral engagement, regardless of traffic source.

Claude’s recommendations feel too obvious or unhelpful
Your content library can be outdated. Ask Claude to rebuild it: “Refresh the content library, then re-run the analysis.” This command re-scans all published posts and catches recent work the library missed.

You disagree with a recommendation, or think it duplicates something
Flag it. Ask Claude to re-check: “Does post X already cover this topic? Should we expand it instead of writing a new one?” The approval layer works both ways. You can challenge a recommendation, and Claude reconsiders it.

GSC shows high impressions but low clicks, and the title or meta looks fine
The problem can be positioning. Your snippet can fail to answer the searcher’s intent. Try a different angle in your title, or rewrite the description to match search intent more closely. Clarity and GSC alone do not tell you intent. They tell you a gap exists. You must fill it strategically.

What you can do now

Once you run your first analysis:

• You have a ranked list of “fix first” posts. Pick one and restructure it.
• You have 3 to 5 “write next” recommendations with data backing. Choose one and draft it.
• You know which topics underperform despite high traffic. Prioritize those for a refresh.
• You have baseline metrics to compare against. Run this analysis again in 4 weeks to measure the impact of your changes.

Example: Fix the top “Fix First” post (30 minutes). Re-run the analysis in 2 weeks. If scroll depth improves and bounce rate drops, you have validated the approach. This process becomes your content maintenance rhythm.

Key principles for best results

1. Data beats opinion. If data contradicts your assumptions, trust the data first. Your intuition about what people want is often wrong.

2. The approval layer prevents hallucination. Claude recommends content only when it aligns across behavior, intent, and deduplication checks. If a recommendation does not pass all checks, Claude does not suggest it.

3. Run the analysis regularly. Analysis every week or every two weeks catches trends early. Monthly is too infrequent. Daily is overkill.

4. Fix before you write. A restructure of an underperforming post takes 30 minutes. It can have more impact than a new post. Prioritize fixes in your first run.

5. Track your changes. Keep a simple log of what you fixed, when you fixed it, and how the metrics changed 2 to 4 weeks later. This log builds your instinct over time.

6. Content clusters matter. If multiple high-traffic pages cover “funding for researchers in Europe,” bundle them into a series or guide. Clarity and GSC signal cluster opportunities if you look for them.

Want more guides like this? Browse all AI Guides or get in touch →