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See how to combine Semrush, Google Search Console, Google Analytics, and Claude to prioritize the content opportunities that matter most. #
You can publish consistently, follow SEO best practices, and still watch competitors outrank you. More often than not, the problem isn’t content quality. It’s content coverage. Your competitors are answering questions your audience is asking, and you’re not in the conversation yet.
That’s where a content gap analysis comes in. It helps you identify topics your competitors rank for that you don’t, then decide which opportunities are actually worth pursuing.
Finding the gaps isn’t the hard part. SEO tools make that easy. The challenge is making sense of thousands of keywords spread across multiple reports and deciding what deserves your attention first.
This workflow shows how to combine competitor data, first-party search data, and AI to prioritize content opportunities and build a roadmap based on business impact, not just search volume.
Bring your SEO data together before you analyze it #
In this workflow, I use Semrush to identify competitive opportunities, Google Search Console to validate where my site is already showing signs of authority, and Google Analytics to add business context. Claude then brings those datasets together, grouping related opportunities, identifying patterns, and helping prioritize what belongs on the content roadmap.
There are two ways to follow this process:
- You can export reports directly from the platforms and upload them to Claude.
- Or, if you’ve connected those platforms through MCP (Model Context Protocol, a standard that allows AI models to connect securely to your data sources), Claude can pull the data directly without manual exports. The workflow changes, but the analysis doesn’t.
Below, I’ll walk through the process I use to turn a pile of SEO data into a prioritized content plan.
Step 1: Choose the right competitors #
A content gap analysis is only as good as the competitors you compare yourself against. That sounds obvious, but it’s one of the easiest mistakes to make.
If you compare your site to Amazon, Reddit, or Wikipedia, you’ll end up with thousands of keyword “opportunities” that were never realistic to begin with. The goal isn’t to find every site ranking for your target keywords. It’s to find businesses competing for the same audience. I typically start with Semrush’s Organic Competitors report. Rather than relying on a list of known competitors, this report identifies domains competing for many of the same keywords. From there, I narrow the list to three to five sites that closely match your business and target audience.
Don’t be surprised if a few familiar names don’t make the cut. Business competitors and organic search competitors aren’t always the same.
You should also filter out sites that will skew the analysis, including:
- Large marketplaces like Amazon
- Community-driven sites like Reddit or Quora
- Reference sites like Wikipedia
- Local directories or review sites
- Publishers that don’t directly compete with your business
There are exceptions. If you’re a publisher, comparing yourself against other editorial sites makes perfect sense. The key is choosing competitors that create the type of content you’re realistically trying to outperform.
Finally, sanity-check the list with stakeholders. Your sales or product teams may point out competitors that don’t appear in Semrush because they’re newer or competing in a strategically important niche.
Once you’ve settled on your competitors, you’re ready to find the gaps that matter most.
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Step 2: Gather and prepare your data #
With your competitor list finalized, it’s time to collect the data Claude will analyze. Whether you’re up exports or connecting through MCP, the goal is the same: bring together competitive rankings, your site’s search performance, and engagement data so you can separate meaningful opportunities from noisy keyword lists.
I like to pull data from three sources.
Semrush: Find the gaps
Start with Semrush’s Keyword Gap tool using the competitors you selected in Step 1.
Pay close attention to three buckets:
Competitors rank, you don’t. These are your biggest content opportunities and often point to missing topics or content hubs.You rank, but competitors rank higher. Focus on keywords where you’re already on Page 1 or 2. These are often quicker wins because Google already associates your site with the topic.You rank, competitors don’t. These are your strengths. Don’t ignore them. They highlight topics where you already have an advantage and should continue investing.
Google Search Console: Validate the opportunity
Next, check Google Search Console before assuming every missing keyword deserves a new page.
For example, Semrush may show that you don’t rank for a particular keyword, but GSC could reveal that you’re already receiving impressions for closely related queries. That tells you Google has started associating your site with the topic, even if rankings haven’t caught up yet. Those “almost there” topics often deserve a higher priority than starting from scratch.
Look for:
- Queries with high impressions but average positions between 8 and 20.
- Existing pages ranking for related terms.
- Long-tail queries that reveal additional search intent.
Google Analytics: Add business context
Search volume is only part of the story. Engagement metrics help answer an equally important question: If you improve visibility for this topic, is it likely to support your business goals?
Review metrics such as:
- Organic sessions.
- Engagement rate.
- Average engagement time.
- Key events or conversions.
- Landing page performance.
If a related content hub already drives engaged visitors or conversions, expanding that topic may be a smarter investment than chasing a completely new keyword with higher search volume.
Clean your data before handing it over to Claude
If you’re manually down the data and up it to Claude, I recommend cleaning it first. Claude is excellent at finding patterns, but it can only work with the data you give it. A cleaner dataset leads to cleaner topic clusters and better recommendations. Remove:
- Duplicate keywords.
- Competitor-branded terms.
- Careers, login, and support queries.
- Locations or product lines outside your business.
- Keywords with clearly different search intent.
- High-intent commercial keywords that are too broad to compete for. (For example, generic industry terms that don’t match your business model.)
Two ways to gather your data:
Manual workflow: Export Keyword Gap data from Semrush, along with query data from Google Search Console and landing page performance data from Google Analytics, then upload the files to Claude.Connected workflow (MCP): Ask Claude to retrieve the Keyword Gap report, GSC query data, and GA4 landing page metrics directly from your connected accounts. You can then move straight into the analysis without down CSVs.
Step 3: Ask Claude to find the story in your data #
At this point, you should have a clean dataset that combines competitive keyword gaps, Search Console performance, and Google Analytics data.
Now comes the fun part.
Instead of scrolling through thousands of rows looking for patterns, ask Claude to organize the data into something you can actually build a strategy around.
The mistake I see most often is asking AI to “cluster these keywords.”
You’ll certainly get clusters back, but they’ll usually be based on keyword similarity alone. That’s useful, but it doesn’t tell you what to do next. Instead, ask Claude to think like an SEO strategist.
Provide context about your business, including:
- Your products or services
- Your target audience
- Your primary business goals
- Any content priorities or constraints
- The exported reports or connected data from Semrush, GSC, and Google Analytics
Then ask Claude to organize opportunities by factors such as:
- Search intent
- Funnel stage
- Business relevance
- Existing authority signals from GSC
- User engagement from GA4
- Recommended content format
- Internal linking opportunities
Rather than returning a spreadsheet of grouped keywords, Claude should produce topic clusters with a clear recommendation for each one. I’ve provided an example below.
For example, one cluster might be labeled Technical SEO Audits and include:
- Supporting keywords.
- Estimated opportunity.
- Existing pages that could be updated.
- Whether a new page is needed.
- Internal linking recommendations.
- Priority score.
- Reasoning behind the recommendation.
Another cluster might reveal that several competitor keywords can be addressed by expanding an existing guide instead of publishing three separate articles. That’s the kind of insight that’s difficult to spot manually but easy for AI to surface.
Separate quick wins from long-term investments
Not every opportunity belongs on the same roadmap. As part of your prompt, ask Claude to classify each cluster into categories such as:
Quick wins: Existing pages that can be refreshed, expanded, or better optimized.New content opportunities: Topics that deserve dedicated content because you have little or no visibility.Authority plays: Larger subject areas that may require multiple pieces of content and ongoing investment to compete effectively.
This simple step helps you move from an overwhelming keyword list to a roadmap with both short-term wins and long-term initiatives.
Don’t skip the human review
Claude can organize information remarkably well, but it doesn’t know your business the way you do.
Before moving on, ask questions such as:
- Does this topic support our business goals?
- Are multiple search intents being combined into one cluster?
- Do we already have content that could satisfy this need?
- Is this a realistic opportunity given our authority and resources?
- Would I actually assign this topic to a writer?
If the answer is “no,” refine the cluster or remove it. The goal isn’t to accept every recommendation. It’s to spend less time organizing data and more time making strategic decisions.
Prompt template: I’ll share the full prompt I use at the end of this article, but the biggest takeaway is this: Don’t ask Claude to organize keywords. Ask it to recommend what your content strategy should be based on the data you’ve provided.
Step 4: Score and prioritize the opportunities #
Once Claude has grouped your keywords into topic clusters, the next step is deciding what deserves your attention first.
This is where many content gap analyses fall apart. Teams naturally gravitate toward the biggest search volumes, but volume is only one piece of the puzzle. A topic that attracts qualified visitors and supports your business goals is often a better investment than a high-volume keyword that’s difficult to rank for or unlikely to convert.
I like to score each opportunity across several criteria before building a roadmap.
Business relevance
Start by asking a simple question: If this content performs well, does it help the business?
Topics that align with your products, services, or customer journey should receive more weight than informational topics with little commercial value.
Existing authority
Next, look at the signals from Google Search Console.
If your site already earns impressions or ranks on the second page for related queries, Google has likely established some level of topical authority. Improving an existing page or expanding a content hub may produce results much faster than starting from scratch.
Search demand
Search volume matters, but it shouldn’t dominate the scoring model.
A collection of related long-tail queries with moderate demand can sometimes generate more qualified traffic than a single broad keyword.
Ranking difficulty
Review the current search results before committing to a topic.
Ask questions such as:
- Are authoritative brands dominating the first page?
- Is the intent primarily informational, commercial, or transactional?
- What types of content are ranking?
- Can you realistically create something more useful or complete?
This quick reality check can save your team from chasing opportunities that aren’t practical.
Estimated effort
Finally, consider the work involved. Some opportunities require a light refresh of an existing article. Others call for a new content hub supported by multiple pages.
Both can be worthwhile, but they shouldn’t carry the same priority if resources are limited.
Let Claude apply the framework
Once you’ve defined your scoring criteria, Claude can evaluate every topic cluster consistently.
For example, you might ask Claude to score each opportunity on a five-point scale for:
- Business relevance.
- Existing authority.
- Search demand.
- Ranking difficulty.
- Content effort.
Then have it calculate an overall priority score and explain why each recommendation received that score. The explanation is just as valuable as the number. If you disagree with a recommendation, you can adjust the weighting, add additional business context, and ask Claude to score the opportunities again.
By the end of this step, you should have more than a list of content ideas. You should have a prioritized content strategy that clearly identifies what to tackle next, what can wait, and what isn’t worth pursuing. I’ve provided an example of Claude’s output below.
Step 5: Turn priorities into page-level recommendations #
Once you’ve prioritized your opportunities, the next step is figuring out exactly what to change.
Rather than handing your team a ranked list of topics, ask Claude to generate page-level recommendations for your highest-priority opportunities. This is where connected data becomes especially valuable.
Because Claude has access to your Semrush research, Google Search Console performance, Google Analytics metrics, and your prioritization framework, it can evaluate each page in context instead of treating every recommendation the same.
For each priority page, I ask Claude to produce a recommendation that includes:
- Why the page was selected.
- The primary keyword cluster.
- Current rankings and impression data.
- Supporting evidence from GSC and competitor research.
- Recommended updates.
- Estimated effort.
- Expected impact.
- Priority level.
One of the biggest advantages of this approach is validation.
Before recommending a refresh, Claude can compare URL-level Search Console data against the original analysis. Sometimes what looks like a great opportunity turns out to be misleading. A keyword may have inflated impression counts, a URL could have been mislabeled in an export, or the page simply isn’t as close to ranking as it first appeared.
Catching those issues before assigning work can save hours of unnecessary effort.
The recommendations also make conversations with stakeholders much easier. Instead of saying, “We should update this page,” you can point to the supporting data, explain why it’s a priority, estimate the effort involved, and tie the recommendation back to your overall content strategy.
Think of these recommendations as implementation plans rather than content briefs. They’re designed to help your SEO and content teams understand what should change, why it matters, and where to focus first. Writers can then use those recommendations to create or update content with confidence.
Below is an example of a page-level refresh brief generated by Claude.
Step 6: Measure whether the gap is closing #
Publishing your content isn’t the finish line. It’s the start of the next round of analysis.
I begin with Google Search Console, tracking whether target queries are gaining impressions, improving in average position, and generating more clicks. When I refresh an existing page, I compare performance before and after the update to see whether the changes actually moved the needle.
Next, I look at Google Analytics. Better rankings don’t always translate into better business outcomes, so I review organic traffic alongside engagement and conversion metrics. If an updated page attracts more visitors but fails to keep them engaged or contribute to conversions, it’s probably time for another round of optimization.
If you’re using Claude through MCP, you can also ask it to compare performance over time and summarize what changed. For example:
- Which refreshed pages improved the most?
- Which content clusters gained the most visibility?
- Which recommendations drove the strongest business results?
- Which opportunities still need attention?
Instead of comparing reports month after month, Claude can quickly surface significant changes and point you toward the pages that deserve your attention.
Finally, don’t treat content gap analysis as a one-time exercise. Competitors publish new content, search behavior shifts, and your own authority evolves. I recommend repeating this workflow every quarter, or more often in fast-moving industries, to find new opportunities and stay ahead of your competition.
The tools will continue to advance, but a repeatable workflow is what creates the advantage.
Build a repeatable content gap analysis process #
A content gap analysis helps you prioritize the opportunities worth pursuing instead of chasing every possible keyword.
Semrush helps uncover competitive gaps. Google Search Console shows where you already have momentum. Google Analytics adds the business context that rankings alone can’t provide. Claude brings those datasets together, helping you identify patterns, prioritize opportunities, and create actionable recommendations in a fraction of the time it would take manually.
Whether you upload reports or connect your tools through MCP, the workflow stays the same. Gather the right data, validate the opportunities, let AI organize the information, and apply your own expertise to decide what comes next. That’s the part AI can’t replace.
The biggest advantage isn’t having better prompts or faster analysis. It’s having a repeatable process that helps your team make smarter content decisions every quarter.
Prompt template: Build a prioritized content gap roadmap #
Here’s the prompt I promised. Use it after you’ve gathered your data, whether you’ve uploaded exports from Semrush, Google Search Console, and Google Analytics or connected those tools to Claude through MCP.
“You are an experienced SEO strategist helping me perform a content gap analysis.
I’ll either provide exported reports from Semrush, Google Search Console, and Google Analytics, or you’ll access those tools through connected MCP integrations.
My goal is to identify the highest-impact content opportunities based on competitor visibility, existing authority, business value, and implementation effort.
Here’s my business context:
– Company:– Industry:– Products/services:– Target audience:– Primary business goals:– Geographic focus:– Any strategic priorities or constraints:– Tone of voice: [Insert brand voice adjectives here (e.g., authoritative, conversational, technical)].
Using the available data, complete the following tasks.
- Identify content gaps
Organize keywords into these categories:– Competitors rank and we don’t.– We rank below competitors.– We rank and competitors don’t.
Highlight any content gaps, opportunities to consolidate pages, or keyword cannibalization issues.
- Validate the opportunities
Use Google Search Console data to determine:– Which topics already receive impressions.– Which pages rank between positions 8 and 20.– Which existing URLs have the strongest chance of improving with optimization.
Use Google Analytics data to determine:– Which pages drive meaningful engagement.– Which pages contribute to conversions.– Which content hubs are worth expanding.
- Create strategic topic clusters
Group related opportunities by:– Search intent– Business relevance– Funnel stage– Recommended content type– Internal linking opportunities
Don’t cluster based only on keyword similarity. Focus on topics that should become part of the same content strategy.
- Prioritize every opportunity
Score each topic cluster using:– Business relevance– Existing authority– Search demand– Ranking difficulty– Estimated effort
Assign each opportunity a priority (High, Medium, Low) and explain why.
Separate recommendations into:– Quick wins– New content opportunities– Long-term authority investments
- Recommend next steps
For every high-priority opportunity, recommend whether we should:– Refresh an existing page– Consolidate multiple pages– Create a new page– Build a pillar page with supporting content Include supporting evidence for every recommendation.
- Deliver the results
Create:– An executive summary– Prioritized topic clusters– A scored opportunity table– Page-level recommendations for the highest-priority URLs– A phased implementation roadmap (30, 60, and 90+ days)
If you find conflicting data between Semrush, Google Search Console, and Google Analytics, explain the discrepancy and recommend which source should guide the decision. The output should both be HTML and a Google Sheet. Before presenting your final recommendations, validate your own analysis. If reviewing Search Console or Analytics data changes your original recommendation, explain why and update your prioritization accordingly.”
This prompt is a starting point. Add any business context, editorial guidelines, or scoring criteria that are unique to your organization. The more context you give Claude, the more useful and actionable its recommendations will be.
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