TL;DR Summary:
Business-First Research: Semrush’s workflow pairs Claude with live data so keyword research starts from your product, audience, competitors, and existing pages instead of generic industry lists.Gap Hunting Wins: The biggest opportunities come from competitor keyword gaps, Search Console queries already ranking in positions 8 to 20, and customer-language phrases pulled from reviews, Reddit, and sales calls.Relevance Beats Volume: The guide’s core message is simple: prioritize keywords by business fit first, then check volume and difficulty, because even zero-volume terms can be valuable if they match real intent.How do you find keywords your competitors haven’t already claimed? Semrush published a workflow on September 10, 2026, showing how marketers can pair Claude with its MCP connector to run a research process built around your own business, not generic industry data. Keyword research with Claude and Semrush matters right now because standard tools tend to hand every business in a category the same list, leaving little room to stand out.
How Keyword Research With Claude and Semrush Starts With a Claude Project
The process begins inside a Claude “project,” a workspace where you save instructions and files that Claude references in every chat. You add a business context file answering five questions: what you sell, who buys it, what problems you solve, who your competitors are, and any rules Claude should follow. You also upload a list of your website URLs, so Claude can tell whether a keyword needs a new page or an update to one you already have. This setup step matters because it turns keyword research with Claude and Semrush into a process built around your specific business, not a blank search.
Connecting Semrush MCP and Search Console Data
Once your project is set up, you connect the Semrush MCP connector through Claude’s “Connectors” menu. MCP stands for Model Context Protocol, a way for Claude to pull live data from Semrush during a chat instead of you copying numbers back and forth. You then export a Queries.csv file from Google Search Console under “Performance” and “Search results,” and upload it to Claude. Semrush recommends refreshing this file monthly so Claude works from current data each time you run the analysis.
Finding Keyword Gaps and Easy Wins
With the project connected, you ask Claude to run a keyword gap analysis, comparing your domain against named competitors one at a time. You can set filters like a minimum search volume of 100 and a keyword difficulty range of 0 to 49. Separately, you ask Claude to scan your Search Console data for queries ranking in positions 8 to 20, since Google already treats your page as relevant for those terms. Semrush’s guide points to SmartSuite as an example: reviews on G2 note that Trello lacks task dependency features, and SmartSuite published content on that exact topic, which now appears in an AI Overview for related searches.
Turning Customer Language Into Keyword Ideas
A distinct part of this keyword research with Claude and Semrush workflow involves feeding Claude real customer language: competitor reviews from sites like G2 or Capterra, Reddit threads, and sales call transcripts. Claude groups this text into themes and turns it into search queries a real person might type. One example from the guide: Claude suggested “best community for new freelancers,” which returned zero volume in Semrush. A manual variation, “best communities for freelancers,” returned 20 monthly searches. This shows why zero volume on an exact phrase doesn’t mean zero demand.
Prioritizing Keywords by Business Relevance
The final step asks Claude to combine every list, keyword gaps, Search Console wins, and validated customer-language terms, into one CSV with columns for search volume, intent, difficulty, topic cluster, and reasoning. Semrush’s guide is clear that terms should be ranked primarily by business relevance, then by volume and difficulty. Keywords with no Semrush data go into a separate CSV for manual review, since a term like “how to find freelance clients consistently” can still be worth targeting even with no measured search volume, depending on what it’s tied to. This is precisely the kind of judgment call that platforms like WriterZen are built to support, since they focus on surfacing keyword opportunities based on how closely they fit your business rather than ranking them by search volume alone.
The main thing to take from this process is that raw search volume shouldn’t drive your decisions on its own. Look closely at the customer language behind each keyword idea, from reviews, Reddit threads, or sales calls, before deciding whether it deserves a page. Refresh your Search Console export regularly so your keyword research with Claude and Semrush stays based on current data. If you want a platform that builds this kind of relevance-first keyword prioritization directly into its workflow, tools like WriterZen combine keyword clustering with revenue forecasting so you can judge opportunities beyond just volume and difficulty.
