I Asked Brass-SEO for Blog Post Ideas
I recently read a post about using Google Search Console to find blog post ideas. It laid out five methods: filter for question queries, find keywords without dedicated pages, spot high-impression low-click gaps, identify striking-distance terms, and flag declining content that needs a refresh.
Every method made sense. Every method also required navigating GSC's Performance report, applying filters, switching between the Queries and Pages tabs, and cross-referencing numbers manually. I have done this before. It takes about an hour if you are thorough, and most of it is clicking around rather than thinking.
So I tried doing the same thing in Brass-SEO instead. I opened the chat, typed plain English questions, and let the AI pull the data for me. Here is what that looked like.
Note: The examples below are illustrative — they show the kind of results and interactions you would see with your own connected data, not a specific site's real numbers.
Quick Navigation
- Setting Up
- Question 1: What Should I Write About?
- Question 2: What Questions Are People Asking?
- Question 3: Where Am I Close to Page 1?
- Question 4: Which Pages Need Better Titles?
- Question 5: What Used to Work That Stopped Working?
- What I Got in 10 Minutes
- Frequently Asked Questions
Setting Up
If you have already connected your Google Search Console and Google Analytics 4 to Brass-SEO, there is nothing to set up. You open the dashboard, pick your site from the property selector, and start typing.
If you have not connected yet, the process takes about two minutes. Brass-SEO walks you through connecting both Google accounts with OAuth — you click a button, authorize read-only access, and you are done. Both GSC and GA4 are required because the AI cross-references both data sources to give you the full picture.
Once connected, you just talk to it.
Question 1: What Should I Write About?
I started broad: "What should my next blog post be about?"
The AI did not give a vague answer. It pulled my GSC query data, looked at what keywords were bringing impressions but not clicks, checked which topics I had existing pages for and which I did not, and came back with specific suggestions.
It found a handful of queries where my site was appearing in search results but had no dedicated page targeting those terms. Each one was a content gap — people searching for something related to my business, Google showing my site, but no page that directly answered the query.
The AI listed them with the impression count and average position for each, and explained which ones had the most potential based on search volume and how close I already was to ranking.
In GSC, finding these would have meant sorting by impressions, clicking each query individually, checking which page was ranking, and deciding whether that page was a good match. The AI did all of that in one pass.
Question 2: What Questions Are People Asking?
Next I tried: "What questions are people searching that lead to my site?"
This is Method 1 from the GSC guide — filtering for queries that start with "how," "what," "why," and so on. In GSC, you would add a regex filter or search for each question word individually.
The AI scanned the query data and pulled out question-format queries automatically. It grouped them loosely by topic and flagged which ones I already had content for and which ones I did not.
A few of the questions surprised me. People were searching for things adjacent to my business that I had never thought to write about — the kind of long-tail, specific queries that make excellent blog posts because they match exactly what the searcher needs. The same kind of opportunity we wrote about in how a long-tail keyword became a blog post.
For each question without a dedicated page, the AI suggested what the blog post should cover and which existing pages to link it to for internal linking.
Question 3: Where Am I Close to Page 1?
Then I asked: "What keywords am I ranking on page 2 for that I could push to page 1?"
This is the striking-distance keywords method — positions 8 through 20 where you are close but not quite visible to most searchers.
The AI pulled keywords in that range, sorted by impressions, and for each one told me:
- The current average position
- How many impressions it was getting
- Which page was ranking for it
- Whether the page was a strong match for the query or a tangential one
For the tangential matches, it recommended writing a new dedicated post. For the strong matches, it suggested specific improvements — update the title tag, add more depth to the section covering that topic, or add internal links from other pages.
This is the analysis that would take the longest in GSC because you have to click each keyword, check the ranking page, evaluate the match, and decide what to do. The AI collapsed all of that into a single answer.
Question 4: Which Pages Need Better Titles?
I followed up: "Which of my pages have high impressions but almost no clicks?"
This targets the CTR gap — pages Google shows to lots of people but nobody clicks. Usually that means the title or meta description does not match what the searcher expects.
The AI found several pages with this pattern. For each one, it explained why the CTR might be low (generic title, missing keyword, title does not match the query intent) and suggested a revised title.
One page had over a thousand monthly impressions and a CTR under 1%. The AI pointed out that the page was ranking for a specific question-format query, but the title was generic — something like "Our Services" instead of a title that answers the question. It drafted a replacement title that included the keyword and matched the search intent.
I had seen this page in my GSC data before but never connected the dots between the high impressions, the low CTR, and the title being the bottleneck. The AI made that connection immediately.
Question 5: What Used to Work That Stopped Working?
Last one: "Are any of my blog posts losing traffic compared to last quarter?"
The AI compared performance across time periods and identified posts where clicks or impressions had dropped. For each declining post, it pulled the keywords that were losing position and suggested what might be happening:
- One post had outdated information (referenced a year-old statistic)
- Another was being outranked by a competitor's newer, more comprehensive version
- A third had lost internal links when I reorganized my site navigation
For the outdated post, it suggested specific sections to update. For the outranked post, it recommended expanding the content and improving the title. For the one with lost internal links, it identified which pages should link back to it.
This kind of time-comparison analysis is possible in GSC but tedious — you have to toggle the date comparison, check each page individually, then switch to the Queries tab for each page to see what changed. The AI handled the cross-referencing automatically.
What I Got in 10 Minutes
Five questions. About 10 minutes of conversation. Here is what I walked away with:
- 3 new blog post ideas backed by real query data, with suggested titles and outlines
- 2 title tag rewrites for pages with high impressions and low CTR
- 1 content refresh for a declining post, with specific sections to update
- 4 internal linking opportunities connecting new content to existing pages
- A prioritized list — the AI ranked the opportunities by potential impact so I knew what to work on first
That is not a report I have to interpret. It is a to-do list.
The same information exists in Google Search Console. The GSC guide explains exactly how to find it. But finding it manually means navigating filters, switching tabs, cross-referencing pages and queries, and building the picture yourself. Brass-SEO builds the picture for you and tells you what it means.
If you are comfortable in GSC and enjoy the process, the manual approach works. If you want to skip the navigation and get straight to the answers, this is what Brass-SEO does.
For the full five-step content pipeline that runs from idea to publish in 90 minutes, see The Guerilla Content Pipeline: GSC to Publish.
Frequently Asked Questions
Do I need to know GSC to use Brass-SEO for this?
No. You do not need to know how to navigate Google Search Console at all. Brass-SEO reads your GSC data through the API and presents the findings in plain English. Knowing what GSC is helps you understand where the data comes from, but you never need to open GSC yourself. The Google Search Console guide for small businesses is useful background if you want to understand the data better.
Can I ask follow-up questions about specific results?
Yes. The conversation is interactive. If the AI mentions a striking-distance keyword and you want to know more — which page is ranking, what the content looks like, what competitors are doing for that term — you can ask. It will pull additional data or audit the page to give you a deeper answer.
How often should I ask for blog post ideas?
Monthly works well for most sites. Your query data changes as Google recrawls your pages and as search trends shift. Asking once a month gives you a fresh batch of opportunities to work with and keeps your editorial calendar grounded in data rather than guesswork. This fits naturally into a monthly SEO check-in.
Are the AI's suggestions always right?
The data is real — it comes directly from your Google Search Console and GA4 accounts. The AI's interpretation and recommendations are informed by that data but are suggestions, not guarantees. Use your judgment about which opportunities fit your business. The AI might surface a high-impression keyword that is technically relevant but not a topic you want to be known for. You decide what to write.
What if I do not have much traffic yet?
Brass-SEO needs data to work with. If your site is new and has very few impressions in GSC, there will not be much for the AI to analyze. In that case, focus on publishing foundational content first — pick topics based on what your customers ask you, and revisit Brass-SEO once Google has had a few months to crawl and start ranking your pages.
Try It Yourself
Connect your Google Search Console and GA4 data, open the chat, and ask: "What should my next blog post be about?"
The AI will pull your data, find the gaps, and tell you where the opportunities are — in plain English, with specific recommendations you can act on today.