How to Pick Blog Topics Using Search Console Data (Advisor Edition)
Advisors tend to pick blog topics one of two ways. They guess based on what feels timely, or they ask a marketing agency to send them a content calendar built from generic keyword research.
Both approaches ignore the only data set that actually matters: the queries your own website is already showing up for. If your site has been live for more than six months and has any indexed content, Google Search Console is sitting on a list of high-intent blog topics tailored specifically to your firm. Most advisors never look at it.
This post is the exact filtering process I use on every advisor site I review. It produces a ranked list of blog topics where the demand is already proven, your firm has some baseline visibility, and incremental optimization can move you from page two to page one. No guessing, no agency calendar, no generic keyword tools.
The principle: your topic list is hiding in the Performance report
When a query shows up in your GSC Performance report, Google has already decided your site is relevant enough to render in the results at least once. That is a meaningful signal. What you need to know next is how close you already are.
The Performance report shows four metrics per query: clicks, impressions, click-through rate, and average position. Two of those metrics, clicks and CTR, are unreliable at the query level for advisor sites. Google's privacy throttling on low-click queries hides clicks below a threshold, which makes CTR mathematically distorted on most advisor queries. The two reliable signals are impressions and average position. Those are the metrics you filter on.
A query showing 200 impressions per month at average position 14 is a stronger blog topic candidate than a query showing 50 impressions at average position 4. The first one has demand and room to grow. The second one is already capturing most of the available traffic it can.
Query A
Demand is proven and there is room to move. Google already renders the site for it; a focused post can carry it toward page one.
Query B
Most of the traffic this query can give is already captured. A new post is unlikely to move it in a way that matters.
Impressions and average position only. Query-level clicks and click-through rate are hidden or distorted by Google's privacy thresholds on advisor sites, so they do not enter the decision.
This is the part that gets missed. A query you already rank well for has little left to give. Your blog topics are the queries where Google thinks you are relevant and does not yet rank you high enough to be seen.
Filter 1: The opportunity zone, average position 10 to 30
Open your Google Search Console Performance report and set the date range to the last 12 months. Apply a filter on the Queries tab for average position between 10 and 30.
Why those numbers? Positions 1 through 9 are already on page one, and a new blog post is unlikely to move them much. Positions 31 and beyond are usually too far back for incremental content effort to reach page one. The opportunity zone is positions 10 to 30: queries where Google has confirmed your relevance and has not yet ranked you high enough to capture meaningful visibility.
Already winning
1 to 9
Already on page one. A new post is unlikely to move them much.
The opportunity zone
10 to 30
Google has confirmed relevance but not ranked the site high enough to capture meaningful visibility. Filter here.
Too far back
31+
Usually too far back for incremental content to reach page one.
Average position in the Queries tab, last 12 months. The filter keeps the middle zone and drops the other two.
If your site is newer or smaller, this filter might return only a handful of queries. That is fine. Even five queries is a ranked topic list, and it is built on real data instead of guesses. If the filter returns hundreds of queries, you have a larger problem to solve, which is content focus, and the filtering below will help.
Filter 2: The impressions threshold, separating signal from noise
The Performance report will surface queries with as few as one impression. Most of those are noise: long-tail variations, misspellings, or one-off curiosity searches that will not repeat. You want queries with enough impression volume to indicate sustained demand.
For most advisor sites, the right threshold is somewhere between 50 and 200 impressions over a 12-month window. Below 50, you are usually looking at noise. Above 200, you are looking at queries with genuine, recurring demand.
The exact threshold depends on your firm's traffic level. A solo RIA with 800 monthly impressions on the site overall should set the bar around 30 to 50 over 12 months. A firm with 8,000 monthly impressions should set the bar around 150 to 250. The principle stays the same: filter out the noise, keep the queries that show consistent demand.
| Site size | 12-month threshold | On a 0 to 250 scale |
|---|---|---|
| Solo RIAAbout 800 impressions a month across the site | 30 to 50 | Lowest bar |
| Most advisor sitesThe general range this post recommends | 50 to 200 | Middle |
| Established firmAbout 8,000 impressions a month across the site | 150 to 250 | Highest bar |
Impressions per query over 12 months. Each green segment is the threshold range for that site size, drawn to scale. Below 50 is usually noise; above 200 is recurring demand.
Increasing impressions on the queries that matter is the upstream version of this exercise. This post is about the downstream half, which is converting impression demand into ranking position through better content.
Filter 3: Question-style queries and long-tail intent
Within the filtered list, pay special attention to queries that contain question words: how, what, why, when, can, should, do, is. Question-style queries are blog post gold for three reasons.
First, they map directly to a content format readers expect. A query like "what is a fiduciary financial advisor" wants an answer post. A query like "how do I roll over a 401k after leaving a job" wants a process post. The intent is unambiguous.
Second, question-style queries are the queries AI search engines lift most often when generating responses. Ranking for them helps you in both Google traditional results and in ChatGPT, Claude, and Perplexity citations.
Third, question-style queries usually have lower competition than head terms. The advisor who builds a thoughtful, 1,200-word answer to a real question often outranks larger firms that only wrote a generic service page.
When you spot a question query in your filtered list, it almost always becomes a dedicated blog post topic. The query goes in the H1 or the first H2, the answer comes in the first 150 words, and the rest of the post fills in the nuance, examples, and edge cases.
Filter 4: Segment branded vs non-branded queries
The Performance report mixes branded queries (your firm name, your name, variations of both) with non-branded queries (everything else). For blog topic selection, you want to separate them.
Branded queries are conversion-stage searches. Someone searching "Melby Wealth Management" already knows about the firm and is trying to find the website. New blog content rarely changes ranking on branded queries because you already rank first by default. These are not your blog topics.
Non-branded queries are the research-stage searches. Someone searching "Roth conversion ladder for early retirement" does not yet know about your firm. They are looking for information. If you rank well, they find your content first, then your firm. These are your blog topics.
Branded
Melby Wealth Management
Non-branded
Roth conversion ladder for early retirement
Both example queries come from the paragraphs above. The forest chip marks the one that belongs on the topic list.
You can segment branded from non-branded by adding a query filter that excludes your firm name and the principal advisor's name. The cleaner the segmentation, the more reliable the topic list.
Building the topic list from the filtered data
After applying all four filters, you should have a list of 10 to 50 queries depending on your site's size. Now you turn that list into a topic calendar.
Step 1: Group similar queries. Several queries often point to the same underlying topic. "Should I do a Roth conversion in retirement" and "is a Roth conversion worth it after age 60" are the same blog post. Cluster the queries into topic groups.
Step 2: Pick the primary query for each group. Inside each cluster, one query usually has higher impressions or better position than the others. That is your H1 keyword. The other queries in the cluster become H2 subheadings and FAQ entries within the post.
Step 3: Rank the topics by combined opportunity. For each topic group, total the impressions across all queries and note the average position of the primary query. Topics with high combined impressions and an average position of 10 to 20 are your top priorities. They have the most demand and the most realistic path to page one.
Step 4: Schedule the posts. A reasonable cadence is one new post per week. With a list of 30 topic groups, you have six months of content scheduled from real data. By the time you publish the last post, the first ones have aged enough to start showing meaningful position improvement.
Common mistakes that wreck this process
Chasing CTR at the query level. Click-through rate at the query level is distorted by Google's privacy throttling on low-click queries, so CTR numbers below a certain threshold are mathematically unreliable. Optimizing for CTR per query at the long tail will lead you in circles. Use impressions and average position for query-level decisions. Use site-wide CTR trends only when looking at aggregate performance.
Ignoring queries with low impressions that cluster together. A single query with 30 impressions looks like noise. Five related queries with 30 impressions each, all on the same topic, is 150 impressions of demand for one cluster. Judge the cluster as a whole.
Treating the topic list as final. The filtered list is your starting input. You still apply judgment about which topics fit your firm's niche, which your compliance reviewer would be comfortable with, and which match the kind of client you want to attract. A query showing strong demand for "best penny stocks for beginners" is not a blog topic for a fee-only retirement advisor, regardless of what the data shows.
Forgetting to re-run the analysis quarterly. Search behavior shifts. New queries emerge in the data as you publish more content. The filtered topic list six months from now will look meaningfully different from today's. Run the same filtering process once a quarter and refresh the calendar accordingly.
Confusing search demand with personal interest. Some advisors find a query they want to write about and ignore the position and impressions data because they have an opinion they want to share. That is fine, and there is room for opinion content, but it is a different exercise. The data-driven topic list is for ranking and traffic. The opinion content is for thought leadership. Keep the workflows separate so you do not confuse the goals.
A short example from a real advisor site
I ran this process on an RIA site recently. The Performance report had about 1,400 queries in the 12-month window. That run used a position band of 5 to 20, which is narrower than the 10 to 30 I use now, and the steps were the same. After the position filter, the list dropped to 312. After the 100-impression threshold, it dropped to 67. After excluding branded queries and questions about specific competitors, the list landed at 44.
One RIA site, 12 months of Search Console data
What each filter did to the query count.
Bars are drawn to scale against the 1,400 starting queries. The 44 surviving queries grouped into 14 topic clusters, and each cluster is one post.
From those 44 queries, I built 14 topic clusters. The highest-priority cluster had four related queries about HSA contribution strategies, totaling 380 combined impressions at an average position of 11. The advisor wrote one focused blog post answering that cluster's question. Within five months, the post moved to average position 4 on its primary query.
HSA contribution strategiesHighest-priority cluster of the 14
Before the post
11
Average position, primary query
Within five months of publishing
4
Average position, primary query
Average position is the measure here, since query-level clicks are hidden by Google's privacy thresholds. One site, one post, the period stated; not an expected result for any other firm.
That post came from asking Search Console what readers had already shown they wanted. The data had been sitting there for a year, and nobody had looked at it.
Frequently asked questions
How do I find blog topics in Google Search Console?
Open the Performance report, set the date range to 12 months, and filter the Queries tab four ways: average position between 10 and 30, enough impressions to show sustained demand, question-style wording, and branded queries excluded. What is left is a list of topics where demand is proven and your site already has some visibility.
What average position range should I look at for blog topics?
Positions 10 to 30. Queries at 1 through 9 are already on page one, and a new post is unlikely to move them much. Queries beyond 30 are usually too far back for a single post to reach page one. The middle band is where Google has confirmed relevance and has not yet ranked you high.
How many impressions does a query need to be worth a blog post?
For most advisor sites, somewhere between 50 and 200 impressions over 12 months. A solo RIA with about 800 monthly impressions across the site can set the bar around 30 to 50. A firm with about 8,000 can set it around 150 to 250. Related low-impression queries can be added together as one cluster.
Should I use clicks or CTR to choose blog topics?
No. At the query level, Google withholds click data on low-volume queries for privacy, which makes clicks and click-through rate unreliable on most advisor queries. Use impressions and average position for query-level decisions. Site-wide CTR trends are fine to look at in aggregate.
How often should I redo the topic analysis?
Once a quarter. Search behavior shifts, and new queries appear in the data as you publish more. Run the same four filters, refresh the topic list, and adjust the calendar. After you have done it twice, the filtering takes about 30 minutes.
What changes when you stop guessing
The advisors I see with the most consistent organic traffic share one habit. They have a documented process for picking content topics, and the process uses their own GSC data instead of generic keyword tools or agency calendars.
This does not mean external tools are useless. Keyword research has a place for new pages, new service offerings, or new geographic markets. But for an existing site with at least six months of indexed content, GSC is the highest-quality data source available because it reflects actual search behavior for your specific domain.
The filtering takes 30 minutes once you have done it twice. The topic list it produces is more useful than anything an outside agency can generate, because it is built on demand that has already been proven against your firm's existing pages. The work after the filtering is the writing, which is the part that always was, and always will be, the bottleneck.
If you have not opened your Performance report in the past month, that is the place to start this week. The topics are already there.
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