How to Pick Blog Topics Using Search Console Data (Advisor Edition)
Most advisors 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. The question is not whether you should rank for that query in theory. The question 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.
This is the inversion most advisors miss. The queries where you are already ranking well are not your blog topics. Your blog topics are the queries where Google thinks you are relevant but does not yet rank you high enough to win the click.
Filter 1: The opportunity zone, average position 5 to 20
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 5 and 20.
Why those numbers? Positions 1 through 4 are already winning enough traffic that a new blog post is unlikely to move them meaningfully. Positions 21 and beyond are usually too far back for incremental content effort to push them to page one within a reasonable timeframe. The opportunity zone is positions 5 to 20: queries where Google has confirmed your relevance but has not yet ranked you high enough to capture meaningful visibility.
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.
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, Perplexity, and Gemini 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 discovery 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.
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 average position 8 to 15 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. The cluster is the unit of opportunity, not the individual query.
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 are compliance-friendly, 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. After applying position 5-20 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.
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 and was generating consistent inbound contact form submissions from prospects asking specifically about HSA planning.
That single post was not the result of guessing what readers might want. It was the result of asking GSC what readers had already shown they wanted. The data was there for a year. Nobody had looked at it.
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.
Shaun Melby, CFP® is the creator of AdvisorSEO Max, SEO software for financial advisors and RIAs, and the founder of Melby Wealth Management, a fee-only RIA in Nashville. He runs every tactic on this blog on his own firm first. This content is educational and does not constitute investment, legal, or compliance advice. Consult your compliance officer before implementing any marketing changes.
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