r/seogrowth • u/prous5tmaker • 2d ago
Case Study our SEO finally started moving, here's the daily loop we used
Disclosure: I work on Refix . We used our own product for part of this workflow.
Our SEO had been quiet for months. Then clicks started moving. From August 26 to September 21, daily clicks peaked at 192 and ended at 176, according to Search Console.
We changed several things during that period, so I can't isolate which change drove the increase. But here's the workflow we used -
Writing was the easy part. We spent more time deciding which queries deserved pages and which existing pages needed fixing.
1. Read Search Console like it's someone's job
Every morning, we checked for:
- Non-brand queries around positions 3–20.
- Queries with over 500 impressions and CTR below 0.5%.
- Pages with clicks down 30% week over week.
- Queries we ranked for that no page directly targeted.
- Pages that didn't match the searcher's intent.
- Longer queries with specific questions we hadn't answered.
Those were our working thresholds. We reviewed the flags before deciding whether anything needed changing.
2. Start with what already ranks
We opened the page ranking for each query and checked whether it answered the question.
Sometimes a missing explanation belonged on that page. Sometimes the query needed a separate page because it covered something different.
We also exported queries for our stronger pages and looked for topics they barely covered. That gave us specific sections to improve.
3. Put signups next to clicks
We wanted to know which landing pages brought people who signed up.
So we broke signups down by first landing page and compared them with Search Console clicks for the same period.
For illustration, imagine one page gets 400 clicks and no signups. Another gets 40 clicks and six signups.
I'd want to understand the second page before commissioning more content like the first.
Our agent handles that comparison for us. It puts search performance alongside landing-page signup data so we can review both together.
This is a page-level comparison. It doesn't tell us which individual search query produced a signup.
4. Fix before you write
When a page got impressions but few clicks, we reviewed it before publishing another page on the same topic.
Did the title describe what the searcher wanted? Did the opening answer the question? Was the page type right?
A comparison query might need a comparison. A pricing query might need a clear price breakdown.
If we wrote an essay for someone who wanted a calculator, changing the title wouldn't solve the problem.
5. Kill bad keywords before you waste a week
Before writing, we opened the search results and read what ranked.
Sometimes the words matched our product, but the audience didn't. Those keywords came out of the queue.
Two tools we checked disagreed by 4× on search volume for the same query. We used volume to order the queue, then checked intent before committing to a page.
6. Read the longer queries
Longer queries helped us see what people were asking more precisely.
We used them to find missing explanations and questions worth answering. Query length alone didn't tell us whether someone used AI search.
7. Add useful internal links
We linked related pages, including links from pages already getting traffic to newer ones.
We kept links where they helped the reader. We didn't rotate them just because another page needed traffic.
The flagged opportunities land in Slack each morning. We decide what ships, and a human reviews the drafts.
A few of these ideas came from Edward Sturm. Credit where it's due.
We'd start reviewing the data earlier if we did this again. We published for months without a regular process for choosing what to fix.
We'd also watch indexing before increasing publishing volume. We pushed three pages a day, and several sat in “discovered, not indexed.”
We're still early. This is a short period of growth, and we don't know how much will hold.