Search Console is still the single most valuable tool for improving both SEO and AI visibility, and almost everyone reads it wrong.
I’ve used it to improve organic and AI-driven traffic for many companies over the years, and the method has never required anything more exotic than the Performance report. What matters is which metrics you treat as the goal and which you treat as signals along the way. Most people stare at average position. Position is the least important number on the screen.
The Three Metrics, In the Right Order
The Performance report gives you four numbers per query and per page. Three of them matter, and each one answers a different question:
- Impressions show you potential. An impression means Google considered your page relevant enough to show for a query. High impressions with low clicks is not a failure, it’s a map of demand you’re eligible for but not yet winning. This is where your opportunities live.
- Clicks tell you how you’re actually doing. Clicks are the primary goal metric, over everything else. Rankings don’t pay for anything, traffic does. Every optimization decision should trace back to whether it produced more clicks.
- Position tells you whether your optimizations are working. Ranking is the progress bar between the two: when a change moves a query from position 14 to 8 to 5, you’re getting closer to the click. It’s a directional signal, not a destination.
Once you internalize that hierarchy, the workflow becomes simple: find queries with meaningful impressions, use position to track whether your changes are closing the gap, and judge everything by clicks.
Run Long Comparisons, Then Hand Them to an LLM
Short date ranges lie to you. Week over week movement is mostly noise, and even quarter over quarter can be seasonal. The comparison I run is long: 16 months against the prior period, the maximum Search Console holds, broken out by query and by page.
Export it to a Google Sheet. Then pass that sheet to Claude or ChatGPT and ask it to find the patterns you’d miss scanning by eye:
- Queries where impressions grew but clicks didn’t, meaning demand rose and your snippet or position failed to capture it
- Pages that decayed over the period, which are your update candidates
- Query clusters that several pages compete for, which are your consolidation candidates
- Rising queries you have no dedicated content for at all
An LLM will chew through 15,000 rows of query data and hand back a prioritized punch list in minutes. This is one of the most concretely useful things you can do with AI in an SEO practice today, and it costs nothing beyond the subscription you already have. I covered the measurement framework this feeds into in Measuring AEO & SEO Performance.
Search Visibility Is the Best Predictor of AI Recommendation
There is now data backing the idea that Search Console is the right place to work from. An analysis of 105,000 ChatGPT prompts across 145 industries and 29,562 domains tested thirteen external signals against which brands got recommended, and the two strongest were appearances in search engines and best search engine rank, ahead of backlink count, backlink authority and PageRank.
A separate controlled experiment reinforces it from the other direction: being the first source in the retrieved set rather than the second was one of four factors that decided citations across every model tested. Rank feeds retrieval, retrieval feeds citation. The data in Search Console is not a legacy SEO artifact, it is the closest measurable proxy for AI visibility that exists today.
Most of the Wins Are in Content You Already Have
A lot of the focus is still on content, and it always has been. Where I differ from most is where that content effort goes: I’ve always been a major proponent of constantly improving your existing content rather than endlessly publishing new pieces.
Some of my blog articles are 6, 7, 8, 9, 10 years old, and I frequently go back into them to update facts, tighten structure, and find new ways to optimize based on what Search Console shows they’re almost ranking for. Those articles continue to drive traffic year after year as a direct result. The publish date is not what earns the ranking, the accumulated authority and the ongoing maintenance are.
I could easily publish new articles on the same topics, and I deliberately avoid it.
One Great Page Beats Ten Lackluster Ones
That restraint is not just an SEO habit, it’s a major beneficial factor for AEO and AIO.
When an AI assistant decides what to cite for a topic, it’s choosing the source that covers the subject completely in one place. Discuss one specific topic really well on one page and you’re far more likely to get mentioned than if you discuss it in lackluster ways across ten different places. Spreading a topic across near-duplicate posts splits your authority for search engines and gives language models ten mediocre candidates instead of one obvious answer.
Search Console shows you exactly where you’ve done this to yourself: filter a core query and count how many of your own pages have impressions for it. Every page past the first is diluting the one that should win. Merge them, redirect the losers, and put the ongoing effort into making the surviving page the best answer available anywhere. That’s the same consolidation argument I made in AI optimization that works, and Search Console is where you find the evidence to act on it.
The Loop
The whole practice fits in a loop you can run monthly:
- Export a 16 month query and page comparison to a Google Sheet
- Feed it to Claude or ChatGPT for opportunity and decay analysis
- Update and consolidate existing content first, create new pages only for genuinely uncovered topics
- Watch position to confirm the changes are moving, and judge the cycle by clicks
No rank trackers, no tool subscriptions, no dashboards. Impressions for potential, position for progress, clicks as the goal. It’s the process behind most of the growth I’ve delivered as an AEO and SEO consultant, and the data has been sitting in your Search Console account the entire time.
Supporting research: Vishwakarma, Kumar and Jamidar (2026), What Gets Cited: Competitive GEO in AI Answer Engines, SIGIR ’26, and LLM Ranking Factors 2026 from The Digital Bloom. I break both studies down in AI Ranking Factors.