Analysis SEO
I start every SEO analysis by pulling Search Console data, not by running a crawl tool, because impressions tell you what Google already sees.
What I’d do first
- Start with Google Search Console to see which queries and pages already have impressions before running any crawl.
- Prioritise fixes based on potential impact on traffic, not just technical severity or tool score.
- Include competitor analysis to find content and keyword gaps, but always check the SERP landscape first.
- Tie every analysis to a business metric like conversion rate or organic revenue, not just rankings.
The path I'd take
I start with a goal. Are we trying to increase organic traffic to a specific section, or improve conversion rate from existing traffic? That decides the scope. I pull data from [Google Search Console](/seo-analysis/) for the last 90 days. I look at total impressions, clicks, average position per query. I export the top 1,000 queries. Then I cross-reference with [Google Analytics](/google-analytics/) to see bounce rate and conversion rate for those landing pages. Only then do I run a technical crawl. I use a tool that checks indexation, core web vitals, mobile friendliness, and duplicate content. I examine the crawl report for 4xx errors, redirect chains, and blocked resources. I also check the coverage report in Search Console for excluded pages. For on-page, I review title tags, meta descriptions, and H1s for a sample of 50 pages. I look for keyword cannibalisation. Then I do a [competitor analysis](/seo-competitor-analysis/) using a tool like Semrush or Ahrefs to see which keywords they rank for that I do not. I also analyse their backlink profile. Finally, I produce a prioritised list. I rank fixes by impact on traffic and ease of implementation. For example, fixing a 404 on a high-traffic page takes priority over optimising a low-traffic title tag. I once found a site where 30% of pages had no indexable content. That was the first fix. It took half a day to implement and increased indexed pages by 40% within two weeks. The output is a clear [SEO report](/seo-report/) with a shortlist of actions, not a laundry list of issues.
Watch-outs
The biggest trap is analysis paralysis. You can find hundreds of issues. Pick the top five that will move the needle. Do not rely on automated scores alone. A tool might flag a page as slow, but if it gets 10 visits a month, it is not worth your time. Context matters. Another watch-out is ignoring user intent. I have seen sites fix every technical issue but lose traffic because they changed content to match a keyword without considering what the searcher actually wants. For example, a page targeting 'best running shoes' should not be a product category page with no comparison. It should be a listicle. Also, be careful with competitor analysis. Just because a competitor ranks for a keyword does not mean you can. They may have higher domain authority or better content. I always check the SERP landscape: if the top results are all big brands, targeting that keyword is a long shot. Finally, do not forget to include business metrics. Rankings are vanity; traffic and conversions are what matter. I tie every analysis to a [KPI](/seo-kpis/) like organic revenue or lead volume. If the analysis does not connect to a business outcome, it is a waste. I also keep a [SEO dashboard](/seo-dashboard/) that tracks the key metrics weekly so I can spot trends without a full re‑audit.
What I got wrong
I used to obsess over keyword rankings. I would track positions weekly and panic when a keyword dropped from #2 to #3. I spent weeks trying to reclaim that spot, only to realise the page had a low click-through rate because the meta description was poor. I should have focused on the snippet and title first. I also ran full SEO audits every month. That was too frequent. Most issues do not change that fast. I now do a deep analysis quarterly and a weekly check on key metrics like impressions and clicks. Another mistake: I ignored the backlink profile. I once analysed a site that had 50% of its backlinks from a private blog network. I missed it because I only used one tool. Now I cross-reference with at least two sources. I also undervalued the importance of Search Console data. I used to start with a crawl, but crawls miss what Google already sees. Impressions and clicks are the starting point. I now always pull Search Console data first. And I learned the hard way that content quality matters more than technical perfection. I fixed a site's technical issues but the traffic did not recover because the content was thin. I now always include a content audit in the analysis. Getting the order wrong cost me months of wasted effort.
Next step
Quick answers
How often should I run an SEO analysis?
I run a deep analysis quarterly for most sites. Weekly checks on impressions, clicks, and core web vitals are enough to catch regressions. Monthly audits are overkill unless you are launching a major site change or see a sudden traffic drop.
What is the most important part of an SEO analysis?
Starting with Search Console data. It tells you what Google already sees and where you already have a foot in the door. Without that, you are guessing. Fixing indexation issues on pages that already earn impressions can bring quick wins.
Should I use automated tools or manual checks for SEO analysis?
Both. Automated tools are good for catching technical issues site-wide, but they miss context. Manual checks on a sample of pages reveal quality problems that tools cannot score, like thin content or poor match to search intent. I use tools to list issues, then manually prioritise.
Sources
Primary documentation is linked directly. Anything commercial is marked nofollow.
- Google Search Central Documentation — Backs up the technical checks and data usage in analysis.
- Google Search Console Help — Supports the recommendation to start with Search Console data.
- Semrush: What Is SEO Analysis? — Provides an overview of analysis components including technical, on-page, and competitor.
- Ahrefs Blog — Used as a reference for content audit and backlink analysis methods.
Notes from Callum Bennett.