Most websites don’t fail because the fixes are complicated.
They fail because the fixes aren’t prioritised properly.
You can have a 60-page audit, a spreadsheet full of recommendations, and a colour-coded roadmap — and still waste months fixing the wrong things in the wrong order.
A good SEO consultant doesn’t treat every issue as equal.
They don’t fix whatever’s easiest.
They don’t chase whatever looks impressive on a report.
They ask one question:
“What action unlocks the biggest impact with the least resistance?”
And the only way to answer that is with data — real data, not assumptions, checklists, or tool-generated noise.
Here’s how I prioritise SEO fixes using data, step by step, and how an experienced consultant separates what matters from what’s just a distraction.
1. Start with the data that shows where revenue is leaking
The first step isn’t technical.
It’s commercial.
You look at where the site is already generating traffic, impressions, or conversions — and then find the pages that are almost performing, but not quite.
The fastest wins come from pages that are:
- ranking in positions 4–15 for high-intent queries
- getting impressions but low clicks (CTR problem)
- getting clicks but low conversions (conversion problem)
- ranking on page two for commercial phrases
These aren’t broken pages.
They’re underperforming assets — and they’re the first things worth fixing, because they’re already showing signs of life.
Why this matters:
It’s faster, cheaper, and far more effective to lift a page from position 8 to position 3 than it is to rank a brand new page from scratch.
2. Use Search Console to find “near misses”
Search Console is where prioritisation starts getting concrete.
I look for three types of data patterns:
High impressions + low clicks
This usually means:
- your title or description isn’t compelling
- your page isn’t matching intent
- you’re ranking for the wrong variation of the query
This content becomes a high-priority quick win.
Clicks dropping despite stable impressions
This hints at:
- competition improving
- your snippet losing attractiveness
- Google favouring richer SERP features
The priority here is freshness and differentiation — reinforcing the content, updating examples, and tightening relevance.
Queries ranking but barely represented on the page
This tells me the page isn’t built around the actual language users are searching — which makes optimisation and restructuring the priority.
3. Use Analytics to validate traffic → conversion behaviour
Search Console shows visibility.
Analytics shows value.
I look at:
- which pages contribute the most to assisted conversions
- which URLs bring in high-intent users
- where users exit repeatedly
- which landing pages convert worst
- which blog posts actually lead people into the funnel
A page driving traffic but zero leads might not be a priority.
A page driving fewer visits but consistent conversions becomes a critical one.
SEO prioritisation is a conversion-first exercise — not a traffic-first one.
4. Use a crawl to map technical issues by severity
Technical audits produce long lists.
Most of those lists aren’t urgent.
In the first hour, I group technical issues by risk:
High-risk (fix immediately)
- pages unintentionally set to noindex
- canonicalisation errors affecting key URLs
- broken internal links to money pages
- site-wide slow load speeds
- severe crawlability problems
Medium-risk (fix after high-impact wins)
- duplicate content at scale
- pagination issues
- thin category pages
- multiple URLs competing for the same keyword
Low-risk (fix if they support growth)
- minor metadata duplicates
- unoptimised image alt tags
- unnecessary redirects
Data guides the order, not the checklist.
5. Competitor benchmarks reveal where “catch-up work” is needed
Competitive gaps matter, but not all of them matter equally.
I use competitor data to prioritise fixes based on:
- which competitors are outranking you for your highest-value terms
- what their content covers that yours doesn’t
- how strong their backlink profile is for your money pages
- the structure of their pages vs yours
- their internal linking depth
For example:
If a competitor has 12 well-built subpages supporting “HR software for small businesses,” and you only have one generic page, that becomes a cluster priority.
On the other hand, if a competitor has dozens of fluff blogs that drive irrelevant traffic, those gaps can be ignored entirely.
6. Backlink data reveals if trust — not content — is the real bottleneck
A site can have excellent content and strong technical foundations and still fail to grow.
The usual culprit?
It isn’t trusted enough.
I use backlink data to check:
- are competitors earning links from real publications?
- is your backlink profile thin compared to the market?
- do you lack links to key commercial pages?
- are competitors earning contextual mentions you’re missing?
If the domain-level trust gap is huge, link-building becomes a top-tier priority.
If the gap is small, content structure often produces a faster return.
7. SERP data tells you which fixes Google will reward first
Sometimes the fastest wins come from understanding how Google already structures the search results.
I analyse:
- What formats dominate the page? (guides? comparison pages? video?)
- What topics keep reappearing across ranking competitors?
- Which SERP features push organic results down?
- Is Google favouring recency or evergreen depth?
- Are AI overviews cannibalising broad queries?
If Google is rewarding 2,000-word deep dives for a keyword, the priority becomes depth.
If Google is rewarding short, crisp list-style content, the priority becomes clarity and structure.
The SERP tells you exactly how to win — if you know how to read it.
8. User behaviour data decides what to fix on-page
This is the part most consultants skip.
If you have access to heatmaps, session recordings or scroll-depth data, you can prioritise:
- which sections users ignore
- where they stop reading
- which CTAs fail to get interaction
- whether the content actually helps the reader
- which parts of the page cause confusion
On-page improvements become far easier when the data shows where readers hesitate, drift, or drop off.
9. Prioritisation formula: Impact × Certainty × Speed
After gathering the data, I use a simple scoring model to rank fixes:
- Impact — how much this change moves the dial
- Certainty — how confident we are it’ll work
- Speed — how quickly it can be implemented
Examples:
High Impact × High Certainty × Fast
- optimising pages ranking in positions 4–10
- fixing broken internal links to key services
- refreshing underperforming commercial pages
High Impact × Medium Certainty × Medium Speed
- building new service clusters
- rewriting entire landing pages
- solving major technical indexation issues
Low Impact × High Speed
- metadata tidy-up
- small internal link adjustments
These are “nice-to-haves,” not strategy drivers.
Final thoughts
SEO isn’t about fixing everything.
It’s about fixing the right things in the right order.
Real prioritisation happens when a consultant looks past the noise and uses:
- Search Console signals
- Analytics conversion patterns
- crawl-level technical data
- competitive gaps
- backlink trust signals
- SERP behaviour
- user interactions
Put together, these data points reveal the path of least resistance — the actions that create momentum, restore lost relevance, and produce measurable growth without wasting months chasing cosmetics.
That’s how consultants prioritise fixes properly.
Not by guessing.
By grounding every decision in the data that actually matters.
