Field-data audit by template
Real-user data grouped by template, since a template failing affects every page it generates and reporting per URL hides that entirely.
What the metrics measure and what they are worth
Three metrics: largest contentful paint for loading, interaction to next paint for responsiveness, and cumulative layout shift for visual stability. All are assessed on field data from real visitors. They influence ranking as a tie-breaker between comparable pages rather than as a primary factor.
The overselling of this category has been unhelpful, and it makes honest work harder to sell. Speed is not going to lift a page above a substantially better result. Where it does decide outcomes is between pages of similar quality and authority, which is a real but narrow effect and worth describing accurately.
The stronger case is conversion, and it is not marginal. Interaction delays and layout shifts cause abandonment directly and measurably. Clients who pursue this work for ranking reasons and then look at their checkout completion frequently conclude the ranking effect was the least valuable part of it.
Field data is what counts, which surprises teams who have been optimising a lab score. A page can score well in a synthetic test on a fast connection and fail badly for real users on mid-range phones. If your field data and lab data disagree, the field data is the one Google uses and the one your customers experience.
The most common cause on real sites is not code you wrote. It is third-party scripts — tag managers, chat widgets, consent banners, analytics — accumulated over years with nobody owning the total. Auditing that stack is usually where the largest available improvement sits, and it is a governance problem as much as an engineering one.
What the engagement covers
The engagement covers a field-data audit by template rather than by page, diagnosis of what causes each failure, a third-party script review, prioritised fixes specified for your developers, and re-measurement against field data once changes have been live long enough to register.
Per-metric diagnosis
What specifically causes each failure — render-blocking resources, unsized media, long tasks, layout shifts from late-loading elements — rather than a generic score.
Third-party script review
Every external script, what it costs in performance, and whether anyone still needs it. Usually the largest single win and rarely owned by anyone.
Prioritised fix specification
Written for your developers, with the expected effect per fix, so engineering effort goes where the measured cost actually is.
Conversion correlation
Performance changes lined up against conversion data, because that is where the value usually shows up and it is what justifies the next phase.
Field re-measurement
Verification after changes have been live long enough to enter the field dataset, which takes weeks rather than being visible immediately.
How the engagement runs
The engagement runs over six to ten weeks: establish which templates fail on field data, diagnose the specific causes, review the third-party script stack, specify fixes for your developers, then re-measure once changes have been live long enough to register.
- 01
Audit field data by template
Real-user measurements grouped by template. A single failing template can drag an entire site's assessment, and per-URL reporting obscures which one.
→ A per-template field data assessment
- 02
Diagnose the causes
What is actually producing each failure, traced to specific resources and behaviours rather than reported as a score that nobody can action.
→ A cause-level diagnosis per metric
- 03
Review the third-party stack
Every external script, its measured cost, and whether it is still needed. This step frequently removes more weight than all the code optimisation combined.
→ A script inventory with removal recommendations
- 04
Specify fixes for developers
Written so your engineers can implement without translation, ordered by measured impact against effort rather than by what is easiest to describe.
→ A prioritised, implementable fix list
- 05
Re-measure on field data
Verification once changes have been live long enough to enter the dataset. Lab improvements that never appear in field data have not actually helped anyone.
→ Before-and-after field measurement
Lab data compared with field data
Teams frequently optimise the wrong dataset. Lab tests run synthetically on controlled conditions and are useful for debugging. Field data comes from real visitors on real devices and is what Google assesses and what your customers experience — and the two regularly disagree.
| Lab data | Field data | |
|---|---|---|
| Source | A synthetic test you run | Real visitors on real devices |
| Used for ranking | No | Yes |
| Best for | Debugging a specific change | Knowing whether users are affected |
| Feedback speed | Immediate | Weeks, as the dataset accumulates |
| Common failure | Scoring well on a fast connection | Mid-range phones on poor networks |
| What we report on | During diagnosis | For every before-and-after claim |
Signals you need this now
You need this when field data shows templates failing, when your lab scores look good while real users are slow, when third-party scripts have accumulated with no owner, or when mobile conversion is materially worse than desktop for no clear product reason.
- Search Console reports templates failing Core Web Vitals
- Lab scores look fine while field data does not
- Third-party scripts have accumulated with nobody owning the total
- Mobile conversion is much worse than desktop
- Layout shifts move buttons as the page settles
- Nobody can say what your consent banner costs in performance
- You are about to replatform and want a baseline first
What clients see
Conversion improvements typically appear first and are the strongest part of the case. Ranking effects are real, narrow and slow, appearing only where you were competing closely with comparable pages. Field data takes weeks to reflect changes, so patience is part of the scope.
Where this fits
Related SEO services
Common in these sectors
The adjacent disciplines
Deciding between options
Questions about Core Web Vitals optimization
Each answer is written to stand alone in 40 to 60 words — the shape an AI Overview or Perplexity citation lifts. Ships with FAQPage schema.
Will improving Core Web Vitals improve our rankings?
Modestly, and only as a tie-breaker between comparable pages. It will not lift a page above a substantially better result. The stronger and more reliable business case is conversion, where the same fixes routinely produce more measurable value.
Why do our lab scores look fine when Search Console says we fail?
Because they measure different things. Lab tests run synthetically on controlled conditions; field data comes from real visitors on real devices, frequently mid-range phones on poor networks. Field data is what Google assesses and what your customers experience.
What usually causes the worst failures?
Third-party scripts, more often than anything the development team wrote. Tag managers, chat widgets, consent banners and analytics accumulate over years with nobody owning the total cost, and auditing that stack is usually the largest available win.
How long before improvements show up?
Weeks, because field data accumulates from real visits rather than updating immediately. A change deployed today will not be reflected in your assessment for some time, which is worth setting expectations about before the work starts.
Is this worth doing if our site is already reasonably fast?
Often not as a ranking play, and we will say so. If your templates pass on field data, the marginal ranking return is close to zero. There may still be a conversion case, and we would scope that honestly rather than selling speed work by default.
Do we need this before a migration or after?
Before, as a baseline. Migrations frequently degrade performance and without a prior measurement nobody can tell whether a post-launch problem is new or inherited. The baseline costs little and settles an argument that otherwise takes weeks.
Find out whether this is your constraint.
Thirty minutes with a senior strategist. We pull your live visibility while we talk and tell you plainly whether a Core Web Vitals optimization is what you need — or whether your problem sits somewhere else.