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CORE WEB VITALS

Core Web Vitals optimisation.

LCP, INP, and CLS diagnosed properly and fixed in the code, rather than treated with a caching plugin and hope.

THE THREE METRICS

Each metric has a different cause.

LCP measures how long the main content takes to appear. It is usually held back by server response, render-blocking resources, or an unoptimised hero image. INP measures response to interaction and is usually held back by too much JavaScript on the main thread.

CLS measures content movement while a page loads. Missing media dimensions, late embeds, and font swaps commonly cause it. Generic speed advice rarely moves all three because each one needs a different technical fix.

Web performance diagnostics and development workspace

THE TARGET

LCP under 2.5 seconds, INP under 200 milliseconds, and CLS under 0.1 are achieved through different work.

Senior capacity, only when the work exists

TYPICAL FIXES

Work at the cause, not the score.

The fixes are selected from the page and real user data rather than pasted from a generic performance checklist.

LCP fixes

Server response, caching, resource preload, critical CSS, image sizing, and font strategy.

INP fixes

Reduced JavaScript work, shorter tasks, deferred scripts, and better event handling.

CLS fixes

Explicit media dimensions, reserved dynamic space, and stable font loading.

Third-party script audit

Tags, chat widgets, and embedded services assessed for their real performance cost.

Field-data diagnosis

Chrome UX Report and Search Console used to understand how real users experience the page.

Code-level implementation

Changes delivered in the theme, platform, or application instead of assumed from a plugin.

FIELD DATA FIRST

Optimise for real users, not a single simulation.

Lighthouse is useful for diagnosis. Google uses aggregated data from actual Chrome users over a 28-day window.

01

Search Console review

The route from reported failing URLs to the exact pages and devices affected.

02

Chrome UX Report data

Real user measurements rather than a fast desktop simulation.

03

Lab diagnosis

Lighthouse and developer tools used to identify the causes behind field failures.

04

Platform-specific approach

WordPress, Shopify, Webflow, Framer, and custom applications assessed by their real limits.

05

Before and after measurement

Changes measured against the metrics that matter instead of visual confidence.

06

Clear expectation setting

A realistic ceiling for the current platform before work begins.

WHAT IS REALISTIC

Improve the platform you actually have.

WordPress typically has meaningful gains available because theme weight and plugin scripts are controllable. Shopify theme work is controllable while app scripts may be the bigger limitation. Webflow and Framer usually start with solid foundations, where images and animation are the usual problems.

Custom applications are the most controllable and also most likely to carry JavaScript-heavy INP problems. The realistic outcome is assessed before starting instead of promising green scores on every platform.

“A good Core Web Vitals plan explains the ceiling before it sells the fix.”

Developer improving web performance in code

COMMON QUESTIONS

Core Web Vitals, clearly scoped.

The questions around measured performance work before the first change is made.

How long before field data improves?

Field data uses a 28-day rolling window, so allow a month after deployment.

Do Core Web Vitals affect rankings?

They are a ranking factor, weighted below relevance, and they also affect conversion.

Why does Lighthouse say 95 when the site fails?

Lighthouse is a lab simulation. Google evaluates data from real user devices and connections.

Can they be fixed on any platform?

Improvement is possible. All-green results depend on the platform and current technical constraints.

Is a caching plugin enough?

It can help LCP and does not resolve INP or CLS.

Can speed improve without rebuilding?

Usually. Images, scripts, and third-party tags produce many of the largest gains.

Failing Core Web Vitals?

Send the URL. You will get a clear view of the failing metric and the cause behind it.

Field data diagnosisCode-level fixesMeasured outcome