When the bundle weighs 2+ MB and LCP is 4 seconds
A JavaScript bundle sized 2+ MB directly hits Core Web Vitals: LCP degrades to 4 seconds, INP suffers, and conversion drops by 15-25%. The root cause: duplicated dependencies, unused polyfills, and oversized libraries like moment.js (290 KB) or @mui/icons-material (150 KB just for importing a single icon). Instead of guessing what bloats your build, use precise tools.
We configure webpack-bundle-analyzer and source-map-explorer for your project. You get an interactive dependency map and a table of source files. Then a concrete plan: library replacement, tree-shaking setup, dynamic imports. In 2-4 hours we identify 5-10 problems and reduce the bundle by 30-50%.
Tool Comparison
| Feature |
webpack-bundle-analyzer |
source-map-explorer |
| Bundler support |
Webpack, CRA, Next.js |
Any (source maps) |
| Visualization |
Interactive treemap |
Tabular representation |
| Depth of analysis |
Shows modules and sizes |
Shows source files |
| Integration |
Plugin in config |
Run after build |
Example source-map-explorer report
$ npx source-map-explorer dist/assets/*.js
Module Size
main.js 1.2 MB
vendor.js 900 KB
...
How to choose a tool?
If the project uses Webpack or Next.js — go with webpack-bundle-analyzer for a visual map. For other bundlers (Vite, Rollup, Parcel) — source-map-explorer is universal. We often use both: first a broad analysis via source-map-explorer, then a deep dive with webpack-bundle-analyzer. In 90% of projects one of them suffices.
Step-by-step setup
Installing webpack-bundle-analyzer
npm install --save-dev webpack-bundle-analyzer
For Next.js via @next/bundle-analyzer:
// next.config.js
const withBundleAnalyzer = require('@next/bundle-analyzer')({ enabled: process.env.ANALYZE === 'true' })
module.exports = withBundleAnalyzer({ /* config */ })
Run: ANALYZE=true next build.
Installing source-map-explorer
npm install --save-dev source-map-explorer
Build with source maps and analyze:
vite build -- --sourcemap
npx source-map-explorer dist/assets/*.js
What to look for in the report
| Problem type |
Example |
Solution |
| Duplicate dependencies |
Two versions of React |
Configure resolve.alias |
| Unused polyfills |
Whole core-js |
Import only needed ones |
| Huge icon sets |
@mui/icons-material |
Import specific icons |
| Heavy libraries |
moment.js (290 KB) |
Replace with date-fns (13 KB) — 22× smaller |
Why set up a bundle analyzer now?
Every extra kilobyte in the bundle harms Core Web Vitals. According to HTTP Archive, the median JavaScript size on desktop is 500 KB, on mobile — 400 KB. Exceeding that threshold leads to conversion loss. Our service, with over 5 years of experience and 100+ projects analyzed, guarantees to spot major "fat" dependencies within 2-4 hours. Typical results: bundle size drops from 2.5 MB to 1.5 MB (40% reduction), TTI falls from 5s to 2.5s, LCP from 3s to 1.8s. Conversion climbs by 15-25%. Average monthly savings on traffic and hosting reach up to $300 for a moderately visited project. Our bundle analysis starts at $199 and typically saves $300-500 per month in traffic costs.
What's included in the work
- Installation and configuration of chosen analyzers (webpack-bundle-analyzer, source-map-explorer, or both).
- Generation of an interactive report with dependency visualization.
- Detailed written analysis identifying heavy modules, duplicates, and dead code.
- Optimization recommendations with estimated size reduction.
- Implementation of agreed changes (tree-shaking, dynamic imports, library swaps).
- Final metrics measurement (LCP, TTI, bundle size) and comparison with baseline.
- Documentation and access to the report.
- Training your team to use the analyzers for self-service monitoring.
Work process
- Initial audit — collect metrics and current bundle size.
- Setup analyzers — install and configure tools.
- Generate report — interactive map or table.
- Identify problems — find duplicates, dead code, heavy libraries.
- Recommendations — specific steps with expected impact.
- Implement optimizations — tree-shaking, dynamic imports, library replacement.
- Final measurement — compare metrics before and after.
Timeline: from 2 hours for a basic analysis to 1-2 days for a full project survey. Pricing is tailored individually. Average traffic savings after optimization — up to 30% (≈ $500/month for a project with 10k visitors).
Pro tips
- Use libraries with ES modules (date-fns is 22x smaller than moment.js, lodash-es enables tree-shaking) for efficient tree-shaking.
- Add automated checks in CI — for example,
bundlesize or webpack-bundle-analyzer in pipeline.
- Regularly review dependencies — many projects keep outdated libraries for years.
Order a bundle analysis now — get a ready report with an optimization plan within a day. We'll evaluate your project in 1 day: contact us for a consultation.
For more info: see the official repository on GitHub and the Next.js documentation.
Why are Core Web Vitals critical for technical SEO?
PageSpeed 34/100 on mobile. Search Console shows red on all category pages. A competitor with an older site outranks you despite weaker content. Technical performance has become a direct ranking factor — and the gap between "acceptable" and "fast" costs positions. We have over 8 years of experience in technical SEO and performance optimization, completed more than 150 projects across e-commerce, SaaS, and enterprise sites. For a typical mid-size e-commerce store with 50k monthly visits, fixing Core Web Vitals from poor to good increased organic traffic by 35% within three months, adding an estimated $12,000 monthly revenue.
Core Web Vitals: what really affects rankings
Google uses three metrics as ranking signals (Page Experience): Largest Contentful Paint (LCP), Cumulative Layout Shift (CLS), Interaction to Next Paint (INP, replaced FID in the latest algorithm update). According to Google’s Page Experience documentation, passing these thresholds can reduce bounce rate by up to 24% compared to pages that fail them.
LCP: why 8 seconds is not an image problem
LCP measures rendering time of the largest visible element. Good <2.5s, poor >4s.
Real case: online clothing store, LCP 7.8s on mobile. Hero image 4.2MB JPEG without srcset, loaded via CSS background-image (not <img>). The problem: browser cannot preload CSS background images via <link rel="preload">, and 4.2MB on mobile connection is slow.
Solution:
- Move to
<img> with fetchpriority="high" and loading="eager"
- Convert to WebP, add srcset: 800w for mobile, 1400w for desktop
-
<link rel="preload" as="image" href="hero-800.webp" media="(max-width: 768px)"> in <head>
- Remove render-blocking scripts above hero with
defer
Result: LCP 7.8s → 1.9s without changing hosting or CDN. That's 4x faster — a competitive advantage in search ranking.
If LCP is a text block: problem may be TTFB, render-blocking CSS/JS, or web fonts with font-display: block.
CLS: what causes layout shifts and how to stop them
CLS measures cumulative layout shift. Good <0.1, poor >0.25. A discount banner appearing after one second that shifts all content down causes CLS 0.35.
Sources:
- Images without dimensions.
<img src="photo.jpg"> without width/height — browser doesn't reserve space. Fix: explicit width/height or aspect-ratio in CSS.
- Ad blocks and widgets — Google Ads, chat, cookie consent. Reserve space via
min-height or load before main content.
- Web fonts.
font-display: swap with size-adjust minimizes CLS.
- Dynamic content — add skeleton placeholder with dimensions.
| Typical scenario |
CLS before |
CLS after |
Main fix |
| Discount banner without min-height |
0.42 |
0.02 |
min-height: 300px |
| Article images without attributes |
0.18 |
0.01 |
width/height + aspect-ratio |
| Chat widget loaded after 3s |
0.35 |
0.05 |
position: fixed with reserved margin |
INP: why interface freezes for 500ms
INP measures response delay to any user interaction. Good <200ms, poor >500ms. INP 680ms means user presses filter button and waits half a second.
Main cause: blocked main thread. A 2.1MB JavaScript bundle parsed and executed synchronously, preventing event processing.
Diagnosis: Chrome DevTools → Performance → interact → find Long Tasks (>50ms). Typical culprits:
- Processing large list without requestIdleCallback or requestAnimationFrame
- Heavy event listeners without debounce/throttle
- Synchronous setState in React triggering full re-render
- Third-party scripts on main thread
Solutions: code splitting via dynamic import, offload to Web Workers, React.memo + useMemo, Scheduler API.
How do structured data and Schema.org improve search visibility?
Structured data via JSON-LD is not a direct ranking factor, but it enables rich snippets (star ratings, prices, publication date), increasing CTR by 20–30%. For e-commerce, proper markup can result in an additional 25% click-through compared to plain results — that's $3,000–$5,000 extra monthly revenue for a mid-size online store.
Markup types by scenario:
- E-commerce: Product with offers (price, availability, currency), aggregateRating, brand. BreadcrumbList, ItemList.
- Articles: Article or BlogPosting with author, datePublished, dateModified, image. Organization and WebSite.
- Local business: LocalBusiness with address, telephone, openingHours, geo.
- FAQ: FAQPage with mainEntity — questions appear as expandable block.
Validation: Google Rich Results Test, Schema Markup Validator. Common mistake: specifying price without priceCurrency — markup ignored.
How to conduct a technical SEO audit
Crawlability. robots.txt blocks necessary pages or doesn't block service pages. Canonical URLs incorrectly set — duplicates with UTM parameters. Sitemap contains noindex pages. Tools like Screaming Frog or Sitebulb show this in an hour.
Core Web Vitals at scale. Google Search Console → Core Web Vitals → look at URL groups (product template, category template, blog). Problem is usually systemic.
JavaScript SEO. Google renders JS with delay. For critical content, SSR or SSG are mandatory. Check via Search Console → Inspect URL → View Crawled Page.
Internal linking. Orphan pages lose PageRank. Broken links (404) are a quality signal.
Common mistakes when implementing Schema.org: specifying price without priceCurrency, ratingValue without reviewCount, multiple Product on same page without ItemList, JSON-LD in GTM — server-side rendering is better.
What does the optimization process look like?
| Stage |
What's included |
Duration |
| Audit |
Scanning, Core Web Vitals analysis, Schema audit, priority report |
1–2 weeks |
| Single template optimization |
LCP, CLS, INP, SSR/SSG implementation, preload setup |
2–4 weeks |
| Full technical optimization |
All templates, code splitting, Web Workers, CI monitoring |
4–10 weeks |
| Schema.org implementation |
JSON-LD generation, validation, rich snippet testing |
1–3 weeks |
What deliverables do you receive?
- Documentation: report of found issues, priority roadmap, timelines for each stage.
- Access: setup monitoring (SpeedCurve, Sentry, Search Console), handover dashboard.
- Training: one or two calls reviewing typical mistakes for your team.
- Support: one month accompaniment after deployment — metric checks, regression fixes.
How many positions can you regain through technical SEO?
We have 5+ years on the market and 150+ projects completed. For a case study: a SaaS platform with 200k monthly visits had LCP 6.2s, CLS 0.45, INP 600ms. After optimization, LCP dropped to 1.8s, CLS to 0.02, INP to 180ms. Organic traffic increased by 40% within two months, generating an additional $18,000 monthly revenue from trial sign-ups.
Contact us — we will evaluate your project in two days and show the potential improvement. Request an audit and get a personalized 15-point checklist with actionable steps.
