In today's digital landscape, achieving high PageSpeed scores and strong search engine visibility is essential for online success. By implementing industry best practices, we successfully optimized our Next.js static website hosted on AWS — achieving green scores on Google PageSpeed and securing a prominent position in Google search results.

Here are the key implementations carried out at Head Digital Works.

Next.js provides built-in static site generation (SSG), ensuring faster load times. By pre-rendering pages at build time, the website loads instantly without server-side processing, leading to improved performance and SEO benefits.

1. Performance Optimization:

   To enhance speed, reduce server load, and ensure maximum scalability, we adopted Static Site Generation (SSG) using Next.js. During the build process, pages are pre-rendered into static HTML, which are then deployed to an S3 bucket and served globally via CloudFront CDN. This means instead of generating pages dynamically on each request, the CDN delivers pre-built HTML instantly from its nearest edge location. The result: significantly faster load times, no runtime rendering, and reduced backend pressure.

   
2. SEO Benefits:

   By generating fully pre-rendered pages at build time, we improve SEO by allowing search engines to easily crawl and index content. Faster page loads positively impact Core Web Vitals, which contributes directly to improved search rankings.

3. Cost Efficiency:

   Without any backend server, we reduced infrastructure costs by deploying static files to AWS. We configured Next.js with output: 'export', which generates static HTML, CSS, and JS files. These are stored in S3 and served via CloudFront — providing low-latency delivery while eliminating compute overhead.

   
4. Image Optimization:

   Built-in next/image optimization automatically resizes and compresses images at build or request time, enhancing page load speeds without sacrificing image quality.

5. Automatic Code Splitting:

   Next.js provides automatic code splitting by default. Each page only loads the JS it needs, reducing initial bundle size and improving performance. When users navigate to other routes, only the additional required code is fetched — enabling smooth, fast transitions.

Hosting our static files on AWS provides speed and reliability by leveraging S3 for high availability and scalability. CloudFront ensures low latency via global edge caching. This setup is also cost-efficient, requiring minimal maintenance while offering fast, consistent delivery.

To achieve green scores on Google PageSpeed Insights, we implemented the following:

1. Image Optimization
   • Converted images to modern formats like WebP.
   • Compressed using TinyPNG before uploading.
   • Stored optimized images on a CDN for delivery.
   • Used Next.js next/image for lazy loading and auto optimization.
   
2. Code Splitting and Lazy Loading

   We used next/dynamic to split and lazy load components. Non-critical UI (modals, charts, etc.) are only loaded when needed, reducing the initial JS payload and speeding up first paint.

   
3. Efficient Caching and CDN Usage

   AWS S3 + CloudFront serve static assets with optimal caching strategies. We use hashed filenames (e.g., home.ab3d4f.js) to enable automatic cache busting. HTML updates trigger CloudFront invalidation only when necessary, reducing overhead and maintaining freshness at the edge.

4. Minimizing Render-Blocking Resources

   We used rel="preload" for fonts and scripts needed early and rel="prefetch" for assets needed later. Non-essential JS was deferred or dynamically imported. These techniques improved perceived and actual load time, especially on mobile and slower networks.

   
5. Fixing Broken Links
   • Scanned with Google Search Console and Screaming Frog SEO Spider
   • Ensured internal/external links are valid and updated
   • Used 301 redirects for deprecated URLs
   • Validated all links across site, social media, and sitemap

Google PageSpeed Insights evaluates website performance based on several critical metrics. Optimizing these ensures a smooth user experience and improved SEO rankings:

a. Largest Contentful Paint (LCP)

Improved speed and visibility of the largest above-the-fold content

🔍 Observations:

• The hero banner (largest visual element) was rendering late, heavily affecting LCP.
• Alice Carousel introduced extra JS weight and delayed rendering.

✅ Actions Taken:

• Replaced Alice Carousel with Embla Carousel, which has a significantly lighter footprint and better performance.
• Applied lazy loading with loading="lazy" and priority hints for the banner image using next/image.
• Used preload for the banner image and ensured it's rendered as early as possible in the DOM.
• Used appropriate image dimensions to avoid oversized image loads — removing extra/unnecessary dimensions reduced file size and improved image loading speed.
• Compressed and served images in next-gen formats (WebP) for better decoding and delivery.

📈 Impact:

• LCP improvement observed in both lab and field data (Lighthouse, Chrome UX Report).
• Faster perceived load of the hero section led to better user engagement.

b. Interaction to Next Paint (INP)

Improved responsiveness to user interactions

🔍 Observations:

• Heavy scripts and synchronous processing delayed interactivity, especially on mobile.

✅ Actions Taken:

• Removed unused JS and CSS to reduce payload.
• Deferred non-critical scripts to avoid blocking the main thread.
• Minimized expensive layout reflows and DOM manipulations.
• Adopted lightweight event handling techniques and asynchronous logic wherever possible.

📈 Impact:

• Smoother and quicker response on user actions (clicks, scrolls, nav).
• INP scores moved closer to the ideal threshold in Lighthouse audits.

c. Cumulative Layout Shift (CLS)

Stabilized layout to prevent unexpected visual movement

🔍 Observations:

• Layout shifts occurred due to images and dynamic elements without defined dimensions.

✅ Actions Taken:

• Added explicit width and height for all images and banners to reserve layout space.
• Gave explicit aspect ratio and min-height to each section and major parent elements to lock down layout structure.
• Controlled dynamic component injection to avoid pushing existing content.
• Used flexbox/grid layout to stabilize sections during content load.
• Avoided injecting ads, popups, or carousels without reserved space.

📈 Impact:

• No visual shifting during load, providing a seamless and professional user experience.
• CLS remained well below the ideal 0.1 mark across all viewports.

d. First Contentful Paint (FCP)

Enhanced how quickly users see initial content

🔍 Observations:

• Delay in rendering text and visuals due to large JavaScript payload, unused CSS, and render-blocking resources.

✅ Actions Taken:

• Enabled dynamic imports to defer non-critical JS and improve initial render path.
• Used code splitting to load only what's necessary per page.
• Improved server response by using Amazon S3 as a CDN to serve static assets globally.
• Removed unused CSS using tools and manual audit.
• Separated critical and non-critical CSS — inlined critical CSS within HTML to reduce blocking render time.
• Reduced TTFB by minimizing server-side logic before rendering.

📈 Impact:

• Faster time-to-first-paint gave users quicker visual feedback and reduced perceived load time.

e. Time to First Byte (TTFB)

Observed improvement in server response time as a side-effect of frontend optimization

🔍 Observations:

• While no specific effort was directed at optimizing TTFB, we noticed a natural improvement in TTFB metrics during performance audits.
• This was likely influenced by the reduction in page weight, efficient resource loading, and leaner frontend rendering logic.

✅ Indirect Optimizations That Helped:

• Removed unused CSS and JavaScript, reducing initial payload size.
• Prioritized critical content rendering, leading to faster delivery of meaningful bytes.
• Lazy loaded non-critical elements, shifting their processing after the initial page response.

📈 Impact:

• TTFB values improved across multiple tests, contributing to better FCP and overall page load time.
• Even without direct backend tuning, the frontend improvements had a positive ripple effect on response times.