Skip to main content

TTFB by scroll behavior

TTFB by scroll behavior without noise

Vasil Dachev avatar
Written by Vasil Dachev
Updated over 2 weeks ago


What is TTFB by scroll behavior

TTFB by scroll behavior shows how Time to First Byte (TTFB) varies based on whether the page was fully fresh, scrollable, or reloaded with a prior scroll position.

This lens can reveal whether server response time is being affected by the context of the navigation — especially in SPAs or apps with aggressive caching strategies.


Healthy TTFB by scroll behavior sample


Should you worry

In a healthy scenario:

  • TTFB remains consistent across all scroll behaviors

  • Back/forward navigations don’t lead to noticeably longer wait times

  • Scrollable content doesn’t impact how quickly the server delivers the page

This points to stable, well-tuned server behavior and no added latency based on scroll history.

Unhealthy TTFB by scroll behavior sample

Slower TTFB for Scrollable, Scrolled pages could mean:

  • The server is missing cache hits when restoring prior views

  • Backend routing is slower when restoring stateful views (common in SPAs)

  • Conditional logic on the server adds delay depending on session history or scroll metadata

Even small delays here can amplify perceived slowness when returning to a page — especially on mobile.

Resolving unhealthy TTFB by scroll behavior

Go-to action plan to resolve an unhealthy TTFB by scroll behavior:

  1. Ask Uxi to analyze your TTFB by scroll behavior values and suggest improvements.

  2. Use Filters to zoom in on problematic page loads and compare against fresh views.

  3. Simulate TTFB of the suspected lens to see if fixing it will resolve the TTFB by scroll behavior. If yes, this is where the resolution focus should be.

  4. Use an automated optimization tool like Navigation AI to improve your TTFB by scroll behavior values.

  5. Once you’ve improved TTFB, set an alert to be the first to know if it starts worsening again.

Try it yourself

Discover how your website performs with real user data.

Did this answer your question?