Page Performance

Analyze page-level health — load times, errors, device breakdown, and how users navigate between pages.

When to Use

• "Which pages have the most errors?"
• "How does /checkout perform on mobile vs desktop?"
• "What pages do users visit before /pricing?"
• "Show me sessions where /dashboard was slow to load"
• "Which pages have the highest abandonment rate?"
• "Compare all product pages by error rate"

Use free fullstory:discover_groups and fullstory:get_opportunity_stats for live signals. Discovery accepts one scope at a time (page_id, domain, app_name, segment_id, or funnel_id); use a segment for device/user filters and the live schema for time ranges. Use computed metrics for exact custom windows.

Workflow

Step 1: Define the scope

What does the user want to know about the page(s)?

• Error rate: console errors, network failures on this page
• Performance: rage clicks, dead clicks, form abandonment on this page
• Navigation: what pages lead to/from this page
• Device breakdown: mobile vs desktop usage and error rates
• Comparison: rank multiple pages by a metric

Step 2: Build page-scoped metrics

For a single page, scope the metric to that page:

fullstory:build_metric(
  query="console errors on /checkout",
  output_type="trend"   # or single_number or top_n
)

For comparing multiple pages, use top_n grouped by page:

fullstory:build_metric(
  query="console errors by page",
  output_type="top_n"
)

Step 3: Add device/browser breakdown

If the user wants to know "mobile vs desktop on this page":

fullstory:build_metric(
  query="page views on /checkout by device type",
  output_type="top_n"
)

Or for errors specifically:

fullstory:build_metric(
  query="console errors on /checkout by device type",
  output_type="top_n"
)

Step 4: Investigate problem pages

If a page stands out (high error rate, high abandonment):

1. fullstory:get_sessions(page="/checkout", metric_id=error_metric_id, limit=5)
2. Load sessions through session-context agent: "What error occurred on /checkout? What page did the user come from and go to?"
3. Synthesize: is the error specific to this page, or does it follow the user from a previous page?

Step 5: Navigation patterns

To understand what pages users visit before/after a given page:

fullstory:get_user_pages(uid="...", options={...})
→ returns all pages visited by a user in order

But this is per-user. For aggregate navigation patterns, build a funnel metric with the page as a step and compute:

fullstory:build_metric(
  query="users who viewed /pricing, then what page did they view next",
  output_type="top_n"
)

Common Analysis Patterns

Guidelines

• Page names in Fullstory are URLs — use path patterns like /checkout or /products/*.
• When comparing pages, always normalize by traffic. A page with 10 errors and 100 views is worse than a page with 50 errors and 10,000 views.
• If a page has high error rate on a specific device/browser, flag it immediately — these are often easy-to-fix compatibility bugs.
• Navigation pattern analysis is fuzzy — Fullstory doesn't have a native "page flow" tool. Be transparent that you're inferring from individual sessions, not getting a computed flow diagram.
• Always surface the metric_url for any page-level metric you build.