How to Track User Journeys in GA4 BigQuery with Session Stitching

Why Session Stitching Matters in GA4

When analyzing user behavior in GA4 BigQuery exports, you’ll quickly realize: sessions are not always clean, continuous, or unified. With Consent Mode, device switching, and delayed user identification (user_id or gclid), GA4 often fragments what should be a single journey into multiple separate sessions.

This is where session stitching comes in — a method to programmatically piece together related sessions into one coherent user journey.

What is Session Stitching?

Session stitching is the process of linking fragmented sessions from the same user into a single logical journey based on time, identifiers, or behavioral patterns.

In GA4 BigQuery exports, a single user journey can be split due to:

• Lack of initial consent (analytics_storage=’denied’)
• Cross-device browsing (mobile → desktop)
• Late arrival of identifiers (user_id, gclid, session_id)

Stitching sessions allows you to answer questions like:

• What sources led to a final conversion?
• How many sessions did a user take before purchasing?
• Where in the funnel did drop-offs happen across days or devices?

How GA4 Fragments Sessions

GA4 defines a session as a group of events with the same ga_session_id, scoped to a user_pseudo_id.

However, issues arise when:

BigQuery SQL: Stitching Sessions Based on User ID

Here’s a simplified example that stitches sessions based on user_id when available, and falls back to user_pseudo_id when it’s not:

WITH all_events AS (
  SELECT
    event_date,
    user_pseudo_id,
    user_id,
    event_timestamp,
    traffic_source.medium,
    ga_session_id,
    event_name
  FROM
    `your_project.your_dataset.events_*`
  WHERE
    _TABLE_SUFFIX BETWEEN '20250701' AND '20250728'
),

with_session_keys AS (
  SELECT *,
    COALESCE(user_id, user_pseudo_id) AS stitched_user,
    CONCAT(COALESCE(user_id, user_pseudo_id), '_', ga_session_id) AS stitched_session_id
  FROM all_events
)

SELECT
  stitched_user,
  stitched_session_id,
  MIN(event_timestamp) AS session_start,
  MAX(event_timestamp) AS session_end,
  COUNT(DISTINCT event_name) AS total_events,
  COUNTIF(event_name = 'purchase') AS purchases,
  ARRAY_AGG(DISTINCT traffic_source.medium IGNORE NULLS) AS session_mediums
FROM with_session_keys
GROUP BY stitched_user, stitched_session_id
ORDER BY stitched_user, session_start

What the Output Reveals

This query gives you a reconstructed view of user sessions, unified by either user_id or user_pseudo_id. From the results, you can:

• See how many stitched sessions led to conversion
• Analyze time between first touch and final action
• Attribute sessions to channels with greater accuracy

Tips for Storing Stitched Sessions

If you’re running this regularly:

1. Materialize the stitched session table into a persistent table (e.g., session_stitched_daily)
2. Partition it by event_date for performance
3. Join it with your CRM or ad platform data to extend the journey

Related Posts

• 📘 Understanding GA4 Sessions with Consent Mode Enabled
• 🔜 Rebuilding Attribution Models with BigQuery + Consent Mode →

TLDR

• GA4 often splits user sessions due to consent and device fragmentation
• Session stitching allows you to reassemble those journeys using SQL
• Use COALESCE(user_id, user_pseudo_id) to group by user
• Store your stitched sessions for ongoing reporting or modeling