How to Combine GA4 and Google Search Console Data for Deeper Organic Traffic Insights

Option A: Manual Export

1. In GSC, go to Performance, then Export to Google Sheets.
2. In GA4, go to Reports > Engagement > Landing Page, then Export.
3. Use VLOOKUP or INDEX-MATCH on the normalized URL to join.

Option B: Apps Script API Fetch

For automated refreshes, use Google Apps Script:

const gscResponse = SearchConsole.Searchanalytics.query(
  'https://example.com',
  { startDate: '2026-01-01', endDate: '2026-03-31',
    dimensions: ['page', 'query'], rowLimit: 25000 }
);

Pros

• Free and familiar interface
• Full control over transformations
• Can automate with Apps Script triggers

Cons

• Manual exports go stale immediately
• GSC API limited to 25K rows per request
• Sheets slows down past ~50K rows

Set Up Exports

• GA4: Admin > BigQuery Links > enable daily export.
• GSC: Set up bulk data export to BigQuery.

The Join Query

SELECT
  gsc.query, gsc.page AS landing_page,
  SUM(gsc.impressions) AS impressions,
  SUM(gsc.clicks) AS gsc_clicks,
  ROUND(AVG(gsc.position), 1) AS avg_position,
  SUM(ga4.sessions) AS sessions,
  SUM(ga4.key_events) AS key_events
FROM `project.dataset.searchdata_site_impression` gsc
LEFT JOIN (
  SELECT REGEXP_REPLACE(page_location, r'https?://[^/]+', '') AS page_path,
    event_date,
    COUNTIF(event_name = 'session_start') AS sessions,
    COUNTIF(event_name IN ('purchase','generate_lead','sign_up')) AS key_events
  FROM `project.dataset.events_*` GROUP BY 1, 2
) ga4 ON REGEXP_REPLACE(gsc.page, r'https?://[^/]+', '') = ga4.page_path
  AND FORMAT_DATE('%Y%m%d', gsc.data_date) = ga4.event_date
WHERE gsc.data_date BETWEEN '2026-01-01' AND '2026-03-31'
GROUP BY 1, 2 ORDER BY impressions DESC

Pros

• Handles millions of rows
• Full SQL flexibility
• Scheduled queries for automation

Cons

• Storage and query costs add up
• Requires SQL knowledge
• No built-in visualization

For data teams comfortable with code, Python offers the most flexible approach. Pull data from both APIs, merge with pandas, and pipe into any visualization tool.

Prerequisites

• Google Cloud project with Search Console API and Analytics Data API enabled
• OAuth 2.0 credentials or service account
• Python 3.8+ with google-analytics-data, google-auth, and pandas

Sample Script

import pandas as pd
from google.analytics.data_v1beta import BetaAnalyticsDataClient
from google.analytics.data_v1beta.types import RunReportRequest, DateRange, Dimension, Metric

ga4_client = BetaAnalyticsDataClient()
ga4_request = RunReportRequest(
    property="properties/YOUR_PROPERTY_ID",
    date_ranges=[DateRange(start_date="2026-01-01", end_date="2026-03-31")],
    dimensions=[Dimension(name="landingPage")],
    metrics=[Metric(name="sessions"), Metric(name="engagedSessions"), Metric(name="keyEvents")],
)

# Normalize and merge
from urllib.parse import urlparse
def to_path(url):
    return urlparse(url).path.rstrip("/") or "/"

df_combined = df_gsc.merge(df_ga4, on="page_path", how="left")

Pros

• Maximum flexibility
• Free (no paid tool required)
• Easy to schedule via cron or Airflow

Cons

• Requires Python and API knowledge
• OAuth setup is non-trivial
• Must handle API quotas and rate limits

If you'd rather not build and maintain the integration yourself, several tools can help — from Semrush's Organic Traffic Insights to ETL platforms like Supermetrics and Funnel.io.