Getting data out of GA4 and into Excel shouldn't require a computer science degree. But if you've tried it, you know the reality: GA4's interface is built for viewing, not exporting. The native CSV export loses formatting, the API requires coding, and BigQuery feels like bringing a cannon to a knife fight.
This guide covers five methods to connect GA4 to Excel, from the simplest 2-minute export to fully automated pipelines. Pick the one that matches your technical comfort and how often you need the data.
| Method | Difficulty | Auto-Refresh | Row Limit | Best For |
|---|---|---|---|---|
| Native CSV Export | Beginner | No | 5,000 rows | Quick one-off snapshots |
| Google Sheets Bridge | Beginner | Semi-auto | 10M cells | Recurring reports without coding |
| BigQuery + Excel | Intermediate | Yes | Unlimited | Large datasets, SQL users |
| GA4 API + Python/VBA | Advanced | Yes | 100K per request | Custom automation, developers |
| AI-Powered Analysis | Beginner | Yes (live) | Unlimited | Analysis without exporting |
The fastest way to get GA4 data into Excel. No add-ons, no API keys, no configuration.
If you're using Explorations (GA4's custom report builder):
Best for: One-off analysis, quick data grabs, sharing a specific report with a colleague who lives in Excel.
Google Sheets sits in the same ecosystem as GA4, making it a natural intermediary. Pull GA4 data into Sheets, then download to Excel or connect live.
The official Google Analytics add-on for Sheets lets you pull GA4 data directly:
Once the data is in Google Sheets:
The GA4 Sheets add-on supports scheduled runs:
Best for: Recurring weekly/monthly reports where you need data in Excel but don't want to code. For more on Sheets-based analysis, see our Google Sheets data analysis tools guide.
GA4 can stream raw event data to BigQuery automatically. This gives you access to unsampled, unlimited data — and BigQuery connects natively to Excel.
Data starts flowing within 24 hours. You'll find it in BigQuery under analytics_PROPERTY_ID.events_*.
Traffic by source (last 30 days):
SELECT
traffic_source.source,
traffic_source.medium,
COUNT(DISTINCT user_pseudo_id) AS users,
COUNT(*) AS events
FROM `your-project.analytics_PROPERTY_ID.events_*`
WHERE _TABLE_SUFFIX BETWEEN
FORMAT_DATE('%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY))
AND FORMAT_DATE('%Y%m%d', CURRENT_DATE())
GROUP BY 1, 2
ORDER BY users DESCTop pages by pageviews:
SELECT
(SELECT value.string_value FROM UNNEST(event_params)
WHERE key = 'page_location') AS page,
COUNT(*) AS pageviews
FROM `your-project.analytics_PROPERTY_ID.events_*`
WHERE event_name = 'page_view'
AND _TABLE_SUFFIX BETWEEN
FORMAT_DATE('%Y%m%d', DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY))
AND FORMAT_DATE('%Y%m%d', CURRENT_DATE())
GROUP BY 1
ORDER BY pageviews DESC
LIMIT 50event_params)Best for: Teams that need unsampled data, SQL-comfortable analysts, or anyone hitting the 5K-row export cap. For a deeper dive, see our BigQuery analytics guide.
The Google Analytics Data API lets you programmatically pull GA4 data directly into Excel. Two approaches: Python (recommended) or VBA (Excel-native).
pip install google-analytics-data openpyxlfrom google.analytics.data_v1beta import BetaAnalyticsDataClient
from google.analytics.data_v1beta.types import (
RunReportRequest, DateRange, Dimension, Metric
)
import openpyxl
import os
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "path/to/credentials.json"
PROPERTY_ID = "YOUR_GA4_PROPERTY_ID"
client = BetaAnalyticsDataClient()
request = RunReportRequest(
property=f"properties/{PROPERTY_ID}",
date_ranges=[DateRange(start_date="30daysAgo", end_date="today")],
dimensions=[
Dimension(name="sessionSource"),
Dimension(name="sessionMedium"),
],
metrics=[
Metric(name="sessions"),
Metric(name="conversions"),
Metric(name="bounceRate"),
],
)
response = client.run_report(request)
wb = openpyxl.Workbook()
ws = wb.active
ws.title = "GA4 Traffic Report"
headers = [h.name for h in response.dimension_headers] + \
[h.name for h in response.metric_headers]
ws.append(headers)
for row in response.rows:
values = [d.value for d in row.dimension_values] + \
[m.value for m in row.metric_values]
ws.append(values)
wb.save("ga4_report.xlsx")
print(f"Exported {len(response.rows)} rows to ga4_report.xlsx")0 8 * * 1 python3 ga4_export.py (runs every Monday at 8 AM)For teams that need everything inside Excel, you can call the GA4 API from VBA using HTTP requests. This approach is more complex and harder to maintain than Python, but avoids external dependencies.
The general flow:
MSXML2.XMLHTTP to authenticate via OAuth 2.0We recommend the Python approach unless your organization strictly requires everything inside Excel.
Best for: Developers and technical analysts who need fully automated, scheduled Excel reports with specific data configurations.
Here's the question worth asking: do you actually need the data in Excel, or do you need answers from GA4?
Most people export GA4 to Excel because they want to:
AI analytics tools handle all of these without the export step. Instead of GA4 → CSV → Excel → pivot table → chart, you ask a question and get the answer directly.
| If Your Goal Is... | Excel Approach | AI Approach |
|---|---|---|
| Weekly traffic report | Export CSV → format → chart → email (30 min) | "Show weekly traffic trends" → share link (2 min) |
| Campaign comparison | Multiple exports → VLOOKUP → pivot table (45 min) | "Compare campaigns A and B by conversions" (1 min) |
| Landing page analysis | Export pages → sort → conditional formatting (20 min) | "Top 20 landing pages by conversion rate" (1 min) |
| Cross-source analysis (GA4 + CRM) | Export both → join in Excel → pray the keys match (60+ min) | Connect both sources → "Join GA4 and CRM data by user" (5 min) |
AI doesn't replace Excel in every scenario:
In these cases, use Methods 1-4 to get the data into Excel, and consider AI for the initial exploration.
GA4 standard reports cap at 5,000 rows. If you need more:
Common causes:
→ Method 1: Native CSV Export. Two minutes, no setup.
→ Method 2: Google Sheets Bridge. Schedule automatic refreshes, download to Excel when needed.
→ Method 3: BigQuery Export. Unlimited data, connects directly to Excel via Power Query.
→ Method 4: GA4 API + Python. Full control, runs on cron/Task Scheduler.
→ Method 5: AI-Powered Analysis. Skip the export entirely. Try Anomaly AI — connect GA4 and ask questions in plain English.
Not with a live, auto-refreshing connection. GA4 doesn't have a native Excel plugin. The closest native option is exporting CSV files (Method 1) or routing through Google Sheets (Method 2). For a direct connection, you need BigQuery (Method 3) or the API (Method 4).
For most small-to-medium sites, yes. BigQuery's free tier includes 10 GB storage and 1 TB of queries per month. GA4 streaming export can accumulate storage faster, but daily export typically stays within free limits for properties under ~1M events/day.
It depends on the method. Native CSV is a one-time snapshot. Google Sheets add-on can schedule daily/weekly pulls. BigQuery daily export updates once per day. The API can pull real-time data on any schedule you configure.
Standard reports: 5,000. Explorations: 10,000. GA4 API: 100,000 per request (with pagination for more). BigQuery: unlimited — you get every single event.
Yes. Methods 1, 2, and 4 work identically on Mac. Method 3 (BigQuery + Excel) works with Excel for Mac's Power Query feature or by exporting CSV from the BigQuery console. The ODBC driver approach is Windows-only, but the other BigQuery options work on Mac.
Now that you know how to get GA4 data into Excel, consider what you want to do with it:
Ready to skip the export entirely? Get started with Anomaly AI — connect your GA4 property and start asking questions. No CSV downloads, no pivot tables, just answers.
Experience AI-driven data analysis with your own spreadsheets and datasets. Generate insights and dashboards in minutes with our AI data analyst.
Founder, Anomaly AI (ex-CTO & Head of Engineering)
Abhinav Pandey is the founder of Anomaly AI, an AI data analysis platform built for large, messy datasets. Before Anomaly, he led engineering teams as CTO and Head of Engineering.
Continue exploring AI data analysis with these related insights and guides.
Complete guide to anomaly detection in Google Analytics 4. Covers GA4 native insights, BigQuery statistical methods, and AI-powered monitoring that catches issues GA4 misses.
Learn how to build GA4 dashboards that drive decisions. Covers native GA4 reports, Looker Studio, Excel, and AI-powered dashboards with templates and real examples.
Master Google Analytics 4 AI features in 2026. Learn how to use predictive metrics, anomaly detection, automated insights, and Gemini integration for smarter analytics.