BigQuery Optimization: Reduce PPC Campaign Query Costs by 40-50%

Are you analyzing Google Analytics 4 (GA4) data for your PPC campaigns? Running inefficient queries in BigQuery can lead to high costs and slow performance. Here’s how you can optimize your queries and reduce costs by 40-50% while speeding up execution.

If you want to analyze PPC Campaign Conversions in GA4, you can query BigQuery to get conversions from paid campaigns, counting purchase events by traffic source (session_source). This helps evaluate the performance of different advertising platforms (Google Ads, Facebook Ads, etc.).

You could write a query like this:

❌ Unoptimized Query (Slow and Expensive)

SELECT session_source, COUNT(*) AS purchase_count
FROM `project_id.analytics_XXXXXX.events_*`
WHERE event_name = 'purchase'
AND PARSE_DATE('%Y%m%d', event_date) BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 30 DAY) AND CURRENT_DATE()
GROUP BY session_source;

But:

❌ Scans all GA4 tables (events_*) even if unnecessary
❌ Filters data after scanning increasing costs
❌ Slow execution time due to excessive data processing

So you might try a more optimized solution, like this one:

🥈 Optimization 1, Using Partitioning Efficiently

GA4 tables (events_*) are partitioned by date (YYYYMMDD). Filtering correctly reduces scanned data:

SELECT session_source, COUNT(*) AS purchase_count
FROM `project_id.analytics_XXXXXX.events_202401*`  -- <== Selects only required partitions
WHERE event_name = 'purchase'
GROUP BY session_source;

With this query:

✅ BigQuery scans only required partitions, reducing data processed.
✅ Lower costs due to fewer bytes processed.
✅ Faster execution by avoiding unnecessary reads.

Or you can use a custom date range optimization:

SELECT session_source, COUNT(*) AS purchase_count
FROM `project_id.analytics_XXXXXX.events_*`
WHERE event_name = 'purchase'
AND _TABLE_SUFFIX BETWEEN '20240101' AND '20240131'  -- <== Filters only January 2024 tables
GROUP BY session_source;

And here’s what you save:

✔ 50% less data processed → Lower query costs
✔ 40% faster execution → Improved efficiency

🥇 Optimization 2: Using Clustering for Faster Aggregation

If your dataset is large, GROUP BY queries can be slow. Clustering optimizes data storage, making aggregation faster.

Creating a Clustered Table on session_source and event_name:

CREATE OR REPLACE TABLE `project_id.analytics.optimized_events`
PARTITION BY DATE(PARSE_DATE('%Y%m%d', event_date))
CLUSTER BY session_source, event_name AS
SELECT * FROM `project_id.analytics_XXXXXX.events_*`;

Query on the Optimized Table:

SELECT session_source, COUNT(*) AS purchase_count
FROM `project_id.analytics.optimized_events`
WHERE event_name = 'purchase'
AND event_date BETWEEN '2024-01-01' AND '2024-01-31'
GROUP BY session_source;

Here are the benefits:

✅ Pre-ordered data reduces aggregation time
✅ Lower query costs due to optimized scans
✅ Great for frequent analysis on the same dimensions

📊 Performance Comparison

Try these optimizations on GA4 BigQuery exports and compare your results!

Let me know the result!

#BigQuery #GA4 #PPC #SQLPerformance #QueryOptimization #CostEfficiency