GA4 Revenue Metrics to BigQuery Mapping

GA4 BigQuery

By mapping GA4 Revenue metrics to BigQuery fields, you can:

#1 Track the total revenue generated from various sources, such as purchases, in-app transactions, and subscriptions. 

#2 Pinpoint which products, services, or campaigns are the main revenue drivers. This knowledge is invaluable for optimizing marketing strategies and resource allocation.

#3 Calculate profitability by subtracting costs such as refunds, discounts, and operational expenses from total revenue.

#4 Analyze how changes in pricing affect revenue and make data-driven decisions to adjust pricing strategies for maximum profitability.

#5 Use historical revenue data to forecast future trends and prepare better for upcoming demands and market changes.

Here’s a breakdown of GA4 Revenue metrics and their corresponding BigQuery fields:

GA4 Revenue MetricsWhat it isBigQuery Field Name Formula
ARPPUMeasures the average revenue generated by each user who has made a purchase.SUM(ecommerce.purchase_revenue) / COUNT(DISTINCT user_pseudo_id WHERE event_name = 'purchase')
ARPURepresents the average amount of revenue generated by each active user.SUM(ecommerce.purchase_revenue) / COUNT(DISTINCT user_pseudo_id)
Average daily revenueThe average amount of revenue collected each day within a specified period.SUM(ecommerce.purchase_revenue) / COUNT(DISTINCT event_date)
Average purchase revenueThe average revenue obtained from purchases over a specified time frame.SUM(ecommerce.purchase_revenue) / COUNT(event_name = 'purchase')
Average purchase revenue per userThe total revenue obtained from purchases divided by the number of purchasing users.AVG(ecommerce.purchase_revenue)
Max daily revenueThe highest revenue recorded in a single day during the selected time frame.MAX(SUM(ecommerce.purchase_revenue) GROUP BY event_date)
Min daily revenueThe lowest revenue recorded in a single day during the selected time frame.MIN(SUM(ecommerce.purchase_revenue) GROUP BY event_date)
Total revenueThe combined total of all revenue from purchases, adjusted for any refunds.SUM(ecommerce.purchase_revenue) - SUM(ecommerce.refund_value)

Points to Consider:

ARPU (Average Revenue Per User): While the formula calculates total revenue divided by the number of distinct users, it does not differentiate between active users. A more precise definition of ARPU considers only active users within a specific timeframe. To refine this metric, you might need to incorporate additional filters based on user activity data.

Other articles on GA4 BigQuery

#1 BigQuery Introduction

  1. How to create a new Google Cloud Platform account.
  2. How to create a new BigQuery project.
  3. What is Google BigQuery Sandbox and how to use it.
  4. Understanding the BigQuery User Interface.
  5. What is BigQuery Data Transfer Service & how it works.
  6. How to create data transfer in BigQuery.
  7. Connect and transfer data from Google Sheets to BigQuery.
  8. How to access BigQuery Public Data Sets.
  9. Best Supermetrics Alternative – Dataddo.

#2 GA4 BigQuery Introduction

  1. Google Analytics 4 BigQuery Tutorial for Beginners to Advanced.
  2. GA4 Bigquery Export Schema Tutorial.
  3. GA4 BigQuery – Connect Google Analytics 4 with BigQuery.
  4. events_ & events_intraday_ tables in BigQuery for GA4 (Google Analytics 4).
  5. pseudonymous_users_ & users_ data tables in BigQuery for GA4 (Google Analytics 4).
  6. How to access GA4 Sample Data in BigQuery.
  7. Advantages of using Google BigQuery for Google Analytics 4.
  8. Impact of Google Advanced Consent Mode on BigQuery & GDPR.

#3 GA4 BigQuery Data Transfer

  1. How to Connect and Export Data from GA4 to BigQuery
  2. How to backfill GA4 data in BigQuery.
  3. How to overcome GA4 BigQuery Export limit.
  4. How to Send Custom GA4 Data to BigQuery.
  5. How to backup Universal Analytics data to BigQuery.
  6. How to send data from Google Ads to BigQuery.
  7. How to send data from Google Search Console to BigQuery.
  8. Sending data from Google Analytics to BigQuery without 360.
  9. How to send data from Facebook ads to BigQuery.
  10. How to pull custom data from Google Analytics to BigQuery.

#4 BigQuery Cost Optimization

  1. Guide to BigQuery Cost Optimization.
  2. Using Google Cloud pricing calculator for BigQuery.
  3. Cost of using BigQuery for Google Analytics 4.

#5 Query GA4 BigQuery Data

  1. How to query Google Analytics data in BigQuery.
  2. Query GA4 data in BigQuery without understanding SQL.
  3. Using GA4 BigQuery SQL generator to create SQL queries.
  4. New vs Returning users in GA4 BigQuery data table.
  5. GA4 BigQuery Composer Tutorial for ChatGPT.
  6. How to track GA4 BigQuery Schema Change.
  7. Calculating Sessions and Engaged Sessions in GA4 BigQuery.
  8. Calculating Total Users in GA4 BigQuery.

#6 GA4 to BigQuery Dimension/Metric Mapping.

  1. GA4 to BigQuery Mapping Tutorial.
  2. GA4 Attribution Dimensions to BigQuery Mapping.
  3. GA4 Google Ads Dimensions to BigQuery Mapping.
  4. GA4 Demographic Dimensions to BigQuery Mapping.
  5. GA4 Ecommerce Dimensions to BigQuery Mapping.
  6. GA4 Event-Scoped Ecommerce Metrics to BigQuery Mapping.
  7. GA4 Item-Scoped Ecommerce Metrics to BigQuery Mapping.
  8. GA4 Revenue Metrics to BigQuery Mapping.
  9. GA4 Event Dimensions to BigQuery Mapping.
  10. GA4 Event Metrics to BigQuery Mapping.
  11. GA4 Geography Dimensions to BigQuery Mapping.
  12. GA4 Link Dimensions to BigQuery Mapping.
  13. GA4 Page/Screen Dimensions to BigQuery Mapping.
  14. GA4 Page/Screen Metrics to BigQuery Mapping.
  15. GA4 Platform/Device Dimensions to BigQuery Mapping.
  16. GA4 User-Scoped Traffic Dimensions to BigQuery Mapping.
  17. GA4 Session-Scoped Traffic Dimensions to BigQuery Mapping.
  18. GA4 Session Metrics to BigQuery Mapping.
  19. GA4 User Dimensions to BigQuery Mapping.
  20. GA4 User Metrics to BigQuery Mapping.
  21. GA4 Advertising Metrics to BigQuery Mapping.

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