Webinar recording

Identifying Traffic Sources Using Raw GA4 Data in BigQuery

It's no secret that the Traffic acquisition report is one of the most popular in GA4. If you've tried to build it from raw GA4 export data to Google BigQuery, you know that there are many ways to do it, and even more nuances to consider. It's time to deal with it all.

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Who is this Course For?

  • Web analysts

    • who want to gain a deep understanding of traffic source identification in GA4
    • who have tried to implement their own logic for last non-direct attribution but faced difficulties
    • who generate reports based on GA4 data in BigQuery and have already discovered that the data in the session_traffic_source_last_click object isn't always accurate
  • PPC SPECIALISTS

    • who want to learn more about traffic source identification in GA4 and understand why conversions from ads sometimes aren't attributed to the campaigns
    • who are tired of searching for the right data in the interface and dealing with sampling, and finally want to see 100% of the data
    • who aim to use BigQuery to build automated reporting for their ad campaigns
  • MARKETERS

    • who try to figure out how GA4 determines traffic sources
    • who periodically use GA4 export data to BigQuery in their work
    • who want to analyze data across popular parameters without the limitations imposed by the GA4 interface
programs

Course Syllabus

  • Module 1.

    Theory and Practice

    What You Will Learn
    • How traffic sources are identified in the GA4 interface
    • Differences between various parameters that provide information about traffic sources
    • Discrepancies between Google's official documentation and the reality in GA4
    • Traffic sources in BigQuery: where to find the data
    • The nuances of the "session_start" event
    • How it's possible for a session to have no page views
    What You Will Achieve

    You'll learn how GA4 identifies sources at different levels, the difference between the six parameters in Source/Media, and how theory differs from practice. You'll also understand where to find traffic source data in the GA4 export schema in BigQuery, along with the specific characteristics of these fields. Additionally, you'll explore common methods for determining traffic sources at the session level, along with their pros and cons.

    What You Will Learn
    • What is a lookback window and why it matters
    • How to incorporate additional logic from GA4 settings, such as excluding payment domains, when writing an SQL query for the last non-direct click attribution model
    • The role of the page_location and page_referrer parameters in identifying traffic sources
    • We will review the script that the PROANALYTICS.TEAM uses in their projects
    • How to add breakdowns by devices and geographic parameters to attribution data
    • Bonus: we'll calculate key metrics for main traffic sources — number of sessions, visitors, conversions, conversion rate, and revenue
    What You Will Achieve

    You'll learn how to identify traffic sources using the last non-direct attribution model and leverage this information to build the reports you need. You'll also discover how to add extra parameters to attribution data and correctly calculate key metrics based on GA4 export data in BigQuery. By the end, you'll have a ready-to-use solution for generating reports on users, sessions, conversions, revenue, and breakdowns by traffic sources, campaigns, devices, and countries. You'll also learn how to adapt this solution to your specific needs, if required.

how much?

Course Fee

Your Ticket to the World of AnalyticsYouTodayProAnalytics.Price:Duration:$30AT YOUR OWN PACEYour Ticket to the World of AnalyticsYouTodayProAnalytics.Price:Duration:$30AT YOUR OWN PACE
Speakers

Speakers of webinar

Maks Hapchuk
Maks Hapchuk

CEO of PROANALYTICS ACADEMY & PROANALYTICS.TEAM

  • In the field of Internet marketing since 2015

  • Certified Google Analytics Specialist

  • The author of the blog Analytics Tips and the Telegram channel of the same name about analytics.

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Natalie Fedorenko
Natalie Fedorenko

Web Analyst

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