GA4 User Acquisition Report in BigQuery: Getting Insights for Data-Driven Marketing

Google Analytics 4 (GA4) offers valuable insights into user acquisition, but sometimes you need more flexibility. Creating a GA4 user acquisition report in BigQuery lets you dig deeper into your data.

You can build a custom GA4 user acquisition report in BigQuery using SQL queries to analyze how users find your website. This approach gives you more control over the metrics and dimensions you want to include in your report.

By replicating the GA4 user acquisition report in BigQuery, you can overcome limitations of the standard GA4 interface. It allows for more advanced analysis and the ability to combine GA4 data with other sources for a comprehensive view of your user acquisition efforts.

Accessing GA4 Data in BigQuery

Google Analytics 4 (GA4) allows exporting data to BigQuery for advanced analysis. This integration enables powerful querying and customization of GA4 data using SQL.

Setting Up BigQuery Export for GA4 Data

To start using GA4 data in BigQuery, you need to set up the export. First, link your GA4 property to a Google Cloud project. Then, enable BigQuery export in GA4 settings. Choose the BigQuery project and dataset where you want the data to go.

GA4 will create tables in your chosen dataset. These tables contain raw event data from your property. The export happens daily, usually a few hours after midnight in your property’s time zone.

Make sure your Google Cloud project has enough quota for BigQuery storage and queries. GA4 exports can generate large amounts of data for busy properties.

Understanding the GA4 BigQuery Export Schema

The GA4 BigQuery export schema has a specific structure. It uses nested and repeated fields to store event data efficiently. The main tables are events_YYYYMMDD, containing daily event data.

Key fields include:

• event_date: The date of the event
• event_timestamp: The exact time the event occurred
• event_name: The type of event (e.g. page_view, purchase)
• user_pseudo_id: A unique ID for each user

Events have custom parameters and user properties as nested fields. To query these, you’ll need to use UNNEST in your SQL queries.

Timestamps in the export use microsecond precision. You may need to parse dates and timestamps for proper analysis.

Foundational Concepts in GA4 Reporting

GA4 reporting relies on key metrics, dimensions, and scoping to provide meaningful insights. These elements form the backbone of effective analytics and help businesses make data-driven decisions.

Key Metrics and Dimensions in GA4

GA4 uses metrics and dimensions to measure and categorize data. Metrics are quantitative measurements like Engagement Rate and Conversions. Dimensions describe qualitative aspects of users, such as Default Channel Group.

Key metrics in GA4 include:

• Engaged Sessions
• Average Engagement Time
• Event Count
• Conversions

Important dimensions are:

• Source/Medium
• Campaign
• Device Category
• User Type

GA4 allows custom metrics and dimensions for tailored reporting. This flexibility helps businesses track specific KPIs relevant to their goals.

Scope and Its Importance in Analytics

Scope defines the level at which data is collected and analyzed in GA4. It’s crucial for accurate reporting and interpretation of data.

GA4 has three main scopes:

1. User-level
2. Session-level
3. Event-level

User-level scope tracks metrics across multiple sessions. Session-level focuses on a single visit. Event-level captures individual interactions.

Understanding scope helps in:

• Choosing the right metrics for analysis
• Interpreting data accurately
• Creating meaningful reports

For example, the User acquisition report focuses on new users, while the Traffic acquisition report considers all sessions. This difference in scope provides distinct insights into user behavior and traffic patterns.

Creating User Acquisition Reports

User acquisition reports provide key insights into how people find and interact with your website. These reports help analyze traffic sources, campaigns, and user behavior to optimize marketing efforts.

Building Blocks of User Acquisition Reports

User acquisition reports in Google Analytics 4 (GA4) rely on several core components. The source/medium dimension is crucial, showing where users came from. Sources include search engines, social media, or direct traffic. Mediums describe how users arrived, like organic search or paid ads.

Campaign tracking is another vital element. It allows marketers to measure the effectiveness of specific marketing initiatives. GA4 automatically tracks Google Ads campaigns, but custom campaign parameters can be added to URLs for other sources.

User acquisition reports also include metrics like:

• New users
• Sessions
• Conversions
• Engagement rate

These metrics help evaluate the quality of traffic from different sources.

Customizing Reports for Targeted Insights

Building custom user acquisition reports in BigQuery allows for deeper analysis. Start by selecting relevant dimensions and metrics for your business goals. Common dimensions include:

• Source
• Medium
• Campaign
• Landing page

Metrics might include:

• Revenue
• Conversion rate
• Average session duration

Create segments to focus on specific user groups or behaviors. For example, segment by device type or geographic location to uncover insights about different user segments.

Use BigQuery’s SQL capabilities to join GA4 data with other datasets. This can provide a more complete picture of the user journey and acquisition funnel.

Querying Data for Acquisition Analysis

Analyzing user acquisition data in GA4 requires specific SQL queries and techniques to extract meaningful insights. The following subsections explore basic and advanced approaches to query GA4 data in BigQuery.

Basic SQL Queries for User Acquisition Data

To start analyzing user acquisition data, simple SQL queries can provide valuable information. Begin by selecting key fields like User_Pseudo_Id, GA_Session_Id, and traffic source dimensions.

Here’s a basic query to get started:

SELECT
  User_Pseudo_Id,
  GA_Session_Id,
  Traffic_Source.Source,
  Traffic_Source.Medium
FROM
  `your_project.your_dataset.events_*`
WHERE
  _TABLE_SUFFIX BETWEEN '20240101' AND '20240108'
LIMIT 1000

This query retrieves user IDs, session IDs, and traffic sources for events within a specific date range. It’s a good starting point for understanding user acquisition patterns.

Advanced Techniques to Unnest GA4 Data

GA4 data in BigQuery often contains nested structures, requiring advanced techniques to extract detailed information. The UNNEST function is crucial for working with event parameters and custom dimensions.

Here’s an example of using UNNEST:

SELECT
  User_Pseudo_Id,
  (SELECT value.string_value FROM UNNEST(Event_Params) WHERE key = 'page_location') AS page_location,
  (SELECT value.int_value FROM UNNEST(Event_Params) WHERE key = 'ga_session_number') AS session_number
FROM
  `your_project.your_dataset.events_*`
WHERE
  Event_Name = 'page_view'
LIMIT 1000

This query unnests event parameters to extract page location and session number for page view events. It allows for more detailed analysis of user behavior and acquisition paths.

Analyzing User Acquisition Metrics

User acquisition metrics help measure the effectiveness of marketing efforts and user growth. These metrics provide insights into user engagement, revenue impact, and overall business performance.

Evaluating Sessions and User Engagement

Sessions are a key metric for understanding user behavior. They represent the number of times users interact with a website or app. GA4 export data in BigQuery allows analysts to track sessions and engaged sessions.

Engaged sessions show deeper user involvement. These are sessions where users spend more time or take specific actions on the site. By comparing total sessions to engaged sessions, businesses can gauge the quality of their traffic.

Active users differ from total users. This metric focuses on users who have taken meaningful actions within a set time frame. It helps identify the most valuable segments of the user base.

Understanding User Growth and Revenue Impact

New users indicate the success of acquisition strategies. This metric shows how many first-time visitors a site or app attracts. Comparing new users to total users reveals the rate of audience expansion.

User growth directly affects revenue. By linking user acquisition data with revenue figures, businesses can calculate the return on investment for their marketing efforts. This helps in making informed decisions about budget allocation.

Custom calculations in BigQuery allow for advanced analysis. For example, businesses can determine the average revenue per user or the lifetime value of different user segments.

Tracking these metrics over time reveals trends in user behavior and business performance. It helps identify successful marketing channels and areas that need improvement.

Reporting on Traffic Acquisition

Traffic acquisition is key for understanding how users find your website. It helps you track which channels bring in the most visitors and which ones need improvement.

Traffic Channels and Their Significance

Traffic channels show how users reach your site. They include organic search, paid search, social media, email, and direct visits. Each channel plays a different role in your marketing strategy.

Organic search brings users who find you through search engines. It’s often a sign of good SEO. Paid search comes from ads you buy on search platforms. It can bring quick results but costs money.

Social media traffic shows how well your social posts perform. Email traffic measures the success of your email campaigns. Direct traffic includes users who type your URL or use bookmarks.

By looking at these channels, you can see which ones work best. This helps you decide where to focus your efforts and budget.

Using Default Channel Groupings in GA4

GA4 uses default channel groupings to sort traffic. These groups make it easier to understand your data. They put similar traffic sources together.

The main groups are:

• Organic Search
• Paid Search
• Social
• Email
• Direct
• Affiliates
• Display

You can see these groups in your GA4 Traffic Acquisition Report. This report shows how much traffic each channel brings. It also shows other details like new users and engagement.

Using these groups helps you compare different types of traffic. You can spot trends and make better marketing choices. For example, if social brings more engaged users, you might want to do more social media marketing.

Engagement and Conversion Analysis

Analyzing user engagement and conversions is key to understanding how well your website or app performs. These metrics help you measure the effectiveness of your acquisition strategies and user interactions.

Measuring Engagement with Time Metrics

Engagement Time is a crucial metric in GA4 that shows how long users interact with your content. It helps gauge the quality of user interactions beyond simple page views.

Average Engagement Time per session gives insight into how much time users spend on your site. This metric can be calculated in BigQuery by dividing total engagement time by the number of sessions.

To track engagement in BigQuery:

1. Query event data
2. Filter for engaged sessions
3. Calculate total engagement time
4. Divide by session count

Use this data to identify which acquisition channels bring in the most engaged users. Compare engagement times across different user segments to spot trends and areas for improvement.

Tracking and Analyzing Conversions

Conversions are vital for measuring the success of your acquisition efforts. In GA4, any event can be marked as a conversion, giving you flexibility in tracking important user actions.

To analyze conversions in BigQuery:

1. Identify conversion events
2. Query event data for these specific events
3. Count occurrences per user or session

User Conversion Rate shows the percentage of users who complete a desired action. Calculate this by dividing the number of users who convert by the total number of users.

Create custom conversion funnels in BigQuery to track user paths to conversion. This helps identify drop-off points and optimize the user journey.

Compare conversion rates across different acquisition channels to determine which sources bring in the most valuable users. Use this data to refine your marketing strategies and improve overall conversion performance.

GA4 Reporting for Beginners

GA4 offers powerful reporting tools for new users. Learning to navigate the interface and create basic dashboards in BigQuery are key first steps.

Navigating the GA4 Interface

The GA4 interface is user-friendly for beginners. Users can find the User acquisition report under the Acquisition menu. This report shows how new users discover a website or app.

GA4 groups traffic sources into Default Channel Groups. These include:

• Organic Search
• Paid Search
• Social
• Direct
• Email
• Referral

Users can click on each channel to see more details. The interface also allows for date range selection and comparison periods.

Creating Your First Basic BigQuery Dashboard

BigQuery lets users analyze GA4 data in depth. To start, one needs to connect GA4 to BigQuery. This link allows data to flow from GA4 to BigQuery.

Next, users can write SQL queries to extract data. A simple query might count users by source:

SELECT traffic_source.source, COUNT(DISTINCT user_pseudo_id) as users
FROM `your_project.your_dataset.events_*`
GROUP BY traffic_source.source
ORDER BY users DESC

This query counts unique users for each traffic source. Users can then visualize this data in tools like Google Data Studio or Looker Studio.

Analysis and Reporting Best Practices

Effective data analysis and actionable reports are key to getting value from GA4 user acquisition data in BigQuery. These practices help turn raw data into useful insights.

Effective Data Analysis Techniques

Start by cleaning and validating the GA4 export data in BigQuery. Check for any issues like missing values or incorrect attribution.

Use SQL queries to segment users by acquisition source, medium, and campaign. Compare conversion rates and engagement metrics across segments.

Create cohorts based on first-touch attribution to track long-term user behavior. Analyze how different acquisition channels impact retention and lifetime value.

Look for trends over time in key metrics like new user growth and cost per acquisition. Use statistical techniques to identify significant changes.

Creating Actionable Reports

Design reports that answer specific business questions about user acquisition. Focus on metrics that drive decisions, not just vanity numbers.

Build custom acquisition reports in BigQuery that go beyond standard GA4 reports. Include calculated fields like customer acquisition cost and projected lifetime value.

Use data visualization tools to create clear, easy-to-understand charts and graphs. Highlight key insights with callout text and conditional formatting.

Set up automated reporting to deliver acquisition insights to stakeholders regularly. Include recommendations for optimizing marketing spend and tactics based on the data.

Understanding User and Session Identifiers

GA4 uses unique identifiers to track users and sessions. These identifiers help analyze user behavior and session data in BigQuery. They are key to getting accurate insights from GA4 data.

Distinct User Identifiers in GA4

GA4 uses two main user identifiers: User_Pseudo_Id and Client_Id. The User_Pseudo_Id is a unique identifier for each user in GA4 BigQuery export. It stays the same across sessions for a single user.

Client_Id is similar but may change if a user clears cookies. For logged-in users, GA4 can use a User_Id. This ID links user activity across devices and platforms.

These IDs help track user journeys and behavior over time. They’re crucial for user-level analysis in BigQuery.

Session Identification and Analysis

Sessions in GA4 are tracked using the GA_Session_Id. This ID is unique for each session. It changes when a new session starts.

The GA_Session_Id helps group user actions within a single visit. It’s key for analyzing session-level metrics like engagement time and bounce rate.

In BigQuery, you can use GA_Session_Id to count sessions or group events. This lets you see how users interact during each visit.

The raw data in BigQuery includes both user and session IDs. This allows for detailed analysis of user behavior across multiple sessions.

Advanced User Acquisition Insights

Digging deeper into user acquisition data reveals valuable patterns and metrics. Advanced analysis techniques help uncover hidden trends in returning user behavior and engagement levels.

Identifying Patterns in Returning User Data

Traffic acquisition reports in BigQuery allow for in-depth analysis of returning users. By examining data over time, businesses can spot trends in how often users come back.

Look at intervals between visits to gauge user loyalty. Short gaps may indicate high engagement, while longer breaks suggest room for improvement.

Compare acquisition channels for returning users. Some sources might bring in one-time visitors, while others attract loyal customers.

Track changes in returning user behavior across different campaigns or seasons. This helps optimize marketing efforts for better long-term engagement.

Analyzing Page Views and Entrances as Engagement Metrics

Page views and entrances offer insights into user engagement levels. BigQuery allows detailed examination of these metrics across different user segments.

Compare page views per session for users from various acquisition channels. Higher numbers often indicate more engaged visitors.

Analyze entrance pages by source to see which content attracts users from different channels. This helps tailor landing pages for better performance.

Track how page views and entrances change over time. Sudden drops may signal issues, while increases show successful engagement strategies.

Use these metrics to calculate engagement rates. Divide total page views by entrances to see how deeply users explore the site after arriving.

Incorporating Additional Data Sources

Integrating data from Google Ads and Search Console enhances GA4 user acquisition reports in BigQuery. This allows for more comprehensive analysis and customization of reports for ecommerce businesses.

Integrating Google Ads and Search Console Data

To improve GA4 user acquisition reports, combine data from Google Ads and Search Console. This integration provides a fuller picture of traffic sources and user behavior.

Google Ads data adds details on paid campaigns. It shows which ads and keywords drive acquisitions. Search Console data reveals organic search performance. It highlights queries and landing pages bringing in new users.

To integrate these sources:

1. Export Google Ads and Search Console data to BigQuery
2. Join this data with GA4 export using common identifiers
3. Create SQL queries to combine and analyze the datasets

This approach allows for more accurate attribution and ROI calculations.