Data Archives - Web Data Geek https://webdatageek.com/tag/data/ Web Analytics | GA4 & BigQuery Thu, 29 Feb 2024 16:57:46 +0000 en-US hourly 1 https://wordpress.org/?v=6.5.2 https://i0.wp.com/webdatageek.com/wp-content/uploads/2020/10/DALL·E-2023-03-10-20.33.29-create-circle-logo-using-_web-data-geek_-text-1.png?fit=32%2C4&ssl=1 Data Archives - Web Data Geek https://webdatageek.com/tag/data/ 32 32 194758041 Impact of iOS 17+ Updates on Digital Advertising and the Importance of UTM Parameters https://webdatageek.com/impact-of-ios-17-updates-on-digital-advertising-and-the-importance-of-utm-parameters/ https://webdatageek.com/impact-of-ios-17-updates-on-digital-advertising-and-the-importance-of-utm-parameters/#respond Thu, 29 Feb 2024 16:57:45 +0000 https://webdatageek.com/?p=1815 In the digital advertising landscape, the recent iOS 17+ updates have introduced significant changes, particularly affecting how traffic from Google and Meta Ads is tracked and reported. Apple’s initiative to enhance user privacy through its ‘Link Tracking Protection’ feature, present in the latest update, has created a new challenge for digital marketers relying on GA4 […]

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In the digital advertising landscape, the recent iOS 17+ updates have introduced significant changes, particularly affecting how traffic from Google and Meta Ads is tracked and reported. Apple’s initiative to enhance user privacy through its ‘Link Tracking Protection’ feature, present in the latest update, has created a new challenge for digital marketers relying on GA4 reports for analytics and insights.

Understanding ‘Link Tracking Protection’

Apple’s iOS 17+ updates have rolled out a feature known as ‘Link Tracking Protection’. This feature operates by removing the ‘gclid’ (Google Click Identifier) and ‘fbclid’ (Facebook Click Identifier) tracking parameters when a user navigates via Safari in private mode, or uses Apple’s native Mail app or Messages. These parameters are crucial for tracking the source of web traffic, and their removal can lead to misreported data.

Consequences for Google and Meta Ads

Without these identifiers, traffic originating from Google and Meta ads may be inaccurately categorized as direct or organic in GA4 reports. This misclassification is particularly troubling for Google Ads advertisers. While Meta advertisers might not feel a significant impact due to the prevalence of the native Facebook app among users, the updates pose a direct challenge to Google’s ability to track ad performance.

The Google Ads Dilemma

For Google Ads, these updates disable the auto-tagging feature when users click on ads in Safari’s private mode. Auto-tagging is vital for Google as it gathers extensive information to optimize ad performance. Losing this data means losing critical insights into user behavior and ad effectiveness.

The Solution: UTM Parameters and Server-Side Tagging

To counteract these tracking limitations, digital marketers must adapt. One effective strategy is the use of UTM parameters in ad URLs. Unlike ‘gclid’ and ‘fbclid’, iOS 17+ updates do not strip away UTM parameters, making them a reliable alternative for tracking ad performance in GA4 reports.

Furthermore, considering server-side tagging can offer a robust solution. This method mitigates tracking limitations imposed by browser and device restrictions, providing a more controlled and reliable data collection mechanism.

Navigating the New Landscape

The iOS 17+ updates underscore the evolving nature of digital advertising and the importance of flexibility in marketing strategies. Advertisers and marketers must stay informed about these changes and adapt their tactics accordingly to ensure accurate tracking and reporting. By leveraging UTM parameters and exploring server-side tagging, businesses can continue to gain valuable insights from their digital advertising efforts, despite the challenges posed by the latest iOS updates.

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Understanding the Differences between Data in Universal Analytics (UA) and Google Analytics 4 (GA4) https://webdatageek.com/understanding-the-differences-between-data-in-universal-analytics-ua-and-google-analytics-4-ga4/ https://webdatageek.com/understanding-the-differences-between-data-in-universal-analytics-ua-and-google-analytics-4-ga4/#respond Thu, 22 Jun 2023 22:53:42 +0000 https://webdatageek.com/?p=1772 Introduction In the realm of digital analytics, data plays a pivotal role in guiding decision-making and propelling business growth. The introduction of Google Analytics 4 (GA4), an avant-garde analytics platform, brings noticeable changes in how data is collected, organised, and reported compared to the traditional Universal Analytics (UA). These disparities may cause differences in the […]

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Introduction

In the realm of digital analytics, data plays a pivotal role in guiding decision-making and propelling business growth. The introduction of Google Analytics 4 (GA4), an avant-garde analytics platform, brings noticeable changes in how data is collected, organised, and reported compared to the traditional Universal Analytics (UA). These disparities may cause differences in the figures reported by both platforms. This article delves into the reasons behind these discrepancies and outlines the contrasts between UA and GA4.

1. Tracking Model

The tracking models of UA and GA4 show fundamental differences. UA relies on cookies to track user interactions, whereas GA4 adopts an event-based model centred on user engagement with specific events. This change in tracking methodology can lead to inconsistencies when comparing data between the two platforms.

2. Data Collection and Reporting

UA primarily collects data through pageviews, events, and custom dimensions. GA4, conversely, presents a more adaptable and customisable approach to data collection using events, parameters, and user properties. This shift in data collection methodology can result in differences in the figures reported between the two platforms.

3. User Engagement Measurement

UA and GA4 diverge significantly in their approach to measuring user engagement. UA concentrates on metrics such as sessions, session duration, and bounce rate, whereas GA4 places emphasis on user engagement through events and user properties. This variance in measuring user engagement can cause differences in metrics and interpretations of user behaviour.

4. Cross-Device Tracking

Tracking users across different devices is a crucial capability for modern analytics platforms. UA achieves cross-device tracking by utilising persistent cookies, allowing it to connect user interactions across a range of devices. In contrast, GA4 utilises a privacy-centric approach relying on probabilistic and deterministic methods to associate user interactions across devices. These contrasting approaches can result in differences in the figures reported, particularly when analysing user behaviour across multiple devices.

5. Data Sampling

Data sampling is the process of using a subset of data to estimate and extrapolate insights about a larger dataset. In UA, sampling is more prevalent, particularly for high-traffic websites, leading to discrepancies in the reported figures. GA4, on the other hand, aims to mitigate data sampling by leveraging the BigQuery integration for more accurate and precise reporting.

Conclusion

The transition from Universal Analytics to Google Analytics 4 introduces substantial changes in the way data is collected, organised, and reported. The shift from cookies to an event-based tracking model, coupled with differences in data collection methodologies, user engagement measurement, cross-device tracking, and data sampling, contribute to discrepancies in the figures reported between the two platforms. It is essential for businesses and analysts to understand these disparities and adapt their analytics strategies accordingly, to derive meaningful insights and make informed decisions.

By embracing the new capabilities offered by GA4 and leveraging its event-based tracking model, businesses can gain a deeper understanding of user behaviour, comprehend cross-device interactions better, and make data-informed decisions in the ever-evolving digital landscape.

Note: While the article provides a comprehensive overview of the differences between UA and GA4, it’s important to consult the official Google Analytics documentation and stay updated with the latest developments to ensure an accurate understanding and implementation of the platforms.

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Navigating the Landscape of GA4 and Server-Side GTM https://webdatageek.com/navigating-the-landscape-of-ga4-and-server-side-gtm-2/ https://webdatageek.com/navigating-the-landscape-of-ga4-and-server-side-gtm-2/#respond Thu, 22 Jun 2023 22:44:19 +0000 https://webdatageek.com/?p=1764 Let’s delve into the world of digital analytics and marketing. Two crucial players, Google Analytics 4 (GA4) and server-side Google Tag Manager (GTM), are transforming the way we approach data tracking and analysis. They offer a forward-thinking method to understand customer behaviour, refine marketing strategies, and keep pace with the rapidly evolving digital landscape. Understanding […]

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Let’s delve into the world of digital analytics and marketing. Two crucial players, Google Analytics 4 (GA4) and server-side Google Tag Manager (GTM), are transforming the way we approach data tracking and analysis. They offer a forward-thinking method to understand customer behaviour, refine marketing strategies, and keep pace with the rapidly evolving digital landscape.

Understanding Server-Side Tracking

Server-side tracking deploys tracking scripts or ad pixels from a server, rather than directly from the user’s web browser. In this setup, the interaction takes place directly between your server and third-party servers such as Google Cloud Platform or Amazon Web Services. The result? No third-party JavaScript code on your website and no third-party cookies in your users’ browsers. This approach allows for more precise data collection and limits disruptions from browser limitations or ad-blocking software.

A Look at Client-Side Tracking

Before we fully comprehend server-side tracking, it’s crucial to grasp client-side tracking. The client-side method runs tracking codes or ad pixels in the user’s browser, communicating directly with third-party servers. While this method is common, it’s increasingly unreliable due to stricter browser restrictions and the proliferation of ad blockers.

Server-Side Tracking on the Rise

The landscape of digital marketing is shifting, with ad blockers and web browser privacy enhancements creating significant challenges for data collection via client-side tracking. Notably, the Brave browser now defaults to blocking Google Analytics and GTM, whilst Safari’s Intelligent Tracking Prevention (ITP) feature restricts third-party cookies. Google also plans to phase out support for third-party cookies. These changes have made client-side tracking less reliable, driving marketers to explore server-side alternatives.

The Benefits of Server-Side Tracking

Server-side tracking offers several compelling advantages:

    1. Accurate Conversion Tracking: Server-side tracking enables API-based conversion tracking, providing a more accurate understanding of conversions compared to traditional browser-based methods.
    2. First-Party Context: Server-side tracking lets you collect third-party data in the context of first-party data, bypassing some tracking restrictions.
    3. Tracking Resilience: Unlike client-side tracking, server-side tracking is not blocked by web browsers or ad blockers, ensuring more consistent data collection.
    4. Data Control: You retain more control over the data sent to third-party vendors
    5. Improved Website Speed: As marketing and analytics tags are not fired from users’ browsers, website speed can be enhanced.

API-Based Conversion Tracking and Server-Side GTM

Server-side tracking allows for the implementation of API-based conversion tracking, which is less affected by browser-based tracking restrictions and ad blockers. For example, you can leverage Google Ads Conversion API and Facebook Ads Conversion API to provide your ad pixels with more conversion data, thereby boosting the performance of your marketing campaigns.

Server-side tracking can also integrate with Google Tag Manager, enabling server-side tagging. This typically involves running a GTM container in a server-side environment, often referred to as a server-side GTM container.

Find out how to setup GA4 serverside tracking in 10 steps

The Cost of Server-Side Tracking

Despite the numerous advantages, server-side tracking comes at a cost. Google suggests running at least three App Engine instances on the Google Cloud Platform for server-side tracking, which could set you back around $120 per month. The exact cost will depend on your traffic levels and the number of events being tracked.

Nevertheless, for organisations heavily reliant on web analytics for decision making, server-side tracking can offer more reliable data and a better return on investment.

Conclusion

Given the changing landscape of web analytics due to privacy regulations and browser restrictions, server-side tracking has emerged as a promising alternative to client-side tracking. With server-side GTM and GA4, businesses can ensure more robust and accurate data collection, providing deeper insights into user behaviour and campaign performance. Although it comes with additional costs, the advantages make it a worthy investment for businesses serious about their digital marketing efforts.

As always, organisations should carefully consider their specific needs and capabilities when deciding to switch to serverside tracking. Consulting with a digital marketing expert can help clarify the best path forward based on the organisation’s unique circumstances.

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How to fix unassigned traffic in GA4? https://webdatageek.com/how-to-fix-unassigned-traffic-in-ga4/ https://webdatageek.com/how-to-fix-unassigned-traffic-in-ga4/#respond Sat, 25 Mar 2023 00:57:31 +0000 https://webdatageek.com/?p=1744 Unassigned traffic in Google Analytics 4 can be frustrating for website owners and marketers, as it refers to traffic that cannot be attributed to a specific source or medium. This can lead to incomplete or inaccurate data, making it difficult to make informed decisions about your website or app performance. In this article, we’ll discuss […]

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Unassigned traffic in Google Analytics 4 can be frustrating for website owners and marketers, as it refers to traffic that cannot be attributed to a specific source or medium. This can lead to incomplete or inaccurate data, making it difficult to make informed decisions about your website or app performance. In this article, we’ll discuss what unassigned traffic is, why it happens, and how to fix it.

What is Unassigned Traffic?

Unassigned traffic in Google Analytics 4 refers to website or app traffic that GA4 is unable to attribute to a specific source or medium. This means that GA4 cannot determine how users arrived at your website or app, making it difficult to track the effectiveness of your marketing efforts.

Unassigned traffic can happen for several reasons, including:

  1. Direct Traffic: When users type your website URL directly into their browser’s address bar, GA4 considers this as direct traffic and doesn’t attribute it to any source or medium.
  2. Missing Tracking Code: If your tracking code is missing or not installed correctly, GA4 won’t be able to track the traffic and attribute it to any source or medium.
  3. Bot Traffic: GA4 may not be able to determine the source or medium for traffic generated by bots or crawlers.

Why is Unassigned Traffic a Problem?

Unassigned traffic can impact your data accuracy and make it difficult to make informed decisions about your website or app performance. Here are some reasons why unassigned traffic is a problem:

  1. Incomplete Data: Unassigned traffic means that your data is incomplete, making it difficult to understand your audience and how they interact with your website or app.
  2. Inaccurate Attribution: Unassigned traffic means that GA4 cannot attribute traffic to specific marketing channels, such as paid search or social media. This can lead to inaccurate attribution and make it difficult to determine which channels are driving the most traffic and conversions.
  3. Missed Opportunities: Unassigned traffic means that you may be missing out on valuable insights and opportunities to optimize your website or app performance. Without complete data, it’s difficult to identify areas for improvement or potential issues that may be impacting user experience.

How to Fix Unassigned Traffic

Fixing unassigned traffic requires a few steps. Here’s how to do it:

  1. Check Your Tracking Code: The first step in fixing unassigned traffic is to check your tracking code. Ensure that your tracking code is installed correctly and that it is firing on all pages of your website or app. If you’re using a tag manager, ensure that your GA4 tag is firing correctly.
  2. Use Campaign Parameters: If you’re running marketing campaigns, use campaign parameters to track your traffic sources. Campaign parameters allow you to add extra information to your URLs, which GA4 can use to attribute traffic to specific campaigns. You can use Google’s Campaign URL Builder to create custom URLs with campaign parameters.
  3. Check Your Referral Exclusion List: If you’re experiencing unassigned traffic from your own domain or subdomains, check your referral exclusion list. The referral exclusion list tells GA4 which domains to exclude from referral traffic. If your own domain or subdomains are not excluded, GA4 may attribute the traffic to the wrong source or medium.
  4. Monitor Your Traffic Sources: Finally, monitor your traffic sources regularly to ensure that you’re not experiencing any unassigned traffic. Use GA4’s Acquisition reports to identify any unassigned traffic and take steps to fix it.

Conclusion

Unassigned traffic can be a frustrating problem for website owners and marketers, as it can lead to incomplete or inaccurate data. However, by following the steps outlined in this article, you can fix unassigned traffic and ensure that your data is accurate and complete. By fixing unassigned traffic, you can gain valuable insights into your audience and optimize website or app performance. This can lead to increased conversions, better user experience, and ultimately, improved business results. By regularly monitoring and fixing unassigned traffic, you can ensure that your website or app is performing at its best, and that you’re making informed decisions based on accurate data.

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Shopify GA4 Integration Guide https://webdatageek.com/shopify-ga4-integration-guide/ https://webdatageek.com/shopify-ga4-integration-guide/#respond Sat, 25 Mar 2023 00:42:48 +0000 https://webdatageek.com/?p=1740 Shopify now supports GA4 integration, allowing you to leverage the powerful analytics features of GA4 for tracking and optimizing your e-commerce store’s performance. This article will guide you through the process of integrating GA4 with your Shopify store using the Google channel app and provide insights into its benefits and limitations. Why Use GA4 for […]

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Shopify now supports GA4 integration, allowing you to leverage the powerful analytics features of GA4 for tracking and optimizing your e-commerce store’s performance. This article will guide you through the process of integrating GA4 with your Shopify store using the Google channel app and provide insights into its benefits and limitations.

Why Use GA4 for Your Shopify Store?

Enhanced Tracking: GA4 offers advanced tracking features, such as event-based tracking and cross-domain tracking, enabling you to gain deeper insights into user behaviour and interactions on your Shopify store.

Improved Reporting: GA4 provides more in-depth and customizable reporting options, allowing you to analyze and visualize your e-commerce data more effectively.

Future-Proofing: As GA4 is the latest version of Google Analytics, integrating it with your Shopify store ensures you’ll have access to new features and updates as they become available.

Integrating GA4 with Shopify Using the Google Channel App

Install the Google Channel App: To start the integration process, install the Google channel app from the Shopify App Store. This app is used for managing the automated set-up of Google Ads and Google Merchant Centre but can also be used to integrate GA4 with your Shopify store.

Configure the App: Once installed, follow the app’s instructions to link your Google Analytics account and select your GA4 property.

Add GA4 Tracking: Shopify’s GA4 integration currently provides a basic implementation of GA4 tracking. To add more advanced tracking features, such as custom events and conversion tracking, you may need to use additional tools or manually configure your GA4 property.

Limitations of Shopify’s GA4 Integration

Basic Implementation: Shopify’s GA4 integration is relatively basic and does not include advanced tracking features or configuration options. You may need to further customize your GA4 property or use additional tools to achieve your desired level of tracking and reporting.

Manual Configuration Required: Shopify does not automatically configure your GA4 property for you. To make the most of GA4’s features, you’ll need to spend some time configuring your property, setting up custom events, and adjusting your reporting preferences.

Summary

Integrating GA4 with your Shopify store using the Google channel app can provide valuable insights into your store’s performance and help you optimize your marketing strategies. While Shopify’s GA4 integration is relatively basic, customizing your GA4 property and making use of advanced tracking features can further enhance your e-commerce analytics capabilities. If you need assistance with the integration process, consider consulting with a professional or an agency specializing in GA4 and Shopify.

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A Comprehensive Guide to Setting Up Custom Events in GA4 https://webdatageek.com/a-comprehensive-guide-to-setting-up-custom-events-in-ga4/ https://webdatageek.com/a-comprehensive-guide-to-setting-up-custom-events-in-ga4/#respond Fri, 24 Mar 2023 23:46:48 +0000 https://webdatageek.com/?p=1735 Google Analytics 4 (GA4) custom events enable you to track unique user interactions on your website or app, offering deeper insights into user behaviour. These insights can help you optimize your marketing strategies and improve SEO. In this step-by-step, beginner-friendly guide, learn how to set up custom events in GA4 and elevate your analytics game. […]

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Google Analytics 4 (GA4) custom events enable you to track unique user interactions on your website or app, offering deeper insights into user behaviour. These insights can help you optimize your marketing strategies and improve SEO. In this step-by-step, beginner-friendly guide, learn how to set up custom events in GA4 and elevate your analytics game.

  1. Grasp the Concept of Custom Events:

Before diving into the setup process, familiarize yourself with custom events and their benefits. Custom events are user interactions that aren’t covered by GA4’s standard event categories. Examples include clicks on specific buttons, form submissions, or other actions you’d like to track. Setting up custom events lets you gather vital data on user behaviour, informing your marketing and SEO efforts.

  1. Identify Custom Events for Tracking:

First, identify the specific interactions you want to track. Consider your website or app’s goals and which user actions are crucial for success. Compile a list of these custom events to stay organized during the setup process.

  1. Implement Custom Event Tracking:

To set up custom event tracking, add code to your website or app. This usually entails modifying your existing Google Analytics tracking code or using Google Tag Manager (GTM) to create a new tag. We recommend GTM for its ease and flexibility.

  1. Add GA4 Events Using Google Tag Manager:

Follow these steps to create custom events using GTM:

a. Log in to your GTM account and select the desired container.

b. In the left-hand menu, click ‘Tags’, then ‘New’ to create a new tag.

c. Select ‘Google Analytics: GA4 Event’ as the tag type.

d. Input your GA4 Measurement ID (found in your GA4 property settings).

e. Configure the tag by adding the event name and event parameters (e.g., category, label), as well as any other relevant details.

f. Choose a trigger for the tag, such as ‘Click – All Elements’, and define the conditions for firing the tag (e.g., when users click a specific button).

g. Save and publish the tag in GTM.

  1. Test and Refine Custom Event Tracking:

After implementing custom event tracking, ensure it functions correctly. Use GA4’s DebugView or GTM’s Preview mode to test and verify your custom events. Make adjustments as needed to improve data accuracy and gain more valuable insights into user behaviour.

Setting up custom events in GA4 is a powerful way to collect essential data on user interactions, informing your marketing and SEO strategies. Follow this comprehensive guide to harness the full potential of custom events and optimize your website or app for success.

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GA4 and BigQuery Introduction https://webdatageek.com/introduction-to-ga4-and-bigquery/ https://webdatageek.com/introduction-to-ga4-and-bigquery/#respond Fri, 24 Mar 2023 22:03:59 +0000 https://webdatageek.com/?p=1710 Discover the power of GA4 and BigQuery integration to unlock advanced analytics capabilities. Learn how combining Google Analytics 4 with BigQuery allows for in-depth data analysis, custom reporting, and data-driven decision-making Google has introduced a groundbreaking new approach to measuring app and web analytics with Google Analytics 4 (GA4). Though most of us at the […]

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Discover the power of GA4 and BigQuery integration to unlock advanced analytics capabilities. Learn how combining Google Analytics 4 with BigQuery allows for in-depth data analysis, custom reporting, and data-driven decision-making

Google has introduced a groundbreaking new approach to measuring app and web analytics with Google Analytics 4 (GA4). Though most of us at the time of writing this are dreading the move from Universal Analytics to GA4, this innovation marks a significant shift for both app and web analytics.

It’s not all bad news though and one of the most exciting features in GA4 is undoubtedly BigQuery integration. All GA4 property owners can now enable data export to BigQuery and leverage the raw event data collected from their websites and apps.

In the previous version of Google Analytics (Universal Analytics), this integration was exclusive to GA360 enterprise properties. However, GA4 makes data export available for free to everyone; you only pay for actual data storage and querying if you exceed Google Cloud’s free tier limits. Your credit card will only be charged after 1 TB of querying per month and 10 GB of storage.

You can also use the BigQuery sandbox environment without a credit card, but be aware that your data tables may expire after 60 days.

Why should you enable BigQuery linking for GA4?

Some reasons include:

  • Store your data in BigQuery (Google Cloud) and/or send it to your data warehouse in other clouds, such as AWS, Azure, or Snowflake
  • Combine and enrich your data with other marketing/CRM/contextual data
  • Visualize your data using tools like Data Studio, Tableau, Looker, or PowerBI
  • Perform advanced data analysis
  • Use your data as input for (machine learning) models

Don’t waste any time; start sending data immediately, as there’s no backfill for historical data already collected in GA4.

Follow these steps to link Google Analytics 4 to BigQuery

How to Set Up BigQuery Linking in Your Google Analytics 4 Property (GA4) There’s no backfill, so start collecting data now. Learn how to set up BigQuery export from Google Analytics 4 (GA4).

To set up GA4 and BigQuery integration from scratch, follow these step-by-step instructions:

Set up a Google Cloud Platform (GCP) account:

  1. Go to https://console.cloud.google.com/ and sign in with your Google account.
  2. Create a new project by clicking the “Select a project” dropdown menu in the top-right corner, then click “New Project.”
  3. Enter a project name, select a billing account, and click “Create.”

Enable BigQuery API:

  1. From the GCP console, click the hamburger menu in the top-left corner and select “APIs & Services” > “Library.”
  2. Search for “BigQuery API” and click on the result.
  3. Click “Enable” to activate the BigQuery API for your project.

Set up a BigQuery dataset:

  1. In the GCP console, navigate to “BigQuery” from the left-hand menu.
  2. Click on your project name in the left sidebar, then click “Create Dataset.”
  3. Enter a dataset ID (e.g., “ga4_data”), select a data location, and configure other settings as needed. Click “Create dataset.”
Create Dataset BigQuery

Set up Google Analytics 4 (GA4) property:

  1. Go to https://analytics.google.com/ and sign in with your Google account.
  2. If you don’t have a GA4 property yet, create one by following the on-screen instructions.
  3. Once your GA4 property is created, navigate to the property’s “Admin” panel by clicking the gear icon in the bottom-left corner.
GA4 Account setup

Link GA4 to BigQuery:

  1. In the GA4 “Admin” panel, click “Data Streams” under the “Data” column.
  2. Select the data stream you want to link (e.g., web or app) and click the “Link to BigQuery” button.
  3. Choose the GCP project and dataset you created earlier, then click “Next.”
  4. Choose “Daily” or “Streaming” export frequency and click “Next.”
  5. Review the linking settings and click “Submit” to start the integration.
Link-BigQuery-in-GA4

Check the data in BigQuery:

After linking GA4 to BigQuery, it may take a few hours for the data to appear in BigQuery. Once the data starts flowing, you can query it in the BigQuery console

  • Go to the GCP console and navigate to “BigQuery” from the left-hand menu.
  • Locate your dataset (e.g., “ga4_data”) in the left sidebar and click on it to view the tables.
  • Click on a table to preview the data or click “Query Table” to write and execute SQL queries.
Check Data in BigQuery

Why you should set-up GA4 and BigQuery now?

Remember that GA4 to BigQuery integration doesn’t provide historical data backfill. Once the integration is set up, only new data will be exported to BigQuery.
Now that you’ve set up GA4 to BigQuery integration, you can start analyzing your raw GA4 data, join it with other datasets, create visualizations with tools like Data Studio, and apply advanced analytics or machine learning models to your data.

We hope this has given you a good Introduction to GA4 and BigQuery and why you should set-up GA4 to BigQuery now?

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How to Avoid (Other) Channel Attribution Showing in GA4 Reports https://webdatageek.com/how-to-avoid-other-channel-attribution-showing-in-ga4-reports/ https://webdatageek.com/how-to-avoid-other-channel-attribution-showing-in-ga4-reports/#respond Fri, 24 Mar 2023 21:31:02 +0000 https://webdatageek.com/?p=1707 Reduce the Impact of (Other) in GA4 Reporting by Removing Unwanted Query Parameters GA4, unlike Universal Analytics, does not have a built-in feature to exclude specific URL query parameters from reports. Failing to exclude query parameters that do not change the contents of a web page will result in multiple entries for the same page, […]

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Reduce the Impact of (Other) in GA4 Reporting by Removing Unwanted Query Parameters

GA4, unlike Universal Analytics, does not have a built-in feature to exclude specific URL query parameters from reports. Failing to exclude query parameters that do not change the contents of a web page will result in multiple entries for the same page, causing high cardinality dimensions in your GA4 reports. This, in turn, leads to the (Other) category appearing in your reports. Follow these steps to remove URL query parameters in GA4 and optimize your report for the keyword “Other showing in GA4 report”:

Step 1 – Identify Unwanted Query Parameters

Ask your web developer for a list of query parameters used on your website that do not change the contents of a web page. These parameters should be excluded from your GA4 reports.

Step 2 – Determine Additional Parameters to Exclude

Decide on any other query parameters you would like to exclude from your GA4 reports. Ensure that important parameters like search query parameters, gclid, gclsrc, dclid, gclsrc, srsltid, and utm parameters are not excluded.

Step 3 – Determine Parameters to Keep in Reports

Identify the query parameters you want to keep in your GA4 reports. Maintaining these parameters ensures relevant data is captured in your reports.

Step 4 – Import Query Parameter Stripping Utility Template in GTM

Log in to your Google Tag Manager account and import a variable template named “Query Parameter Stripping Utility.”

Step 5 – Create a User-Defined Variable in GTM

Create a new user-defined variable in GTM based on the imported template with the following configurations:

  • Enter the query parameters you don’t want to exclude under ‘Exclude All Parameters.’
  • Type the variable {{Page URL}} in the text box under ‘Field which needs parameters removed’ and click on the ‘Save’ button.

Step 6 – Edit the GA4 Configuration Tag

Edit the GA4 configuration tag by adding the following new row under ‘Fields to set’:

  • ‘Field Name’: page_location
  • ‘Value’: {{Exclude query parameters}}

Step 7 – Save, Preview, and Publish Your GTM Container

Save the tag, preview it to ensure it is working correctly, and then publish your GTM container.

By following these steps, you will reduce the impact of (Other) channel attribution in your GA4 reports and optimize your blog post for the keyword “Other showing in GA4 report.” Remember to monitor your reports regularly to identify any new query parameters that may need to be excluded.

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Items dimensions & metrics (GA4 Ecommerce) https://webdatageek.com/items-dimensions-metrics-ga4-ecommerce/ https://webdatageek.com/items-dimensions-metrics-ga4-ecommerce/#respond Fri, 10 Mar 2023 21:14:22 +0000 http://el.commonsupport.com/newwp/naxly/?p=90 Google Analytics 4 provides a wealth of data that can be accessed directly from tables without needing any complex calculations. However, there may be certain dimensions and metrics that require some advanced SQL skills to access and calculate. I have learned some valuable insights on the best practices for calculating these dimensions and metrics that […]

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Google Analytics 4 provides a wealth of data that can be accessed directly from tables without needing any complex calculations. However, there may be certain dimensions and metrics that require some advanced SQL skills to access and calculate. I have learned some valuable insights on the best practices for calculating these dimensions and metrics that I wanted to share.

I will provide a combined query for default dimensions and metrics, as well as an example query for each non-default dimension or metric. If you only need to access one default dimension or metric, you can refer to the example query and simply copy the corresponding part. However, make sure to include any additional conditions required to accurately calculate the results, such as with, from, where, group by, having, and order by. It’s important to note that the query I am providing is ungrouped, which means that each row corresponds to an event and may contain duplicate rows.

Here are the default dimensions and metrics. For non-default dimensions and metrics, please refer to the example query.

 

Dimension Description
items.item_id The ID of the product or item being purchased
items.item_name The name of the product or item being purchased
items.item_brand The brand of the product or item being purchased
items.item_variant The variant of the product or item being purchased
items.item_category The category of the product or item being purchased
items.item_category2 The second category of the product or item
items.item_category3 The third category of the product or item
items.item_category4 The fourth category of the product or item
items.item_category5 The fifth category of the product or item
items.coupon The coupon code used for the purchase
items.affiliation The affiliation code for the purchase
items.location_id The ID of the location where the purchase occurred
items.item_list_id The ID of the list that the item was a part of
items.item_list_name The name of the list that the item was a part of
items.item_list_index The position of the item in the list
items.promotion_id The ID of the promotion that was applied
items.promotion_name The name of the promotion that was applied
items.creative_name The name of the creative that was displayed
items.creative_slot The position of the creative that was displayed

 

Metric Description
items.price_in_usd The price of the item in US dollars
items.price The price of the item
items.quantity The quantity of the item purchased
items.item_revenue_in_usd The revenue generated by the item in US dollars
items.item_revenue The revenue generated by the item
items.item_refund_in_usd The refund amount in US dollars for the item
items.item_refund The refund amount for the item

 

Example SQL Code

This can be used to select dimensions and metrics related to items from a Google Analytics 4 export in BigQuery

 

SELECT
-- Dimensions:
-- The ID of the item
items.item_id,
-- The name of the item
items.item_name,
-- The brand of the item
items.item_brand,
-- The variant of the item
items.item_variant,
-- The category of the item
items.item_category,
-- The sub-category of the item
items.item_category2,
-- The sub-sub-category of the item
items.item_category3,
-- The sub-sub-sub-category of the item
items.item_category4,
-- The sub-sub-sub-sub-category of the item
items.item_category5,
-- Metrics:
-- The price of the item, in USD
items.price_in_usd,
-- The price of the item in local currency
items.price,
-- The quantity of the item
items.quantity,
-- The revenue of this item, calculated as price_in_usd * quantity, in USD
-- (populated for purchase events only)
items.item_revenue_in_usd,
-- The revenue of this item, calculated as price * quantity, in local currency
-- (populated for purchase events only)
items.item_revenue,
-- The refund value of this item, calculated as price_in_usd * quantity, in USD
-- (populated for refund events only)
items.item_refund_in_usd,
-- The refund value of this item, calculated as price * quantity, in local currency
-- (populated for refund events only)
items.item_refund,
-- Additional dimensions:
-- The coupon code applied to this item
items.coupon,
-- A product affiliation to designate a supplying company or brick and mortar store location
items.affiliation,
-- The location associated with the item
items.location_id,
-- The ID of the list in which the item was presented to the user
items.item_list_id,
-- The name of the list in which the item was presented to the user
items.item_list_name,
-- The position of the item in a list
items.item_list_index,
-- The ID of a product promotion
items.promotion_id,
-- The name of a product promotion
items.promotion_name,
-- The name of a creative used in a promotional spot
items.creative_name,
-- The name of a creative slot
items.creative_slot
FROM
-- Change this to your Google Analytics 4 export location in BigQuery
bigquery-public-data.ga4_obfuscated_sample_ecommerce.events_20201130,
-- Unnest the 'items' column to make its fields available as separate columns
UNNEST(items) AS items

 

Output

This query selects various dimensions and metrics related to e-commerce events, such as item IDs, names, brands, categories, and revenue information. The data is obtained from a Google Analytics 4 export location in BigQuery.

This query would be useful when analysing e-commerce activity and performance, such as identifying popular items, revenue trends, and promotions that are effective in driving sales. It can also be used to gain insights into customer behaviour and preferences by examining the characteristics of items purchased, such as categories and brands. The data can be further analysed and visualised using various tools and techniques to help inform business decisions and improve e-commerce strategies.

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