Understanding your website traffic and measuring marketing effectiveness are essential to digital success. The experts at Tagmate have curated this guide to help fellow marketers and website owners navigate their way through the Google Analytics 4 platform. Since it is currently under development and optimization, users will need to continue learning as it evolves.

In this extensive guide, we’ll explore three critical capabilities in Google Analytics 4 (GA4) to master those tasks:

1. Traffic source classification
2. Channel groupings
3. Attribution modeling

Gaining expertise across these foundational elements will provide the insights needed to analyze and optimize your acquisition and conversion strategies. 

Let’s dive in:

Traffic Source Classification in GA4

Traffic source classification refers to how Google Analytics automatically categorizes and labels each session hitting your website or mobile app. This provides the foundation for analyzing where your traffic originates.

Traffic Source Dimensions

Traffic classification is powered by a specialized set of dimensions in GA4:

• Source – The specific publisher, website, or app the traffic originated from.
• Medium – The high-level category describing the general type of traffic
• Campaign – The individual initiative, marketing effort, or advertising campaign driving the traffic.
• Keyword – The specific keyword searched to reach the site via paid search ads.
• Ad Content – The text and content of the ad clicked from search or display.
• Source Platform – The advertising or traffic management platform used to purchase media or that provided organic visibility.

These dimensions handle automatic classification across all sessions into logical traffic source attributes. Many reports in GA4 allow selecting these dimensions for segmentation, filtering, and analysis.

For example, the Acquisition overview report uses Default Channel Grouping as its primary dimensions:

This provides a high-level breakdown of volumes and conversions by source and traffic type.

Enhancements in GA4

While traffic sources exist in Universal Analytics, GA4 introduced several meaningful improvements:

• More accurate source – Directly reflects the specific publisher rather than intermediary sites or redirects that obscure the true origin.
• Standardized medium – Consistent categorical definitions applied to each medium across all sources and platforms.
• Source platform added – New dimension showing the ad buying platform or organic source platform like Search Ads 360.
• Channel grouping expansion – Eight new default channels added including video, shopping, audio, and push notifications.
• YouTube improvements – Traffic properly classified as organic or paid YouTube video rather than lumped under social.
• Classification accuracy – Overall improvements to accuracy driven by Google’s ecosystem data and machine learning advancements.
• Standardized medium – Consistent categorical definitions applied to each medium across all sources and platforms.
• Source platform added – New dimension showing the ad buying platform or organic source platform like Search Ads 360.
• Channel grouping expansion – Eight new default channels added including video, shopping, audio, and push notifications.
• YouTube improvements – Traffic properly classified as organic or paid YouTube video rather than lumped under social.
• Classification accuracy – Overall improvements to accuracy driven by Google’s ecosystem data and machine learning advancements.

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These enhancements yield trusted, granular, consistent insight into where website and app sessions originate. Reliable traffic classification establishes the foundation for further acquisition analysis using channel groupings.

Cross-Channel vs. Integration Dimensions

An important distinction exists between cross-channel and integration-specific traffic source dimensions:

Cross-channel dimensions provide a unified view spanning all traffic types and sources. Examples are Source, Medium, Campaign, and Keyword. These offer perspective across your full acquisition landscape.

Integration-specific dimensions enable analyzing one specific platform in detail like Search Ads 360 traffic. Dimension names prefix the associated platform like SA360 Source or SA360 Medium. Use these to dig into a particular channel.

Choose cross-channel or integration dimensions based on the scope of analysis needed. Cross-channel is best for a generalized view while integration offers focused inspection of one channel.

Now that we’ve covered the fundamentals of traffic classification, let’s explore how these sources are summarized into channels using channel groupings.

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Channel Groupings in Google Analytics 4

Channel groupings categorize traffic sources into meaningful groups and consolidate them under common channels. This simplifies gaining insights from the wide array of acquisition sources.

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Why Channel Groups Matter

Without channel groupings, viewing raw traffic sources can be an overwhelming list:

Potentially thousands of rows spanning campaigns, ad groups, keywords, publishers, etc. Difficult to extract any meaning.

Channel grouping rolls up sources into logical, high-level categories aligned to marketing activities:

Much more digestible view organized by channel. Focuses on how channels perform rather than individual sources.

Common channel examples:

• Paid search
• Organic search
• Referral traffic
• Social media
• Affiliates
• Email
• Display ads
  

Grouping to channels provides aggregation useful for reporting and identifying opportunities.

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Default Channel Groups in GA4

Google Analytics 4 comes pre configured with default channel groups encompassing all the major marketing channels:

• Organic search
• Paid search
• Organic social
• Paid social
• Referral
• Email
• Affiliate
• Display
• Direct
  

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Recent updates expanded the classification to include:

• Organic video
• Paid video
• Shopping
• Audio
• Push notifications
  

Improvements leverage Google's ecosystem data for enhanced default grouping accuracy - particularly for YouTube, search, social, and display channels.

The predefined groups cover most use cases without modification, providing trusted “out of the box” channel insights.

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Custom Channel Definitions 

You can create custom channels to categorize traffic sources that don't fit into the default channels. For example, you could make custom channels for specific social media campaigns.

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Custom channels can be grouped together into custom channel groups. This allows you to view aggregated performance data across related custom channels.

To create a custom channel, go to Admin > Data Streams > Channel definitions. Click +Add new channel and define the channel name, description and condition.

To create a channel group, go to Admin > Data Streams > Channel groups. Click +Add new group, give it a name and description, then add your custom channels to it.

Custom channels and groups can then be used as dimensions in Acquisition reports to view performance data.

The key differences compared to Universal Analytics:

• In GA4, custom channels are defined per data stream rather than per property.
• Channel definitions are based on conditions rather than regex matching of traffic source.
• Groups are limited to 25 channels instead of 200.

Attribution Modeling in Google Analytics 4

Attribution refers to the process of assigning credit for conversions and revenue to touch points along the user journey. Attribution aims to quantify the influence of marketing channels in driving business outcomes.

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The Importance of Attribution

In today’s complex digital landscape, most converts involve multiple touch points across different channels. Some key questions attribution helps answer:

• Which channels and campaigns contribute most to revenue?
• How much conversion credit should we assign to email vs. social vs. SEO?
• What is the true ROI of our display ad investment?

Without attribution, you’re limited to last-click analysis which assigns 100% credit to the final touchpoint before conversion. This underrepresents the influence of avenues that assist further up the funnel.

Multi-touch attribution provides a complete, accurate picture of ROI across channels. This enables optimizing your marketing budget across the initiatives delivering highest value.

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Attribution Models in GA4

Google Analytics 4 offers several models to allocate conversion credit to different touchpoints along the user journey:

• Last Non-Direct Click - Attributes 100% credit to the last channel the user clicked through before converting, excluding direct traffic. This is the same as Last Click attribution.
• Data-Driven - Uses advanced machine learning to distribute credit optimally between touchpoints based on their actual calculated impact. This is the recommended model.

The following attribution models are being deprecated as of November 2022:

• First Click - Attributes 100% credit to the first channel the user clicked through or engaged with.
• Linear - Distributes credit evenly between all touchpoints in the conversion path.
• Time Decay - Assigns more credit to recent touchpoints, with credit decaying over time.
• Position-Based - Similar to time decay, but distribution is based on position in path rather than time.

With the deprecation of these models, Last Non-Direct Click and Data-Driven attribution will remain. The data-driven model is recommended as it uses Google's latest AI to determine the true influence of each interaction. But Last Non-Direct Click still provides an additional rules-based perspective.

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How Data-Driven Attribution Works

Google Analytics data-driven attribution relies on two key components:

1. Analyzing path data to develop a conversion rate model - Assesses the incremental lift in conversion probability based on exposure to each click event.
2. Attribute credit based on conversion impact - Allocates higher credit to touchpoints that maximally increase conversion rate predictions.
   

By incorporating sophisticated machine learning, the model uncovers touchpoints with greatest influence in driving conversions. This avoids overvaluing the first or last click which often don’t fully capture attribution.

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Attribution Reports in GA4

Attribution reporting exists within the new Advertising Center in GA4 covering all ad-focused data:

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- Conversion Path Report

Visualize the complete path-to-conversion and view attributed credit by channel. Filter and segment to analyze attribution across different dimensions.

- Model Comparison Report

Compare conversion or revenue credit allocation across different models. Identify the optimal model for accurate insights.

These reports provide a detailed yet digestible analysis of the customer journey and channel contributions.

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Enhanced Attribution in GA4

Significant enhancements were made to attribution capabilities compared to Universal Analytics:

• Data-driven attribution for all - Previously limited to 360 customers only but now available to everyone.
• Cross-device modeling - Accounts for user activity across devices to eliminate device-specific bias.
• Calibrated incrementality - Controls for external factors to isolate true impact of each touchpoint.
• Exportable credit - Share fractional attribution data out to integrated Google ads platforms.
• Cross-platform - Consistent methodology applied across web and mobile apps.

These improvements equip all GA4 properties with enterprise-grade attribution accessible to everyone.

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Property-Level Attribution Settings

New in GA4 is the ability to define a default attribution model and lookback window at the property level:

This is configured in Property Settings > Attribution Settings.

The property attribution model applies to:

• Conversion drill-down reports
• Custom channel reports
• GA4 explore

It does NOT apply to dedicated conversion path or model comparison reports - those still allow flexible model selection.

The lookback window defines the conversion window post-click evaluated in modeling. This lookback gets applied to all data property-wide.

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Attribution Best Practices

To maximize the value gained from attribution analysis in GA4, keep these tips in mind:

• Enable Google signals - Improves cross-device mapping for accurate modeling.
• Focus on integrations - Direct integrations like SA360 yield more precise touchpoint data to inform models.
• Analyze multi-touch paths - Many journeys involve influence across several channels.
• Test different models - Helps determine the optimal model for your unique business needs.
• Review regularly - Revisit channel contributions as new initiatives launch.
  

Applying attribution equips you to continuously optimize marketing spend toward high-value activities proven to drive conversions.

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Summing Up

In this extensive guide, we explored three foundational analytics capabilities in Google Analytics 4:

• Traffic classification - Automatic labeling of sources using dimensions like Source, Medium, Campaign, and Keyword. GA4 introduced meaningful improvements to classification accuracy.
• Channel groupings - Rollup of sources into marketer-friendly channels providing aggregated insights. Leverage GA4’s expanded default channel definitions.
• Attribution - Modeling the allocated conversion credit across touchpoints in the customer journey, with advanced data-driven modeling now available to all.
  

Mastering each equips you to better understand your acquisition sources, analyze performance by channel, and optimize spend towards truly impactful initiatives.

Key takeaways:

• Leverage GA4’s rich set of traffic source dimensions for filtering and segmentation.
• Review channel group reports to analyze performance by marketing channel.
• Implement attribution to uncover the true influence of marketing activities.
• Enable enhanced data collection to fuel accurate attribution modeling.
• Revisit channel and attribution reports regularly to optimize resource allocation.
  

These capabilities form the foundation for unlocking the full value of Google Analytics. Focus on each area to continually improve marketing performance.

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At the same time, it is scalable and more secure than dealing with analytics manually.

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