How is eCommerce Tracking in Google Analytics 4 Different from the Traditional Universal Analytics

What started as Urchin has now transformed into the next generation of Analytics, i.e., Google Analytics 4 (GA4).

Built with a strong focus on privacy, cross-device tracking, and machine learning, GA4 delivers a more holistic view of the customer journey.

Moving away from Universal Analytics’ session-based data model, GA4 adopts an event-based approach, offering a more detailed and nuanced understanding of user interactions.

The transition empowers businesses to gain deeper insights into customer behavior without relying heavily on cookies, aligning with the demands of a privacy-conscious digital landscape.

For eCommerce businesses, this evolution marks a new era in tracking capabilities. While Universal Analytics offered robust tools to track eCommerce metrics, GA4 took it further by offering enhanced data collection and reporting features that cater specifically to the modern eCommerce environment.

In this blog post, you will learn how Google Analytics 4 equips eCommerce businesses with advanced insights into user behavior across all touchpoints.

You will also learn how the UA to GA4 transition has enabled marketers to track eCommerce performance with greater accuracy and adapt to the demands of a data-driven, privacy-focused world.

Let’s get started!

Evolution of eCommerce Tracking: From Universal Analytics to Google Analytics 4

1. Before 2020: Universal Analytics—The Go-To Platform for eCommerce Tracking

a. Universal Analytics (UA) establishes itself as the industry standard, helping eCommerce businesses track essential metrics like transactions, revenue, product performance, and user behavior.
b. Many organizations rely on UA for informed decision-making and optimizing customer experiences.

2. 2020: The Introduction of Enhanced eCommerce in UA

a. Google introduces enhanced eCommerce tracking in UA, enabling more detailed insights on product impressions, clicks, and user interactions.
b. This update helps businesses gain a deeper understanding of their customers, leading to wider adoption of enhanced eCommerce tracking.

3. 2020: The Launch of Google Analytics 4

a. Google unveils GA4, featuring an event-based tracking model and cross-platform measurement.
b. Initially, GA4’s eCommerce tracking tools were limited compared to the extensive, enhanced eCommerce options in UA.

4. 2021—2022: Expansion of GA4 eCommerce Features

a. Google gradually upgrades GA4 to offer enhanced eCommerce tracking, closing the gap with UA’s features.
b. Many businesses start testing GA4 for eCommerce, often running it alongside UA to compare insights.

5. July 2023: UA Shuts Down and GA4 is Effective

a. UA stops processing new data, marking a complete shift to GA4 for eCommerce tracking.
b. Businesses fully migrate to GA4, focusing on optimizing setups, leveraging new tools, and ensuring accurate data tracking.

6. Post July 2023: GA4 Becomes the Standard for eCommerce Tracking

a. GA4 becomes the primary eCommerce analytics platform. Businesses adapt to its event-driven data model and explore advanced features such as purchase events, refunds, and machine learning insights.
b. Businesses also shift their focus to adopting best practices for effective GA4 eCommerce tracking.

UA vs GA4 eCommerce Tracking—A Detailed Comparison

eCommerce Conversion Tracking—What Changed in Google Analytics 4

In Universal Analytics (UA), eCommerce conversions were tracked through ‘transactions’, which recorded details like product ID, quantity, and revenue. Google Analytics 4 (GA4), however, uses ‘purchase’ events as the main metric for eCommerce tracking.

Universal Analytics ‘Transactions’

Example: A customer buys three different books. In UA, this would have been recorded as a single transaction and captured details of the books and the total purchase amount.

Google Analytics 4 ‘Purchase’

Example: After setting up GA4 measurement IDs on the payment pages, this same transaction would be logged as a ‘purchase’ event, recording each book, their individual prices, and the total purchase amount.

This shift also changed the method of tracking and analyzing eCommerce conversions. Here’s how:

a. While GA4 allows for more detailed tracking of purchase events when configured correctly, it lacks some of the automatic tracking features that UA offered for transactions.
b. In GA4, tracking for checkout and payment pages must be specifically set up, as it is not automatically enabled.
c. While UA automatically captures certain transaction data, setting up GA4 to track purchases can vary in complexity depending on the site’s platform and often requires support from the platform’s technical team.

For optimal tracking of purchase conversions in GA4, ensure that you:

a. Reach out to your website platform’s support team and add the correct GA4 measurement ID to the checkout and payment pages.
b. Separate GA4 measurement IDs if you’re tracking conversions from multiple sources, such as Google Ads and organic site traffic.

Factors to Consider for eCommerce Measurement: Transitioning from Universal Analytics to GA4

Data Structure and Collection

Both Universal Analytics and Google Analytics 4 offer advanced eCommerce features, however, the data layer structure and event names have been updated in GA4. To collect accurate data, it’s essential to set up a well-structured data layer that captures all the necessary eCommerce details.

Reporting and Analysis

With GA4’s updated organization of metrics and dimensions, you’ll need to adapt your reporting methods. Emphasize key metrics such as user engagement, conversion rates, and customer lifetime value to gain valuable insights.

eCommerce Metrics and KPIs

Metrics like revenue and transactions remain important in GA4, though they may be calculated differently.

For instance, Universal Analytics calculated total revenue as the sum of all revenue values across transactions. In contrast, GA4 calculates total revenue based on the sum of all value parameters across purchase events. GA4 also introduces ‘revenue per user’, which divides total revenue by the number of unique users.

Additionally, GA4 offers the ‘unique purchases’ metric, counting the number of unique transaction IDs rather than total purchase events. This metric is similar to UA’s transaction count and prevents double-counting when users make multiple purchases in a single session.

You can further analyze product performance in GA4 using metrics like purchase rate, average order value, and item quantity. For insights into customer behavior, examine purchase funnels, user journeys, and engagement metrics.

Implementation and Setup

Leverage debugging and testing tools to verify your setup, and define event parameters for eCommerce actions clearly to streamline analysis. Google Tag Manager can help you manage tags and triggers efficiently, thereby simplifying the setup.

Recent Google Analytics 4 Enhancements & Updates eCommerce Businesses Should Know

Data-Driven Attribution (DDA) Updates

a. Enhanced Machine Learning: Google’s updated algorithms now offer more refined attribution for complex eCommerce customer journeys, delivering more accurate insights across multiple touchpoints.

b. Expanded Data Processing: DDA can now process a broader range of data points, allowing for more granular insights that are especially valuable for stores with varied product lines.

c. Accelerated Learning: GA4’s model now requires less historical data to generate actionable insights, making it particularly useful for new businesses or those experiencing seasonal sales changes.

d. Cross-Device Tracking: Enhanced ability to track and attribute conversions across devices, which is essential in eCommerce, where mobile browsing often leads to desktop purchases.

Data-driven attribution analyzes a customer’s path to purchase and considers every interaction along the way. For instance, DDA might show that while your email marketing isn’t the last touchpoint before a purchase, it plays a key role in nudging customers toward conversion, which a last-click model might overlook.

Here’s how Data-Driven Attribution benefits eCommerce:

a. Captures Diverse Interactions: Tracks a wide range of actions such as product views, add-to-cart events, email clicks, and ad impressions.

b. Evaluates Conversion Probability: Assesses the likelihood of each touchpoint leading to a sale.

c. Credits Touchpoints Based on Influence: Allocates conversion credit across touchpoints based on their contribution to the purchase decision.

d. Adapts to New Data: Continuously refines itself as it learns from ongoing customer behavior and evolving marketing strategies.

Paid and Organic Last Click Attribution Model

a. This model assigns credit to the final channel (paid or organic) that the customer interacted with before converting.
b. This model is easy to understand and implement and provides straightforward insights into which channels are most effective at driving final sales.
c. However, this model can undervalue top-of-the-funnel activities, such as brand awareness campaigns, that contribute to the decision-making process but don’t directly close the sale.

Google Paid Channels Last Click Attribution

a. This model attributes conversions solely to the last Google Ads click, disregarding other channels.
b. It offers clear visibility into which Google Ads campaigns directly drive sales, helping you optimize your ad spend.
c. However, this model overlooks the influence of other marketing channels, potentially underestimating the value of organic efforts and paid channels outside of Google Ads.

Key Takeaway

It’s remarkable to see how the digital marketing landscape has evolved over the years. Embracing Google Analytics 4 for your eCommerce business and staying updated with the latest transformations in the analytics landscape is essential to understanding your customers and the market.