Google has declared updates coming soon to Google Analytics 4 (GA4) properties.
As of mid-October 2023, specific attribution models will be removed from the platform.
At the same time, Google has introduced a new “calculated metrics” feature that allows you to create custom metrics tailored to your business needs.
According to the statement from Google, the First Click, Linear, Time decay, and Position-based attribution models will be turned off all GA4 properties next month.
Understanding Google Analytics Changes
Google Analytics, the go-to tool for web analytics, has undergone some notable updates. These changes aim to enhance user experience and offer a more comprehensive insight into website performance. In this article, we’ll explore the removal of four models, the addition of Custom Metrics, and demystifying the term “GTM.”
The Importance of Google Analytics
Before delving into the updates, it’s crucial to understand why Google Analytics is essential for businesses.
1. Tracking User Behavior: Google Analytics allows businesses to track user behavior on their websites, providing valuable data about visitor demographics, interests, and more.
2. Monitoring Website Performance: It enables the monitoring of website performance, including page load times, bounce rates, and user interactions.
3. Making Data-Driven Decisions: With the insights obtained from Google Analytics, businesses can make informed, data-driven decisions to optimize their websites and marketing efforts.
Farewell to Four Models
Google Analytics has said goodbye to four attribution models:
1. Model 1: Last Non-Direct Click: This model attributed the conversion to the last channel that wasn’t a direct visit.
2. Model 2: Last Google Ads Click: It credited the last click on a Google Ads ad before a conversion.
3. Model 3: Last Ad Click: This model attributed the conversion to the last ad click, regardless of the channel.
4. Model 4: Linear Attribution: Linear Attribution distributed conversion credit equally across all touchpoints.
[GA4] Calculated metrics
Create your own metrics using existing and custom metrics to define a formula for a new GA4 metric
Calculated metrics are metrics that combine one or more existing or custom metrics within a mathematical formula to produce a new and potentially more beneficial metric. For example, an Item margin metric could be created using the Item price standard metric and the Item COGS custom metric.
With calculated metrics, you can adjust any metric to fit your business needs or logic. You can weight or discount existing metrics and create new metrics by combining standard or custom metrics.
These new metrics are typically more applicable than the original metrics from which they’re created, as they contain the additional business logic that enables direct decision making & action in Google Analytics.
You can access calculated metrics in reports, explorations, and the Google Analytics Data API.
Prerequisites
You must be an Administrator or Editor to create a calculated metric.
Limits & caveats
You can create up to 5 calculated metrics per standard property and 50 calculated metrics per 360 property.
You can’t reference a calculated metric in the formula for another calculated metric. You can only use predefined metrics and custom metrics in the formula for a calculated metric.
Create a calculated metric
- Click Custom definitions.
- In the Calculated metrics tab, click Create calculated metric.
- Complete the following fields to define the calculated metric. See examples to get started.
Field Description Name Enter a name for the calculated metric. The name you choose appears in the metric selectors for reports. The name must be unique across all other calculated and custom metrics. You can change the name later. API name Google populates the API name based on the value in the Name field. You can change the populated name during creation, but you can’t change the name once you create the calculated metric.
The API name must be unique across all other calculated and custom metrics. However, if you archive the calculated metric, you can use the API name for a different calculated metric.
The API name can contain alphanumeric characters and underscore only. Special characters, symbols, and spaces are not allowed.
Description Explain the calculated metric to other users of the Google Analytics property, who may not be aware of how the calculated metric is computed. The description is available in reports when hovering over the metric. Formula Start typing to see a list of predefined metrics you can use to define the formula. You can also enter numbers and decimals. Metric names must be surrounded by single curly brackets {}. Formulas are limited to 1024 characters.
You can enter operators between metrics and numbers to define the formula. Accepted operators include:
– (minus or negative)
+ (addition)
* (multiplication)
/ (division)
() (parenthesis)Unit of measurement Select your desired unit of measurement, which determines how you calculated metric appears in reports. - Click Save to create the calculated metric.
Edit a calculated metric
- Click Custom definitions.
- In the Calculated metrics tab, click Create calculated metric.
- To the right of the calculated metric in the table, click More [
] > Edit.
- Edit the calculated metric. You can change the name, description, formula, and unit of measurement.
- Click Save.
Copy a calculated metric
- Click Custom definitions.
- In the Calculated metrics tab, click Create calculated metric.
- To the right of the calculated metric in the table, click More [
- Edit the copied version of the calculated metric.
- Click Save.
Use cases
Example 1: Shipping revenue as % of purchase revenue
- Name: Shipping percentage
- API name: (automatically populated)
- Formula: {Shipping amount} / {Purchase revenue}
- Unit of measurement: Currency
Example 2: Item discount percentage
- Name: Item discount percentage
- API name: (automatically populated)
- Formula: {Item discount amount} / {Item revenue}
- Unit of measurement: Standard
Embracing Custom Metrics
Now, let’s embrace the new addition: Custom Metrics.
1. What are Custom Metrics?: Custom Metrics are user-defined metrics that allow you to track and measure specific user interactions. They provide a more personalized view of your data.
2. How to Create and Use Custom Metrics: Users can create Custom Metrics in Google Analytics, defining the parameters they want to track, and then incorporate them into reports for a more tailored analysis.
3. Practical Applications of Custom Metrics: Custom Metrics can be used to measure anything from video engagement to form submissions, offering businesses a deeper understanding of their users’ interactions.
Demystifying GTM
GTM, or Google Tag Manager, is a vital part of the Google Analytics ecosystem.
1. What is GTM (Google Tag Manager)?: GTM is a tag management system that allows users to manage and deploy tracking codes and marketing tags on their websites without needing to edit the site’s code.
2. How does GTM Work with Google Analytics?: GTM can be integrated with Google Analytics to streamline the process of adding tracking codes and tags, making data collection more efficient.
3. Benefits of Using GTM: GTM simplifies tracking and ensures that data is collected accurately, reducing the risk of errors in the implementation of tracking codes.
Making the Most of Google Analytics Changes
To make the most of these changes, businesses should consider:
1. Leveraging Custom Metrics for Better Insights: By creating and implementing Custom Metrics, businesses can gain a deeper understanding of user behavior and engagement.
2. Combining GTM and Google Analytics Effectively: The integration of GTM with Google Analytics can streamline the tracking process, saving time and reducing the risk of errors.
3. Preparing for the Future with Google Analytics: Staying updated with Google Analytics changes and being proactive in implementing them will give businesses an edge in the digital landscape.
Conclusion
Google Analytics’ updates have removed four attribution models, introduced Custom Metrics, and emphasized the importance of GTM. These changes empower businesses to gather more precise data, make informed decisions, and optimize their digital strategies effectively.