GA4 Predictive Audiences – The differences to Universal Analytics Similar Audiences
In the realm of digital advertising, precision in targeting the right audience can make a significant difference in campaign success. Google Analytics currently provides two distinct audience targeting features: Universal Analytics Similar audiences and GA4 predictive audiences. However, with Universal Analytics soon going obsolete, we will be left with predictive audiences from GA4. To make the most of the new version of Google Analytics it is important to know the difference between the two. While both methods aim to optimize ad targeting, they differ in their underlying technologies and approaches. In this article, we will delve into the dissimilarities between Universal Analytics Similar audiences and GA4 predictive audiences, revealing their unique characteristics and functionalities.
Universal Analytics Similar Audiences
Expanding Horizons through Similarities, Universal Analytics Similar audiences have emerged as a valuable asset for advertisers seeking to broaden their reach. These audiences are crafted by identifying and targeting individuals who share similar characteristics and behaviors with existing custom audiences or remarketing lists. An example would be setting up an audience that look like your current customers. Google would look at your current customers and find people with similar interests, demographics and patterns.
The essence of Universal Analytics Similar audiences lies in analyzing data and patterns through sophisticated algorithms. By recognizing commonalities among users, this method enables marketers to tap into new potential customers who exhibit traits akin to their existing audience.
Creating Universal Analytics Similar audiences involves carefully selecting a suitable source audience and fine-tuning targeting settings to achieve optimal results.
GA4 Predictive Audiences
Paving the Path with Intelligent Insights With the advent of Google Analytics 4 (GA4), advertisers gained access to a more advanced audience targeting capability known as predictive audiences. GA4’s predictive audiences adopt a forward-thinking approach by leveraging machine learning algorithms to forecast future user behavior and identify potential converters.
Predictive audiences in GA4 utilize machine learning models to analyze user behavior patterns. These insights are then harnessed to anticipate future user actions and identify potential customers who are likely to convert.
To utilize predictive audiences in GA4, advertisers need to set up the feature and harness the power of machine learning models to optimize marketing efforts, enhance campaign performance, and allocate resources effectively. On 1. July, 2023, Google will stop processing data in Universal Analytics and will be replaced by Google Analytics 4.
You can utilize predictive audiences to optimize targeting in advertising channels like Google Ads. To do this you need to:
- Ensure your Google Analytics 4 (GA4) property is linked to your Google Ads account.
- In Google Ads, navigate to the “Audiences” section.
- Click on “Create new audience” and select “Website visitors” or “App users.”
- Choose the “Google Analytics” option and select your GA4 property.
- Select the “Predictive audience” option.
- Define the audience characteristics, such as user behavior, demographics, or conversion goals.
- Set the audience size and duration.
- Save and apply the audience to your Google Ads campaigns.
- Monitor the performance of your campaigns targeting the predictive audience.
- Make adjustments and optimizations based on the audience insights and conversion data.
Methodology: Universal Analytics Similar audiences rely on analyzing existing audience data to find users with similar characteristics and behaviors. Conversely, GA4 predictive audiences leverage machine learning algorithms to forecast future user behavior, identifying potential converters based on their anticipated actions.
Focus: Universal Analytics Similar audiences are primarily focused on expanding the reach of existing audiences by targeting users with similar traits. GA4 predictive audiences adopt a proactive approach, enabling advertisers to identify potential future customers based on behavioral patterns and anticipated actions.
Technological Advancement: Universal Analytics utilizes algorithms to analyze historical data, while GA4 harnesses the power of machine learning models to predict future user behavior. This advanced technology ensures more accurate and dynamic targeting capabilities.
How to set up predictive audiences:
- Ensure you have GA4 implemented: Make sure you have Google Analytics 4 tracking code properly installed on your website or app.
- Access the Google Analytics interface: Log in to your Google Analytics account and navigate to the GA4 property for which you want to create the predictive audience.
- Go to the settings section: Click on “Audiences” under the correct property.
- Click the “New audience” button to start creating a new audience.
- Choose the predictive audience type: Select the “Predictive audience” option from the available audience types.
- Configure audience settings: Provide a name for your audience and set the desired audience parameters, such as the prediction duration, confidence threshold, and audience size.
- Define audience characteristics: Specify the desired characteristics, behaviors, or actions that you want the predictive audience to target. This can include specific events, user attributes, or conversion goals.
- Review and create the audience: Double-check the audience settings and characteristics you have configured. Once satisfied, click the “Save” button to create the predictive audience.
- Apply the audience to your marketing campaigns: After creating the predictive audience, you can apply it to your marketing campaigns within Google Analytics or use it in conjunction with other advertising platforms, such as Google Ads.
In the ever-evolving landscape of digital advertising, precise audience targeting remains a fundamental aspect of campaign success. Universal Analytics Similar audiences and GA4 predictive audiences offer powerful tools to optimize targeting efforts. While Universal Analytics looks for similarities among existing audiences to expand reach, GA4 takes a proactive approach, utilizing machine learning algorithms to anticipate future user behavior. Understanding the distinctions between these targeting methods enables advertisers to leverage the right approach to effectively reach and engage their desired audience, ultimately driving campaign success. Finally, it is important to transfer to Google Analytics 4 before 1. July, 2023. The switch will be a progressive step for us as well and we are excited to learn more about this data driven world we live in.
GA4 Data Sampling explained
Sampling is an important part of data analysis and research. It is the process of selecting a subset of data from a larger population in order to make inferences about the population as a whole. Sampling is used in many different fields, including marketing, economics, and social sciences.
Google Analytics 4 (GA4) is the latest version of Google Analytics, and it includes a new sampling feature. This feature allows users to select a subset of data from their entire population in order to make more accurate and reliable inferences. In this post, we’ll discuss what GA4 sampling is, how it works, and why it’s important.
What is GA4 Sampling?
GA4 sampling is a feature of Google Analytics 4 that allows users to select a subset of data from their entire population in order to make more accurate and reliable inferences. This feature is especially useful for large datasets, as it allows users to quickly and easily analyze a subset of data without having to analyze the entire population.
How Does GA4 Sampling Work?
GA4 sampling works by selecting a subset of data from the entire population. This subset is then used to make inferences about the population as a whole. The sampling process is based on a number of factors, including the size of the population, the type of data being analyzed, and the desired accuracy of the results.
The sampling process begins by selecting a random sample of data from the population. This sample is then analyzed to determine the characteristics of the population as a whole. The results of this analysis are then used to create a model that can be used to make inferences about the population.
Why is GA4 Sampling Important?
GA4 sampling is important because it allows users to quickly and easily analyze a subset of data without having to analyze the entire population. This can be especially useful for large datasets, as it allows users to make more accurate and reliable inferences about the population as a whole.
In addition, GA4 sampling can also be used to identify trends and patterns in data. By analyzing a subset of data, users can identify trends and patterns that may not be visible when analyzing the entire population. This can be especially useful for marketing and research purposes.
GA4 sampling is an important feature of Google Analytics 4 that allows users to select a subset of data from their entire population in order to make more accurate and reliable inferences. This feature is especially useful for large datasets, as it allows users to quickly and easily analyze a subset of data without having to analyze the entire population. In addition, GA4 sampling can also be used to identify trends and patterns in data. By analyzing a subset of data, users can identify trends and patterns that may not be visible when analyzing the entire population.”
We are happy to help you to strategically advance your online marketing! You can easily book a non-binding consultation with us and ask us your questions.
Referral Exclusions in Google Analytics
Adding referral exclusions is very important if you want to prevent your webshop’s revenue from being linked to the wrong source.
Example: You have a Google Ads campaign running. A total of €300 was spent and the turnover is €3,000. In Analytics you can see that €1,000 is from Google Ads and €2,000 is from Referrals. When you open the referrals list, you will see all possible banks, such as 3dsecure.deutsche-bank.de and mastercardsecurecode.sparkassen-kreditkarten.de . This has to do with the fact that someone paid by credit card, Paypal or similar during the order and therefore left the web shop for a while, only to come back to the thank you page later. Google Analytics assumes that this visitor z. B. comes from the Deutsche Bank website, which is not correct.
How can I exclude referrals?
If you go to Administration -> Tracking Info -> List of Referral Exclusions in Google Analytics, you can add these exclusions one by one. However, this is a tedious task, because there are dozens of referral websites in the German-speaking area – internationally there are many hundreds.
As a marketing agency, we have to set this up for every webshop and it’s quite frustrating. Fortunately, there is a practical solution: go to the Realhe.ro website and save the “GA Import domain exclusions 2.0” button in your bookmarks bar. Then go to the Referral Exclusions in Google Analytics. You can then click the button on your bookmarks bar, copy and paste the list. Then all you have to do is click “Import” and you’re done!
List of referrals to exclude
These are the most common referral websites including German, Dutch, Belgian and English banks.
If you come across new foreign sources, you can add them to your list later. Feel free to send us an e-mail or call us and let us know about this referral. Then we add them to the list above. Thanks in advance!
Do you need support or would you like to hand over the online marketing of your company into professional hands? Then do not hesitate to contact us! At Nakoa Digital we are performance marketing and retail media experts. We are happy to help you to strategically advance your online marketing! You can easily book a non-binding consultation with us and ask us your questions.
Google Analytics 4 – What is changing?
Google has been offering its tracking tool Universal Analytics (formerly Google Analytics ) since 2012. This enables users to carry out a data traffic analysis of websites. In February 2022, Google came under criticism for a lack of data protection in Europe and both the Austrian and French data protection authorities announced that Google violated the EU’s General Data Protection Regulation. That’s why Google has now launched a new version: Google Analytics 4. Here, data protection and machine learning are the absolute focus. This is intended to sustainably promote improvements in marketing ROI.
What are the new features of Google Analytics 4?
Unlike before, the new version works both with and without cookies and no longer stores users’ IP addresses. Instead, the customer journey is tracked based on user IDs and Google signals, and long-term advertiser requests are also recognized. Since smartphone apps are being used more and more, Google is also taking this into account. Thanks to intelligent solutions, it does not matter whether the user changes devices during a purchase process and, for example, downloads a smartphone app on a desktop due to an advertisement on the Internet and then uses this to complete the purchase. Google Analytics is able to recognize this through the user IDs. If important data is lost due to data protection, Google Analytics 4 can fill the gaps in evaluations through data modeling.
Other new features that Google is integrating include new prediction metrics that predict future trends, for example. In addition, you get a better and cross-platform overview of the buying process of customers and users and Google offers an extended, wireless integration into its own advertising platforms. This optimizes campaign performance and increases marketing ROI. In addition, automatic notifications are sent when important trends are identified in the data, for example when there is higher product demand due to new customer requirements. All in all, Google Analytics 4 will replace the current Universal Analytics as the new standard application.
When will the change take place?
On July 01, 2023 it will be possible to process data in standard Universal Analytics Properties. From this point on, new data can only be edited in Google Analytics 4 Properties. However, access to old, previously processed data will still be possible at Universal Analytics at least until November 1st, 2023.
When should I act?
We recommend not waiting too long to switch. The new Google Analytics generation has been on the market since October 2020 under the previous name App + Web . The analyzes of the two programs are very different and are difficult to compare. It therefore makes sense to use both versions now. As a result, Google’s algorithms can already collect and learn valuable data. In addition, you can easily familiarize yourself with the new functions and thus ensure that all functions are still available and no data is lost.
Do you need support with the implementation of Google Analytics 4 or would you like to hand over your company’s online marketing to professionals? Then do not hesitate to contact us! At Nakoa Digital we are performance marketing and retail media experts. We are happy to help you to strategically advance your online marketing! You can easily book a non-binding consultation with us and ask us your questions.