Google Analytics 4 (GA4) represents a major step forward for the Google Analytics platform in a variety of ways.
Similarly, GA4 can show you the 10% of your audience which is most likely to purchase, as shown above.
In this case, you might want to set up ad suppression. Given that these users are the most likely to purchase anyway, you might want to divert your paid media dollars to ensure they’re spent targeting users who need that extra nudge to convince them to buy.
At the same time, you could also use Google’s “Similar Audiences” feature — think look-alike modeling — to encourage Google’s ad-buying platforms to target new users who are similar to the people who are most likely to buy.
As with the retargeting use case, all of this can be done with just a few clicks. And by combining these techniques, you should be able to quickly reduce churn, improve return on ad spend (ROAS), and efficiently expand your reach.
- GA4 unifies tracking for organizations across their websites, apps, and any other digital experiences.
- GA4 equips organizations to manage their analytics deployment in a more privacy-safe manner, thanks to a raft of new features that provide granular control over what data is collected and when.
- GA4 brings a variety of in-platform machine learning (ML) capabilities to marketers’ fingertips, meaning taking advantage of ML requires nothing more than properly setting up a GA4 account.
Predictive Metrics and Audiences
When implementing GA4 to start collecting data, Google’s ML algorithms begin to learn from your unique dataset to help project metrics like revenue and churn. For example, for retailers tracking e-commerce activity, the platform will begin to measure purchase probability and predicted revenue automatically. And regardless of your business model, churn probability will help you understand how likely it is that a user who has been to your site in the past week will fail to return to your site in the next week. Predictive metrics are helpful because they provide a view into the future, but where this gets really interesting is in using predictive metrics to create predictive audiences. For example, GA4 can show you the 20% of your audience that is most likely to churn — that is, not return to your website or app. You can take this segment of your audience, push it out to one of Google’s ad-buying platforms, and run a retargeting campaign intended to reduce churn. All of this can be done with just a few clicks, with no need to develop your own ML models.
Similarly, GA4 can show you the 10% of your audience which is most likely to purchase, as shown above.
In this case, you might want to set up ad suppression. Given that these users are the most likely to purchase anyway, you might want to divert your paid media dollars to ensure they’re spent targeting users who need that extra nudge to convince them to buy.
At the same time, you could also use Google’s “Similar Audiences” feature — think look-alike modeling — to encourage Google’s ad-buying platforms to target new users who are similar to the people who are most likely to buy.
As with the retargeting use case, all of this can be done with just a few clicks. And by combining these techniques, you should be able to quickly reduce churn, improve return on ad spend (ROAS), and efficiently expand your reach.
Analytics Intelligence
As GA4 learns from your unique dataset, it becomes better able to detect outliers — interesting data points to be aware of. For example, did traffic decrease beyond the expected range? Have conversion rates increased beyond the expected range? Why? With GA4’s anomaly detection feature, you’ll automatically be alerted when metrics deviate from the expected range. That’s a helpful feature which should help you focus on issues that actually matter. But on top of that, GA4’s contribution analysis will automatically help you understand why the anomalies are happening. For example, if conversion rate increased beyond what was expected, which specific audience segment drove that increase? When you’re automatically getting alerts about unusual data patterns and their root causes, you’ll spend less time doing rote reporting and be better equipped to take action on the data.Behavioral and Conversion Modeling for Consent Mode
Over the past several years, internet users have become more concerned about privacy. As a result, organizations now often have to get consent before collecting data about web or app usage. Of course, not all users consent, which means that marketers face the prospect of losing data. There’s a lot that organizations can do to maximize consent rates: have a clear and transparent privacy policy, provide useful personalization in exchange for the data being collected, and so on. However, consent rates will never be 100%, which begs the question: how can we “fill the gap” when we can’t directly observe data? That’s where Google’s Consent Mode comes in. Consent Mode helps you connect GA4 to a consent management platform. When users consent to being tracked, GA4 operates as normal. When users don’t consent, GA4 automatically respects those wishes, falling back on modeled data rather than directly observed data. How does this work? When a user declines consent, GA4 doesn’t track that person the way it normally would. Instead, it uses data from similar users within your GA4 dataset to predict, or model, the data for the user who declined consent. In this way, you don’t have total data loss when consent is declined. Instead, you end up with a more complete and more accurate dataset, which includes a blend of directly observed data and modeled data. This helps in retaining the most accurate possible data, while respecting users’ choices when it comes to privacy.Getting Started With Machine Learning in GA4
The fact that GA4 is making turn-key ML-based solutions directly available within the product is exciting, especially for organizations that have struggled to take advantage of ML to date. In order to get started, though, there are a few key factors to consider.You need to migrate to GA4
First, and perhaps most obviously, if you want to take advantage of capabilities such as predictive audiences, you need to actually implement GA4.Track key business metrics in GA4
When you deploy GA4 on your sites/apps, be sure to track more than just the “out-of-the-box” metrics GA4 provides. Include the key business metrics that really matter for your organization. This means, for example, implementing e-commerce tracking or other kinds of conversion events like form submissions and downloads.Amass enough data to train the models and become eligible
Like all machine learning models, GA4’s models work better when they’ve had more data to learn from. In addition, there are eligibility requirements in place that need to be met before you can utilize some of these features. As such, the sooner you migrate to GA4, the sooner GA4’s models will be optimized for you, and the sooner you’ll be able to take advantage of these new capabilities.Accessible Machine Learning Capabilities for Your Business Analytics
Many organizations know that machine learning can help them optimize performance in new ways, but they run into high barriers to entry. GA4 brings several ML-based capabilities in-platform, making them accessible to any organization using GA4. Whether you want to reduce churn with customized retargeting, optimize ROAS and expand reach based on your best existing customers, or understand the root causes behind interesting trends in your data, GA4 can help. To get started, get GA4 deployed across your sites and apps, start collecting your key business data, and let GA4’s models start learning from your data, opening the door to a variety of valuable new capabilities.
