GA4 Transition: How It Can Enhance Your New Data Analytics Journey

In October 2020, Google announced the release of Google Analytics 4 (GA4), the next generation of its web analytics platform. This release marks a significant shift from Universal Analytics (UA), which has been the standard for web analytics since its release in 2012.

On July 1, Google will begin the process of deprecating (i.e., phasing out) the UA tracking code and will no longer provide any support or updates for UA. This means that businesses and website owners who are still using UA will no longer be able to receive technical support or benefit from any new features or improvements to the platform. Although the data collected by UA properties prior to July 1 will not be deleted or lost, businesses and website owners will no longer be able to collect any new data using UA.

Yes, Google is forcing companies to make the GA4 transition, but there will be some good, long-term benefits once you do. That’s because Google is striving to future-proof its data analytics. Whether GA4 will accomplish that goal remains to be seen.

What does future-proofing data analytics with GA4 mean?

GA4 is designed to be more flexible and customizable than UA, so it can stay ahead of the curve with expected (and hopefully unexpected) changes and demands in data collection. Google is attempting to future-proof data analytics through:

1. Privacy-centric data collection. This involves the use of first-party cookies and data streams to protect user privacy while still providing valuable data. The goal: ensure businesses can continue to collect and analyze data even as privacy regulations and user preferences play out over time. (More on this in the next section.)
2. Event-based data collection. Marketers can still track actions users take on any platform tied to GA4, such as specific button clicks, video views, and form submission. Those who used UA should already be familiar with this concept. The goal: easily track custom events and actions unique to your business.
3. Cross-device and cross-platform tracking. GA4 allows businesses to track user behavior across devices and platforms, including mobile apps and websites. The goal: help businesses get a more complete view of the customer journey and understand how users interact with their business across different touchpoints.
4. Advanced machine learning and AI capabilities. GA4 pulls from Google’s growing machine learning and AI capabilities to provide more actionable insights and predictions. The goal: quick adaptation to changing customer behavior and preferences.
5. User-centric reporting. Businesses can gain a more complete understanding of individual user behavior and preferences. The goal: allow businesses to tailor their marketing and customer experience strategies to the specific needs and preferences of their users.
6. Seamless integration with other Google tools. GA4 has been designed to integrate more seamlessly with other Google tools, including Google Ads and Google Marketing Platform. The goal: a faster, easier, and more holistic understanding of marketing performance.

Overall, making the transition to GA4 will provide businesses with a platform that is built to keep up with the evolving needs of data analytics and customer behavior. By leveraging GA4’s advanced capabilities and user-centric approach, businesses can gain valuable insights into their customers and evolve quickly in an ever-changing landscape.

Five ways GA4 will better protect user privacy

Given increasing legal action globally to preserve user privacy, Google is striving to have more privacy measures in place with GA4 out of the box. Here are five key ways:

1. Use of first-party cookies. As I mentioned before, GA4 uses first-party data instead of third-party cookies. First-party cookies are set by the website the user is visiting, whereas third-party cookies are set by a third-party domain. By using first-party cookies, GA4 can ensure that user data is not shared with other domains.
2. Enhanced user consent controls. GA4 allows businesses to implement enhanced user consent controls, including customizable cookie banners and granular consent options. This ensures that users have more control over their data and can choose whether or not to share it with the business.
3. Data minimization. GA4 collects only the data that is necessary for businesses to understand user behavior and optimize their marketing efforts. This ensures user data is not collected unnecessarily, which better protects privacy.
4. Advanced data deletion options. GA4 provides advanced data deletion options, allowing businesses to delete user data at the individual user level. Businesses can now easily honor user data deletion requests and comply with privacy regulations.
5. Enhanced data security. GA4 provides enhanced data security features, including data encryption and secure data transmission. This helps protect data from unauthorized access and breaches.

GA4’s privacy protections better align with today’s privacy regulations and user expectations. With the GA4 transition, businesses can ensure that they are collecting and analyzing data in a privacy-centric manner, which can improve user trust and overall business reputation.

Predictive metrics included in GA4

I mentioned Google is trying to future-proof data analytics with, among other things, advanced machine learning. Machine learning is behind many of the predictive metrics in GA4, and it uses historical data to forecast future trends and behavior. Some examples of predictive metrics in GA4 include:

• Purchase probability, a calculation of the likelihood a user will make a purchase on a website. This metric can help businesses identify high-value customers and target them with tailored marketing campaigns.
• Churn probability, a calculation of the likelihood that a customer will stop using a product or service. This metric can help businesses identify at-risk customers and take steps to retain them.
• Revenue predictions, a forecast of future revenue based on historical data. This metric can help businesses identify opportunities for growth and plan their marketing and sales strategies accordingly.
• Session-length predictions, a forecast how long a user will spend on a website. This metric can help businesses identify engaging content and improve the user experience on their website.

How can GA4 protect privacy and predict customer behavior at the same time?

I know if may seem counterintuitive that GA4 is designed to both protect user privacy and provide businesses with even better customer behavior insights than UA, but Google is attempting to walk this fine line by using a combination of privacy-focused features and machine learning algorithms.

As I noted earlier, when you make the GA4 transition, the new platform will include several privacy-focused features designed to protect user data. For example, GA4 uses a privacy-focused data model that allows businesses to collect only the data they need and no more. It also includes advanced data controls that allow businesses to configure data collection based on user consent and other privacy settings.

At the same time, GA4 uses machine learning algorithms to predict customer behavior based on historical data, which Google has been collecting for more than a decade. These algorithms are designed to work within the privacy-focused data model to protect user data while still providing valuable insights. In addition, GA4 includes features that allow businesses to analyze customer behavior across multiple devices and channels, providing a more complete picture of customer interactions. This helps businesses make more informed decisions about their marketing and sales strategies, while still protecting individual user privacy.

Overall, GA4 is designed to balance the need for valuable customer insights with the need to protect user privacy. By using advanced privacy-focused features and machine learning algorithms, GA4 allows businesses to predict customer behavior while still respecting user privacy.

GA4 uses machine learning algorithms to predict customer behavior based on historical data, which Google has been collecting for more than a decade.

GA4 transition can help small businesses reach their profitability goals

Data analytics can help a small business reach its profitability goals in several ways. Depending on your business model and the products or services you sell, GA4 can support you in:

• Identifying profitable customer segments by analyzing the customer behaviors and preferences associated with higher profitability. This information can then be used to target marketing efforts more effectively and to tailor products and services to meet the needs of the most profitable customers.
• Optimizing pricing strategies by analyzing sales data and pricing trends to find the price points associated with the highest profitability. This information can be used to adjust pricing strategies to maximize profits.
• Improving operational efficiency and streamlining operations by analyzing data on sales, inventory, and other key performance indicators (KPIs). This can help businesses to increase profitability by reducing overhead costs and improving margins.
• Identifying new revenue streams through analyzing market trends and customer data. This can help you find opportunities to expand into new markets or offer new products and services, potentially increasing revenue and profitability over time.

Companies of all sizes can take advantage of GA4 capabilities

With the tools and strategies GA4 offers, even small businesses with limited resources can better optimize their marketing efforts and improve ROI.

First, focus on key metrics. Decide what KPIs matter most to your business. GA4 allows you to set up custom metrics and dimensions, which can provide valuable insights into customer behavior and marketing performance.

Second, don’t miss out on the new AI-powered insights, especially predictive ones. I already mentioned GA4 leverages advanced machine learning and AI capabilities. The platform provides valuable insights into customer behavior and trends, including built-in reports and automated logic, which can help small businesses more quickly identify opportunities for growth.

Third, make data-informed, not data-driven, decisions. As a data analyst, I’ve built my work around this principle because there’s an important distinction here. Data-driven decisions and data-informed decisions are both based on data analysis, but they differ in their level of reliance on data.

Data-driven decisions are based solely on data analysis. In a data-driven approach, data is used to guide and inform decision-making, and the final decision is made solely based on the insights revealed by data analysis. This approach assumes that data is objective and can be used to make the best decisions possible.

Data-informed decisions, on the other hand, are decisions that are based on a combination of data analysis and other factors, such as your business goals, experience, and expert judgment. In a data-informed approach, data is used to inform decision-making, but it is not the sole factor that determines the final decision. This approach, which I adopted early in my career, recognizes that data can provide valuable insights, but it also acknowledges that data has limitations and cannot provide a complete picture of a situation.

Read more about my perspective on data-informed decision-making in business. Not sure how to make your company’s GA4 transition? The Dames Marketing & PR can help.