Google Removes Key Features From GA4
In recent months, Google has removed two key features from GA4, leaving publishers and marketers mystified. The first is the removal of real-time data updates, and the second is the removal of many attribution models.
Data in GA4: nowhere near real-time
Real-time data is necessary for anyone trying to build an audience in today’s fractured attention landscape. Readers have thousands of venues and millions of topics to choose from. If you manage to get their attention, they either quickly consume your content and then forget your site forever, or you manage to engage them effectively and they become loyal readers. If you get a surge in attention, and fail to follow up and optimize the audience interest in that topic, that audience will sate their appetites elsewhere.
This is why real-time data is necessary. It is used to drive content production (“Should we write a followup?”), content distribution (“Should we email this to people? Should we tweet it?”), make immediate adjustments to improve user engagement (“Should we put this on the home page?”), optimize performance (“Did we screw up the site code?”), and enhance overall user experience.
Recently, customers have come to us sharing that GA4 is taking as long as 48 hours to completely update their data. They initially reported that Parse.ly was over-reporting yesterday’s data by 41% compared to GA4; however, when they logged in a day later, they noticed GA4 had updated to match Parse.ly’s numbers. After investigation, they found Google’s guidance states: “Google Analytics allows for a data processing delay of 24-48 hours. During that time, data may change, so reports may not be consistent when viewing the last 24-48 hours in an account.”
This impacts not only the dashboard visualization of GA4 but whatever other tools and products GA data is piped into. If you are using their data stream with internal dashboarding tools, or embedding it into your product experiences, it’s worth checking how quickly the data updates.
Parse.ly data is real-time—both the Dashboard and the Data Pipeline. The Dashboard is the familiar interface that editors, writers, and marketers use to quickly understand the impact of their content. The Data Pipeline is what engineers, data analysts, and product managers use to get a real-time data stream, enriched with content metadata and performance metrics, piped into their internal data warehouses for usage in products and dashboards. Most of the time, our customers use the Data Pipeline to combine content data with data coming from other tools (adtech platforms, CRMs, etc).
GA4 removed attribution models and made them more complicated
Google announced they are removing the First Click, Linear, Time decay, and Position-based attribution models in favor of “automated, AI-driven attribution.” The default attribution will switch to paid and organic models, with Last Click remaining available.
Teams that have built their strategies around these models will be forced to embrace Google’s AI-driven attribution models, which are more complex and opaque. To counterbalance this, Google is rolling out a new feature called “calculated metrics.” This tool allows users to craft bespoke metrics. However, we’ve found that most of the new customizable features that Google has introduced via GA4 have led to less usage of the tool: the more options you need to select, the more confusing it is, and the less conclusions you actually reach.
This will also create confusion or discrepancies across different teams and reports when there’s a lack of proper oversight and governance.
Parse.ly has First Click, Linear, Last Touch, and Last Post Touched attribution models that are easy to understand, accessible, and loved by newsrooms and marketers worldwide. Pageviews are fantastic, but you need more to tell the whole story. Our conversions attribution helps you understand the real impact of your content on business metrics.
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If your business uses real-time data or accessible conversion models, get in touch with our experts to schedule a demo today.