5 Reasons to Migrate to GA4 and BigQuery
Tags: Google, Bigquery, Ga4, Google Algorithm, Google Analytics

Google Analytics 4(GA4) is the next generation of Google Analytics rolled out by Google in 2020. There are major differences between GA4 and Universal Analytics. The most fundamental of them being the complete migration to event-based data capture in GA4 where everything from a session to a transaction is captured as an event or an event parameter. BigQuery is a cloud data warehouse within Google Cloud Platform(GCP) that lets you run highly performant queries of large datasets and can be connected to GA4 for data export.

Why migrate to GA4 and BigQuery?

1. Raw Data Export at no additional cost

Universal Analytics(UA) raw data export to BigQuery was historically only available to the paid version of GA or GA360. Unlike previous versions, GA4 raw data export is readily available to everyone at no additional cost. So access to hit-level data for big data purposes is easily available to everyone with quick integration of GA4 and BigQuery. Access to both days as well as live streaming data (intraday) data is available at no additional cost. BigQuery also has a free tier of 10GB of monthly storage and 1TB of monthly processing. This is a reasonable size given for free for a lot of hit level data and with proper database pruning mechanisms, can accommodate data for multiple recent months for modeling purposes.

2. Easy Upgrade from Universal Analytics

The upgrade from Universal Analytics to GA4 is seamless and easily configurable if you are using Universal Analytics. The property can be set alongside Universal Analytics in both cases of a gtag.js based implementation or a tag manager-based implementation. GA4 Setup assistant by Google is available to move to GA4 without many hassles. Enhanced Ecommerce implementation can be slightly more tedious compared to a standard implementation. The details on tag manager-based enhanced e-commerce migration can be explored here.

3. Default Analytics going forward

When setting up a new Analytics, GA4 has become the default analytics within the Google Analytics setup. We suggest setting up GA4 alongside Universal analytics to capture data on both GA4 and previous versions. However, going forward there are chances that Universal Analytics might get deprecated especially for the free users of GA and they will be asked to migrate to GA4. Considering the differences in GA4 and the learning curve for using a new solution, we recommend users get familiar with GA4 before it gets forced upon.

4. Flexibility and No Limitations

Unlike Universal Analytics where the schema of GA decides the number of custom attributes to be passed, GA4 has no such limitations. Using the event parameters, you can pass as many different attributes for a hit on the website or the mobile app. In Universal Analytics users could only pass parameters of Event Action, Event Label, Event Value, and Category for each event. In GA4 users can pass as many custom parameters as needed. As every hit in GA4 is recorded as an event. This gives the ability to capture as many details as possible for a user action using event parameters.

Secondly, Universal Analytics had many detailed reports under Acquisition, Behavior, and Audience built-in and the flexibility in terms of creating custom reports were available. However, custom visualization and combining attributes of different scopes were fairly limited. GA4 on the other hand had minimal pre-built reports and encourages creating custom reports and visualizations. Analysis Hub and Templates within the exploration section of GA4 provides an open-source library for sharing and collaboration purposes. This provides unlimited possibilities for visualization and analysis with GA4, unlike Universal Analytics.

Thirdly when marketers move your data into BigQuery, they have the flexibility of combining the data with external sources for composite analysis and modeling. This can be achieved by stitching the data using the User ID attribute available within Google Analytics. This is a powerful feature to enable a data warehouse in an owned cloud (GCP) setup.

5. Machine Learning Capabilities Available in BigQuery

BigQuery ML enables users to create and execute machine learning models in BigQuery by using standard SQL queries. BigQuery ML empowers marketers to build powerful models using GA4 hit-level data. So enabling GA4 and BigQuery, Marketers can use BigQuery ML to build and evaluate ML models. Analysts don’t need to export small amounts of data to spreadsheets or other applications or wait for limited resources from a data science team. Models available within BigQuery can use to solve multiple cases including churn modeling, recommendation system, and propensity models. Advanced marketers can also build their own Customer Data Platform (CDP) using GA4 BigQuery capabilities. Details on the same can be explored here.

Google Analytics 4 is the future of analytics and using the BigQuery Integration provides capabilities to work at raw hit level data and combine data streams. The machine learning capabilities available within GA4 are tremendous and can help in setting up the machine learning foundation within the marketing arm of any organization.

If you need help setting up Google Analytics 4 for your apps or website and create a data warehouse, Deepflux is here for you. Get in touch with our team today.