In today’s world, where everyone is striving to reach the holy grail of Artificial Intelligence, it is essential to have high-quality and sufficient data to be utilised in data models used in data science. On the other hand, we have Google Analytics 4, which in its standard (free) edition already includes some artificial intelligence components, particularly predictive analytics, beneficial for e-commerce and purchasing. We must not forget that this version already has a connector to export data to Google BigQuery, which is very beneficial for several reasons:
- No historical limitations (Google Analytics 4 only allows for 14 months of data exploration)
- Correction of bots that are recorded, as far as I know, there is no retroactivity, meaning there is no reprocessing, and Google BigQuery allows for this use with T-SQL commands.
- Data maintained in Google BigQuery can be integrated with other BackOffice systems such as your ERP and CRM, in addition to increasingly important loyalty systems, especially in the uncertain days of both national and international economies.
- The ability to use models embedded in Google BigQuery, such as BigQuery ML, to build prediction models for churn, purchase probability, and purchase frequency. This offers the opportunity to work with inventory management to create product bundles that sell well together.
- Creation of specific audiences through the updating of models created with data in Google BigQuery, thereby utilising the Google Marketing Platform and platforms like Google Ads, Search Ads 360, and DV 360.
Join us on the journey of data-driven marketing!
- Understanding Google Analytics 4: Why You Should Export Data to Google BigQuery
- Artificial Intelligence in Marketing More Than Just a BuzzWord
- Understanding Google Analytics 4 – How Data Integration Benefits Your Business
- Mitigate privacy concerns on digital campaigns and third-party cookies
- Understanding Churn in Google Analytics 4 : A business requirement