As a C-level, you know how important it is to have accurate data about your customers and product in order to make strategic decisions and identify insights for improvement, whether in marketing, product or business. However, you probably use many tools that generate large amounts of data, without unifying and each platform has its own reports. That’s why migrating your Google Universal Analytics data to your database, can be interesting to became a data decisions maker, but it can be a complicated process, and if it´s not done correctly, you could lose valuable information about your customers.

But don’t worry, in this article we will show you how you can migrate your Google Universal Analytics data to your database safely and efficiently. So if you want to avoid any possible data loss and make sure you have all the necessary information about your customers, read on!

Analytics in Google Analytics vs your database

Google Analytics is a very useful tool for understanding user behaviour on your site. But what if you want to combine the information you get from GA with your own customer data stored in your databases?

To be able to do this, you need to be able to transfer the information from GA to your own tools and systems so that you can access it quickly and easily. However, this is not always easy, as it can be difficult to migrate GA data into your databases without losing important information.

That’s why it’s essential to have a reliable method for migrating data from GA to your databases. This way, you can ensure that the information you collect from your users is effectively integrated with your own customer data, allowing you to get a more complete view of your users and make more informed decisions in your marketing strategy.

Google analytics export raw data

Fortunately, there are several options for moving Google Universal Analytics data into your database with minimal effort. Here are some of the most popular options:

1. Use cloud-based analytics tools: If you are already using tools such as Looker or Tableau, you can easily set up an automated process to extract GA data from your platform and load it directly into your database. This will allow you to get valuable information from your website users without having to worry about the data migration process.
2. Create a custom ETL pipeline: If you are not using a cloud-based analytics tool, another option is to create a custom ETL pipeline using BigQuery or other similar technology. This option may be more technical, but it will allow you to extract and transfer GA data more quickly and efficiently.
3. Use a data lake solution: Another option is to implement a data lake in Amazon S3 or Azure Blob Storage to store all raw GA event streams in a central location before transferring them to your own databases. This option makes it easy to store all raw event streams without having to worry about overwriting existing records or losing valuable historical information during the migration process.

In addition, storing all raw event stream information in a central location such as S3 or Azure Blob Storage can reduce complexity when trying to access different data sets from different sources, as they will all be stored in one place.

Export Google Analytics data to excel for calculation of cac and performance metrics for forecasting.

This could be a real use case of how we can leverage the data exported to our database.
The workflow does not end once the Google Analytics data has been imported into a data lake. Modelling work is needed, to work with the raw data, before we can extract useful information.
Let’s take an example of the tasks needed to calculate a metric such as CAC (Customer Acquisition Cost).

1. Google Analytics data needs to be modelled to calculate metrics such as campaign cost and CAC. This involves extracting the cost of AdWords campaigns and adding it as a field in the data table.
2. As for GA4, the date field needs to be converted to ISO 8601 timestamp format. This can be done in the same table and a trigger can be set up to activate when it is updated.
3. Access to other datasets such as Facebook, linkedin… is needed.
4. Obtain customer data from our database or from Stripe.
5. Add these fields to calculate the final metric.

Conclusion

Migrating data from Google Universal Analytics (GA) to your own databases is a process that can be complicated if you have not gone through it, it can lead to a considerable increase in server costs if you are not careful and it will take several months of implementation as well as hiring people specialised in architecture.
If you want to speed up the process, avoid complications in the implementation, you know that you can use our tool.