On July 1, 2023, Universal Analytics (Google Analytics 3) properties will stop processing new hits and will be replaced by Google Analytics 4. If you are currently using Universal Analytics, we strongly recommend that you start backing up your Google Analytics data in a database.

Here’s what you need to know:

1. What happens to my Universal Analytics data in GA3?
2. Can I move data from Universal Analytics to GA4?

The data stays back in GA3, and it is not transferred to GA4. After the end date, for a grace period of six month, you can access historical reports in Google Analytics 3 dashboard and afterwards you will permanently loose access to the data.

At the very least, you should

1. Start export your historical data from Universal Analytics prior to December 2023.
2. Try to merge the GA3 hit level data in GA4

Yes. Electrik.AI has multiple Data Pipelines which can export Universal Analytics data to any database or data warehouse.

1. Google Analytics 3 Hit Level Data
Export raw unsampled hit level data from Google Analytics 3 daily to your database. Get 300+ dimensions and metrics from Google Analytics.

2. Google Analytics 3 Historical Data
Export historical hit level data from Google Analytics 3 monthly to your database. Get 100+ dimensions and metrics from Google Analytics.

3. Google Analytics 3 Custom Reports
Export custom reports created in Google Analytics 3 daily to your database. Choose your own dimension and metrics (max. 7) from Google Analytics.

Google Analytics captures all the actions and events on your website, but it does not provide access to raw event-level data via reporting tools in the Google Analytics Dashboard and Google Analytics Data Export APIs. This makes actions like segmenting users by behavior using machine learning tools challenging. because you are limited by the granularity of data being exported from Google Analytics.
On top of that the data provided is

Export of Google Analytics Raw Data is a process of transferring raw and event level data collected from your website by google analytics platform to your private datastore. This Export of Google Analytics Raw Data to the datastore of your choice, gives you complete ownership of the data. Export of Google Analytics Raw Data is desirable, but currently, it is difficult to implement in Google unless a program is written with the use of “google analytics data export API”. Google Analytics Raw Data is a stream of event-level data collected from your website by Google Analytics. Even though this raw event-level data is present in Google Analytics it is not available unless you export it out of Google Analytics. For better understanding of google analytics raw data, check out this blog.

Exporting raw data from google analytics is not easy. Currently there are four ways to get raw data from google analytics.

• Electrik.AI
• GA Premium
• Parallel Tracking
• Custom Solution using Google Analytics Data Export API

Electrik.AI

Electrik.AI has a cloud-based export utility that can setup periodic raw data collection from Google Analytics to your desired datastore in the cloud or on premise.

Electrik.AI uses custom dimensions in google analytics to capture event level information that is not provided by Google Analytics. All events/pageview that are sent from your website to Google Analytics are enriched with event level information using a Java Script placed on your website. Thanks to this, we can export raw event data from Google Analytics along with all the dimensions and metrics associated with it.

Electrik.AI uses your Google Tag Manager container to deploy this Java Script, so that you don’t need to make any manual changes to your website. Electrik.AI improves your Google Analytics data collection, by exporting these additional dimensions along with Google Analytics data which are not available by default.

1. Timestamp: It is a custom dimension that captures the exact timestamp when the event happened, in the YYYYMMDD HH:MM:SSS format with the time zone offset.

2. Session ID: It is a session-scoped custom dimension that collects a unique id, used to identify events that belong to the same session.

3. Client ID: It is a session-scoped custom dimension that collects the unique value assigned to the client’s device from the google analytics cookie.

4. User ID: It is a custom dimension that collects the value representing a user who has logged in to your website, allowing you to identify all the sessions and events of a user.

5. Hashed IP: address – a session-scoped custom dimension that collects the hashed value of IP address used by the user. The IP address must be hashed as Google does not allow capture of PII data in GA.

This additional event level information is sent to Google Analytics along with the data that is already being sent from your website by default to Google Analytics. Check out this awesome post by Simo Ahava on improving data collection with custom dimensions and Google Tag Manager for more details.

Benefits of Exporting Raw Google Analytics Data with Electrik.AI:

Parallel Tracking

Parallel GA Tracking involves a Java script alongside the Google Analytics script, making best effort to collect about the same clickstream of data as Google Analytics to a tracking server which then sends the raw data to a storage. To implement parallel tracking of your Google Analytic data, you need a cloud platform to implement your computing needs and make sure your systems can scale automatically and you do not have to worry about managing the infrastructure.

Even after doing that you only get a fraction of the raw data which is available in Google Analytics. Plus, you lose your existing configurations in Google Analytics such as customized views, advanced segments and custom channel groupings.

Benefits of Parallel Tracking

1. Get Data in Realtime.
2. Unlimited Custom Dimensions and PII Data.

Disadvantages of Parallel Tracking

1. Less Metrics and Dimensions as compared to Google Analytics
2. Maintain Infrastructure.
3. Lose existing custom configurations in Google Analytics.

Custom Solution using Google Analytics Data Export API

Building your own solution using Google Analytics Data Export API is always a choice, you will need an understanding of how APIs work, a Google Analytics account, and a Datastore of your choice. The steps involved in google analytics raw data export are as follows:

Step 1: Identify Your Data

The first step is to identify the right dimension and metric combinations allowed by Google Analytics API. Lucky for us there is a tool provided by Google Analytics which makes it easy. Along with the dimension and metrics combination you also need to choose the time period you want to pull the data, the trick is to make multiple requests for a small time period rather than making one request for longer time period.

Step 2: Export Your Data

Now that you have identified right dimension and metric combinations, we will use the Google Analytics Reporting API to export data out of Google Analytics. This would involve making multiple requests. However, the common dimension in all your requests sent to Google Analytics would be the custom dimension we discussed earlier in this post.

Step 3: Transform Your Data

You must first transform your data to ensure that it is in a format that can be accepted by your datastore. For example, it will be easy to use a JSON format for datastores that support JSON, but you may have to choose to convert to a CSV or SQL format for more traditional relational databases. But the most important step is to join the data exported by multiple requests based on common custom dimensions to get a composite record.

Step 4: Create a Data Receiving Repository in your Datastore

Creating a data stage for your data could make your data transformation easier to perform before it is finally ingested for analysis/reporting.

Step 5: Load Your Data

It is advisable to design a schema for your chosen datastore and then map it to your Google Analytics data. In this way, you are almost ready to load your raw data from Google Analytics to a datastore after making sure that all the steps are completed to suit your needs.

Congratulations, you have now exported raw data from Google Analytics to Datastore.

But this does not end here, you need run this process daily to export raw data from Google Analytics and you must keep in mind the limits-quotas of Google Analytics APIs. Also, technologies like Google Analytics are evolving and you might find what was working yesterday might not work today. Trust me we have been following this space closely.

Benefits of Custom Solution

1. Choose the dimensions and metric you want.
2. Tailor made solution to suit your needs.

Disadvantages of Custom Solution

1. Maintain Computing Infrastructure and code.
2. Keep track of evolving Google Analytics APIs.

Give Raw Google Analytics Data a try, you would be surprised with the amount of depth it adds to your marketing data analysis. Using Electrik.AI, marketing professionals with no programming experience can export raw data from Google Analytics in few minutes. You can view the list of all dimensions/metrics exported from Google Analytics here.