Autopilot to Google Data Studio

This page provides you with instructions on how to extract data from Autopilot and analyze it in Google Data Studio. (If the mechanics of extracting data from Autopilot seem too complex or difficult to maintain, check out Stitch, which can do all the heavy lifting for you in just a few clicks.)

What is Autopilot?

Autopilot is a visual tool that allows marketers to track their prospects' customer journeys. Some of the information stored in Autopilot is valuable input for business analytics.

What is Google Data Studio?

Google Data Studio is a simple dashboard and reporting tool. It's free and easy to use, but it lacks the sophisticated features of higher-end reporting software. Many of the connectors it supports are for Google products, but third parties have written partner connectors to a wide variety of data sources. Its drag-and-drop report editor lets users create about 15 types of charts.

Getting data out of Autopilot

Autopilot exposes data through a REST API, which developers can use to extract information. For example, to retrieve a batch of 100 contacts, you could call GET /v1/contacts.

The call returns a JSON object with two or three properties as a reply:

  • total_contacts: the total number of contacts
  • contacts: the current batch of 100 contacts
  • bookmark: if there are more contacts on the list, the bookmark allows you to access the next group of contacts via another GET call.

Each Autopilot contact may have any or all of 26 standard fields, along with any custom fields you may have defined.

Loading data into Google Data Studio

Google Data Studio uses what it calls "connectors" to gain access to data. Data Studio comes bundled with 17 connectors, mostly to pull in data from other Google products. It also supports connectors to MySQL and PostgreSQL databases, and offers 200 connectors to other data sources built and supported by partners.

Using data in Google Data Studio

Google Data Studio provides a graphical canvas onto which users drag and drop datasets. Users can set dimensions and metrics, specify sorting and filtering, and tailor the way reports and charts are displayed.

Keeping Autopilot data up to date

At this point you’ve coded up a script or written a program to get the data you want and successfully moved it into your data warehouse. But how will you load new or updated data? It's not a good idea to replicate all of your data each time you have updated records. That process would be painfully slow and resource-intensive.

Instead, identify key fields that your script can use to bookmark its progression through the data and use to pick up where it left off as it looks for updated data. Auto-incrementing fields such as updated_at or created_at work best for this. When you've built in this functionality, you can set up your script as a cron job or continuous loop to get new data as it appears in Autopilot.

And remember, as with any code, once you write it, you have to maintain it. If Autopilot modifies its API, or sends a field with a datatype your code doesn't recognize, you may have to modify the script. If your users want slightly different information, you definitely will have to.

From Autopilot to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Autopilot data in Google Data Studio is to store that data inside a data warehousing platform alongside data from your other databases and third-party sources. You can find instructions for doing these extractions for leading warehouses on our sister sites Autopilot to Redshift, Autopilot to BigQuery, Autopilot to Azure SQL Data Warehouse, Autopilot to PostgreSQL, Autopilot to Panoply, and Autopilot to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to move data automatically, making it easy to integrate Autopilot with Google Data Studio. With just a few clicks, Stitch starts extracting your Autopilot data, structuring it in a way that's optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Google Data Studio.