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.
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
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.
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 from Autopilot to Google Data Studio automatically. With just a few clicks, Stitch starts extracting your Autopilot data via the API, structuring it in a way that is optimized for analysis, and inserting that data into a data warehouse that can be easily accessed and analyzed by Google Data Studio.