Customer.io to Google Data Studio

This page provides you with instructions on how to extract data from Customer.io and analyze it in Google Data Studio. (If the mechanics of extracting data from Customer.io 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 Customer.io?

Customer.io powers email, SMS, and other customer interactions with a rules-based engine that automates communication distribution.

Getting data out of Customer.io

Customer.io publishes information about email activity through webhooks, which you can set up through its management interface. You can select from more than a dozen events to trigger a data exchange.

Sample Customer.io data

Customer.io sends the information it returns in JSON format in an HTTP POST. Each JSON object may contain dozens of attributes, which you have to parse before loading the data into your data warehouse. Here's an example of what data might look like for email-related events:

{
"data": {
  "campaign_id": "1000002",
  "campaign_name": "Upgrade to Premium",
  "customer_id": "98513",
  "email_address": "customer@example.com",
  "email_id": "NTE4MzE6FwGLxwJkAAJkABcBIfcaAVVvdGukFUsYV2hY6QFlOjQ4YTZhODljLTM3MjktMTFlNi04MDQwLTYzNGY3NzAzM2NhNjozNDMwMzEA",
  "message_id": "1000013",
  "message_name": "First Upgrade Email",
  "subject": "Have any doubts?",
  "template_id": "343031",
  "variables": {
    "attachments": null,
    "customer": {
      "created_at": 1466453747,
      "email": "customer@example.com",
      "id": 98513,
      "name": "John Doe",
      "plan_name": "free"
    },
    "email_id": "NTE4MzE6FwGLxwJkAAJkABcBIfcaAVVvdGukFUsYV2hY6QFlOjQ4YTZhODljLTM3MjktMTFlNi04MDQwLTYzNGY3NzAzM2NhNjozNDMwMzEA",
    "event": {
      "page": "https://customer.io/pricing/"
    },
    "event_id": "48a6a89c-3729-11e6-8040-634f77033ca6",
    "event_name": "viewed_pricing_page",
    "from_address": null,
    "recipient": null,
    "reply_to": null
  }
},
"event_id": "b50cb221c60f87cdf06e",
"event_type": "email_drafted",
"timestamp": 1466456299
}

Keeping Customer.io 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 Customer.io.

And remember, as with any code, once you write it, you have to maintain it. If Customer.io modifies its API, or the API 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 Customer.io to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing Customer.io 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 Customer.io to Redshift, Customer.io to BigQuery, and Customer.io to Snowflake.

Easier yet, however, is using a solution that does all that work for you. Products like Stitch were built to solve this problem automatically. With just a few clicks, Stitch starts extracting your Customer.io 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.