MailChimp to Power BI

This page provides you with instructions on how to extract data from MailChimp and analyze it in Power BI. (If the mechanics of extracting data from MailChimp 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 MailChimp?

MailChimp is a marketing automation platform and email marketing service that companies use to send more than a billion email messages a day.

What is Power BI?

Power BI is Microsoft’s business intelligence offering. It's a powerful platform that includes capabilities for data modeling, visualization, dashboarding, and collaboration. Many enterprises that use Microsoft's other products can get easy access to Power BI and choose it for its convenience, security, and power.

With high-value use cases across analysts, IT, business users, and developers, Power BI offers a comprehensive set of functionality that has consistently landed Microsoft in Gartner's "Leaders" quadrant for Business Intelligence.

Getting data out of MailChimp

MailChimp also offers a RESTful API for syncing campaign information and stats. To get your MailChimp data into your data warehouse, you can extract it from MailChimp's servers using the MailChimp API. To get information about a campaign with the API, for example, you could call GET /campaigns/{campaign_id}.

MailChimp generates a lot of data. Anytime you send an email or someone opens and reads one, it generates an event. Depending on your needs, you may want to use webhooks to receive streaming updates of these events as they happen. If so, you'll need to build code on your end to receive the streaming data.

Sample MailChimp data

The MailChimp API returns JSON-formatted data. A call to get campaign information might return data that looks like this.

{
  "id": "42694e9e57",
  "type": "regular",
  "create_time": "2018-12-15T14:40:36+00:00",
  "archive_url": "http://eepurl.com/xxxx",
  "status": "save",
  "emails_sent": 0,
  "send_time": "",
  "content_type": "template",
  "recipients": {
    "list_id": "57afe96172",
    "segment_text": ""
  },
  "settings": {
    "subject_line": "I have a watermelon farm.",
    "title": "Freddie's Jokes Vol. 1",
    "from_name": "Freddie",
    "reply_to": "freddie@freddiesjokes.com",
    "use_conversation": false,
    "to_name": "",
    "folder_id": 0,
    "authenticate": true,
    "auto_footer": false,
    "inline_css": false,
    "auto_tweet": false,
    "fb_comments": false,
    "timewarp": false,
    "template_id": 100,
    "drag_and_drop": true
  },
  "tracking": {
    "opens": true,
    "html_clicks": true,
    "text_clicks": false,
    "goal_tracking": true,
    "ecomm360": true,
    "google_analytics": true,
    "clicktale": ""
  },
  "delivery_status": {
    "enabled": false
  },
  "_links": [
    {
      "rel": "parent",
      "href": "https://usX.api.mailchimp.com/3.0/campaigns",
      "method": "GET",
      "targetSchema": "https://api.mailchimp.com/schema/3.0/Campaigns/Collection.json",
      "schema": "https://api.mailchimp.com/schema/3.0/CollectionLinks/Campaigns.json"
    },
    {
      "rel": "self",
      "href": "https://usX.api.mailchimp.com/3.0/campaigns/42694e9e57",
      "method": "GET",
      "targetSchema": "https://api.mailchimp.com/schema/3.0/Campaigns/Instance.json"
    },
    {
      "rel": "delete",
      "href": "https://usX.api.mailchimp.com/3.0/campaigns/42694e9e57",
      "method": "DELETE"
    },
    {
      "rel": "cancel_send",
      "href": "https://usX.api.mailchimp.com/3.0/campaigns/42694e9e57/actions/cancel-send",
      "method": "POST"
    },
    {
      "rel": "feedback",
      "href": "https://usX.api.mailchimp.com/3.0/campaigns/42694e9e57/feedback",
      "method": "GET",
      "targetSchema": "https://api.mailchimp.com/schema/3.0/Campaigns/Feedback/Collection.json"
    }
  ]
}

Preparing MailChimp data

If you don't already have a data structure in which to store the data you retrieve, you'll have to create a schema for your data tables. Then, for each value in the response, you'll need to identify a predefined datatype (INTEGER, DATETIME, etc.) and build a table that can receive them. MailChimp's documentation should tell you what fields are provided by each endpoint, along with their corresponding datatypes.

Complicating things is the fact that the records retrieved from the source may not always be "flat" – some of the objects may actually be lists. In these cases you'll likely have to create additional tables to capture the unpredictable cardinality in each record.

Loading data into Power BI

You can analyze any data in Power BI, as long as that data exists in a data warehouse that's connected to your Power BI account. The most common data warehouses include Amazon Redshift, Google BigQuery, and Snowflake. Microsoft also has its own data warehousing platform called Azure SQL Data Warehouse.

Connecting these data warehouses to Power BI is relatively simple. The Get Data menu in the Power BI interface allows you to import data from a number of sources, including static files and data warehouses. You'll find each of the warehouses mentioned above among the options in the Database list. The Power BI documentation provides more details on each.

Analyzing data in Power BI

In Power BI, each table in the data warehouse you connect is known as a dataset, and the analyses conducted on these datasets are known as reports. To create a report, use Power BI’s report editor, a visual interface for building and editing reports.

The report editor guides you through several selections in the course of building a report: the visualization type, fields being used in the report, filters being applied, any formatting you wish to apply, and additional analytics you may wish to layer onto your report, such as trendlines or averages. You can explore all of the features related to analyzing and tracking data in the Power BI documentation.

Once you've created a report, Power BI lets you share it with report "consumers" in your organization.

Keeping MailChimp 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.

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, MailChimp's API results include fields like create_time that allow you to identify records that are new since your last update (or since the newest record you've copied). Once you've take new data into account, you can set your script up as a cron job or continuous loop to keep pulling down new data as it appears.

And remember, as with any code, once you write it, you have to maintain it. If MailChimp 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 MailChimp to your data warehouse: An easier solution

As mentioned earlier, the best practice for analyzing MailChimp data in Power BI 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 MailChimp to Redshift, MailChimp to BigQuery, MailChimp to Azure SQL Data Warehouse, MailChimp to PostgreSQL, MailChimp to Panoply, and MailChimp 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 MailChimp to Power BI automatically. With just a few clicks, Stitch starts extracting your MailChimp 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 Power BI.