Campaign Monitor to Tableau

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

Campaign Monitor is a SaaS email marketing platform that enables businesses to create, send, and manage branded email. The product includes a drag-and-drop builder, visual journey designer, and real-time performance metrics. It offers list management tools to help organizations build segmented lists based on their own criteria, and optimization tools to help users track the effectiveness of their campaigns.

What is Tableau?

Tableau is one of the world's most popular analysis platforms. The software helps companies model, explore, and visualize their data. It also offers cloud capabilities that allow analyses to be shared via the web or company intranets, and its offerings are available as both installed software and as a SaaS platform. Tableau is widely known for its robust and flexible visualization capabilities, which include dozens of specialized chart types.

In addition to its business software, Tableau also offers a free product called Tableau Public for analyzing open data sets. If you're new to Tableau, this offering is a great way to experience Tableau's capabilities at no cost and share your work publicly.

Getting data out of Campaign Monitor

You can use Campaign Monitor's RESTful API to get data about clients, campaigns, lists, and more into your data warehouse. For example, to get a campaign summary, you could GET GET /campaigns/{campaignid}/summary.{xml|json}. The final parameter specifies whether the data is to be returned in XML or JSON format.

Sample Campaign Monitor data

Here's an example of the kind of response you might see when querying a campaign summary and specifying that the data returned should be in JSON format.

{
    "Recipients": 1000,
    "TotalOpened": 345,
    "Clicks": 132,
    "Unsubscribed": 43,
    "Bounced": 15,
    "UniqueOpened": 298,
    "SpamComplaints": 23,
    "WebVersionURL": "http://createsend.com/t/y-A1A1A1A1A1A1A1A1A1A1A1A1/",
    "WebVersionTextURL": "http://createsend.com/t/y-A1A1A1A1A1A1A1A1A1A1A1A1/t",
    "WorldviewURL": "http://myclient.createsend.com/reports/wv/y/8WY898U9U98U9U9",
    "Forwards": 18,
    "Likes": 25,
    "Mentions": 11
}

Loading data into Tableau

Analyzing data in Tableau requires putting it into a format that Tableau can read. Depending on the data source, you may have options for achieving this goal, but the best practice among most businesses is to build a data warehouse that contains the data, and then connect that data warehouse to Tableau.

Tableau provides an easy-to-use Connect menu that allows you to connect data from flat files, direct data sources, and data warehouses. In most cases, connecting these sources is simply a matter of creating and providing credentials to the relevant services.

Once the data is connected, Tableau offers an option for locally caching your data to speed up queries. This can make a big difference when working with slower database platforms or flat files, but is typically not necessary when using a scalable data warehouse platform. Tableau's flexibility and speed in these areas are among its major differentiators in the industry.

Analyzing data in Tableau

Tableau's report-building interface may seem intimidating at first, but it's one of the most powerful and intuitive analytics UIs on the market. Once you understand its workflow, it offers fast and nearly limitless options for building reports and dashboards.

If you're familiar with Pivot Tables in Excel, the Tableau report building experience may feel somewhat familiar. The process involves selecting the rows and columns desired in the resulting data set, along with the aggregate functions used to populate the data cells. Users can also specify filters to be applied to the data and choose a visualization type to use for the report.

You can learn how to build a report from scratch for free (although a sign-in is required) from the Tableau documentation.

Keeping Campaign Monitor data up to date

Now what? You've built a script that pulls data from Campaign Monitor and loads it into your data warehouse, but what happens tomorrow when you have new campaigns?

The key is to build your script in such a way that it can identify incremental updates to your data. Thankfully, Campaign Monitor's API results include fields like Date 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.

From Campaign Monitor to your data warehouse: An easier solution

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