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Integrating Marketing Cloud with Google Data Studio

Does your organization use Salesforce Marketing Cloud for marketing automation and Google Data Studio for reporting and analysis? Have you tried to get data from the former into the latter, only to find that there is no existing connector that easily joins Marketing Cloud and Data Studio?

One of our clients recently came to us with this exact issue, and Fíonta discovered that several options exist for organizations that might need to connect Marketing Cloud and Data Studio to gain a fuller picture of marketing ROI. Happily, these options exist at multiple levels of complexity — and of investment.

Before you begin

We start with some key decisions your organization might want to make before embarking on this project:

  • How often do you need to update reports in Data Studio?
  • How much data do you need to export from Marketing Cloud?
  • Are there specific additional features you’d be interested in exploring, such as real-time data updates, managed syncing, or end-user support?
  • What are your budget parameters for this project?
  • Do you prefer a connection that can be managed by internal resources, or a more robust solution that might require ongoing maintenance?

Depending on your answers to those questions, you might consider one of the following solutions, listed below in order of developer effort (least to most).

Manual export and upload

The lowest lift connection requires manual export of your relevant data from Marketing Cloud, with a corresponding manual upload to Data Studio. This basic option is low cost and requires no ongoing maintenance, although it is less flexible than some others.

There are two possible ways to export data from Marketing Cloud:

  • Via a manual or automated CSV export, which has the benefit of being a no-code export, and can be accessed via FTP.
  • Through Marketing Cloud’s API. The tradeoff for the custom development required for this approach is that you will likely have more flexibility and control over what data is exported.

If you are interested in a no-frills manual approach, you can follow these instructions to create a CSV export, and use Data Studio’s CSV file upload connector to upload the exported data. As this is a strictly manual method, you cannot set automated uploads on a schedule, but of course you can perform the export / upload process as often as is required for your organization’s specific reporting needs.

Use of a connection service

There are several third-party services that offer pre-created Marketing Cloud-to-Data Studio connectors, including Panoply, Skyvia, and Hevo. Use of a data management connection service requires no development on your organization’s side, either initially or as APIs change over time, and offers features that would be difficult to replicate in the simpler manual export-and-upload solution outlined above. However, these solutions do require a monthly investment of $200+ per month, depending on the service chosen and your organization’s specific requirements as outlined in the Before You Begin section above. 

Custom connection solution

A custom-built connection is the gold standard for maximum strength, flexibility, and customization. Creating a bespoke connection for your organization requires custom development upfront, along with potentially periodic maintenance/updates as APIs change, and may also involve ongoing licensing or hosting costs. 

We outline some custom solutions below which assume the use of a Heroku app with Python or nodeJs.  Most of these solutions also require third-party services which charge their own fees, depending on the service. 

  1. Google Sheet connector: This is the lowest service fee option, but comes with the highest risk of API changes requiring maintenance. In this case, a Python app would grab a CSV from Marketing Cloud via FTP, and upload that CSV to a Google Sheet via Google’s APIs. The Google Sheet connector in Data Studio would be configured to pull data from that sheet. This would likely work best with smaller data sets.
  2. Big Query / MySQL / Postgres connectors: This is likely the most flexible custom solution. In this case, a Python app would pull data from Marketing Cloud (either CSV or API) and update the database directly. Data Studio includes connectors for each of these types of databases already. In most cases, hosting a database anywhere incurs service fees, which would increase the ongoing cost of this solution. Big Query has fees for the amount of data stored, and per-query; MySQL and Postgres hosts usually charge a flat monthly rate.  
  3. CSV via Supermetrics: This solution involves fees to third-party service providers, but could potentially be a lower-code solution while not requiring the maintenance of an app to connect with Google Sheets. Supermetrics is a premium connector service, starting at $39/month at the time of this writing. There is a Data Studio connector for Supermetrics, and Supermetrics can automatically pull a public CSV file from elsewhere, on an automated schedule. This does have security implications since the URL to the file has to be publicly accessible, as Supermetrics documentation states it does not work with logins. This would require a Heroku app to download a CSV, or to use the API and create one, and then host it on a web service for Supermetrics to find.  

Fíonta understands that every nonprofit and association has particular requirements when it comes to reporting and analysis, and we would be happy to help your organization select and implement the best possible solution for your specific use cases. We are also well versed in custom development, particularly with Heroku, and happy to advise on the “buy versus build” analysis that may be of interest when considering third-party services and Total Cost of Ownership (TCO). 

Contact us if you have any questions or would like to start down the road of more robust marketing data reporting and analysis!