I recently had a client who needed to integrate multiple systems with an email marketing system and move data between them using Make.com.
Make.com is an automation platform that connects systems and automates workflows without coding. One of its features is the Data Store, which acts as a simple database within the platform.
One challenge was standardising country names across different systems. Each system used different formats: some used full names, others ISO codes, and a few had custom abbreviations. For example, “United States” could also be named “United States of America”, “USA”, or “U.S.A.” This inconsistency can cause problems with data analysis, reporting, and system integration.
The client had an internally agreed list of country names, and the email marketing system needed to store them accordingly.
My project was to integrate Bizzabo and Zoom Webinar event booking systems. I found multiple examples where both systems named some countries differently from the agreed list.
I created a Data Store in Make containing the mapping. One column had the country name as defined by the various third-party systems, and the other had the internally agreed country name.
The set-up process was straightforward:
- Create a new Data Store in Make
- Import the mapping data (you can use CSV for this)
- Set up a search function in your scenario to match incoming country names.
From there, I can search the data store and use the internally agreed name in the next step.
Figure 1: Data Store setup showing country name mapping
The country name returned from the data store can then be used in the next step.
Figure 2: Make scenario using the Data Store result
You can take this one step further and check if the Data Store returns a country. If not, use the country from the source. You can use Make.com’s ifempty() to achieve this, as shown here:
Figure 3: Make scenario using the Data Store result in an ifempty()
The ifempty() function will use the first value, as long as it’s not empty. If it is empty, it will use the second value. In this case, if the Data Store doesn’t find a match, it will be empty, and the country from the source system (the one that is triggering this scenario will be used instead.
With this in place, we can be sure that the country names in the email marketing system are always consistent.
Wrapping up
This is a simple example of leveraging Make’s Data Store to search and replace text automatically. It’s a great way to keep data consistent and easy to update the Data Store if needed.
Other Applications
This approach is useful for other types of data standardisation, such as:
- Product names and SKUs across different e-commerce platforms
- Company names in CRM and accounting systems
- Job titles in HR systems
- Currency codes in financial applications
The key benefit is maintaining the mapping in one central location, making it easy to update and manage over time.