Does your website cater to a local, hyper local, national or global audience?
Well… in any case, it would prove useful for you to know the behavioral differences among the users of the different regions. This is where Google Analytics can help!
Google Analytics can prove useful for the following reasons:
- There may be certain products or services which garner better demand in certain regions than in others.
- There could be particular regions that give your business more overall conversions than the other regions.
- Certain marketing campaigns may cost more in specific regions.
So, how can you use the Google Analytics dashboard to get such insights for the different regions? Well, the geography data within Google Analytics enables you to organize data around specific geographical regions for your business or website.
In fact, you can customize different regions around the globe or in a country based on specific region ID codes by importing geographical data and mapping geographical IDs to custom regions.
The Geography Data import feature also empowers you with the ability to serve dynamic content throughout your site as well as focus more on promoting the popular products in a specific region to other potential users in that region.
At the same time, you may even direct users to specific regionalized landing pages that offer the necessary features to those users.
In this article, we explain how you can set up the import of Geography data as well as how you can use it for effective analytics to increase user engagement and gain more conversions for your website.
Example Usage of Geographical Data
The Google Analytics dashboard only reports data for the default geographical regions.
But, what if your business organizes data according to particular sales regions (such as East, West, and Central) for a country (for example the US) (see the reference example from Google Support). In such a scenario, importing geographical data helps to map the specific sales regions for your business to the default Google Analytics geographical regions.
You can display the data based on the custom (specific) sales regions in Google Analytics. For the example above, you just need to import the states that must be included within each custom sales region (East, West, and Central) and segment the data by region accordingly.
Setting up the Geographical Data Import Feature in Google Analytics
Now that you understand how the Geography Data import functionality can prove valuable from the simple example above, we can look at the detailed steps involved in setting up the functionality in Google Analytics.
To do so, follow the process below:
- Identify the regions that you need to import. From our example above, we are considering the three sales regions as East, West, and Central for the US.
- Look up Google’s region IDs for each state by referring to Google’s Geographic Targeting Tool, as seen below. Enter the country as “US” and select the dropdown for “State” to list the “Criteria ID” and “Name” for the various US states.
Map your site data to a geographical ID dimension. Per Google Analytics, these dimensions correspond to five different levels (continent, country, region, city, district) in the geographical hierarchy.
For our example, we will use state, which comes under the “region” level (corresponding to the geographical ID dimension “ga:regionId”) of the geographical hierarchy.
So, for the state of Alaska, the criteria ID value “21132” will be mapped to “ga:regionId”.
Next, we can view the data together in three columns in a new sheet. The three columns will be the region ID (mapped from the criteria ID), our custom sales region, and the state.
An example, mapping of these three columns for the US state of Alaska would look this way:
ga:regionID Sales Region State 21132 West Alaska
- The sales region dimension does not exist in your Google Analytics dashboard by default. So, we first need to create (add) this custom dimension by following the steps below:
- From your Google Analytics dashboard, navigate to the Admin tab -> Property column -> Custom Definitions -> Custom Dimensions.
- Select “New Custom Dimension”.
- Add the name as “sales region” and set the scope to “Session”.
- You can start collecting the data immediately by ticking the “Active” box and clicking the “create” button.
You need a container (data set) to hold the imported data which is the mapping of the criteria IDs to the sales regions. Follow the below steps to create a new geographical data set:
- From your Google Analytics dashboard, navigate to the Admin tab -> Property column -> Data Import.
Click on “New Data Set” and choose “Geography Data” as the type.
You can give the data set a name, such as “US sales regions” or just “sales regions”.
- Organize the “Data Set Schema” by setting the “key” as your geographical dimension ID (ga:regionId for our example) and “imported data” as your custom dimension (“sales region” for this case).
- Click on “Get Schema” to get it as your CSV upload file header.
- You can download the schema template from the previous step and start creating a spreadsheet to upload the region specific data of your choice. The first row of this spreadsheet will contain the internal dimension names (the header obtained from Step 7 above). You can then enter the data in a manner similar to the first two columns of the mapping table in Step 4.
- Finally, you can export the above spreadsheet as a .csv file and upload the data file to Google Analytics from the “Data Import” zone under the “Property” column.
How Do You Display the Custom Region-Based Dimensions in the Reports?
For our example above, sales region is a custom dimension that will not appear by default in the standard reports (for example the location report). So, to view this dimension in the Google Analytics dashboard location report, you can add “country” as the primary dimension and select
“sales region” as the secondary dimension. You can even include these dimensions in custom reports.
We hope the above information about geographical data and the process to import it proves useful for your website or business. Please let us know your feedback or queries in the comments section below!