But there’s another way to add data to Google Analytics – you can import data using a feature called Dimension Widening.
With Dimension widening you can import additional dimensions and metrics directly into Google Analytics via a CSV upload or programmatically import data via an API.
Let’s take a look at how you might use Dimension Widening to augment the data in your account, and ultimately do better analysis.
Why Add More Data?
Analytics is more valuable when you can align the tool more closely with your business strategies and tactics. Adding additional data, like customer history, content publishing information, advertising cost data, etc. can help provide context to your data, thus making it easier to gauge performance and identify opportunities for improvements.
Adding additional data can also streamline your reporting (yes, basic reporting still happens) by consolidating all of your data in a single system that everyone has access to.
That’s where Dimension Widening comes in.
It is a mechanism to move data into Google Analytics.
How Dimension Widening works
You can upload two types of data to Google Analytics: Dimensions and Metrics.
A dimension is an attribute of a user or the sessions she creates.
A metric counts something – like time, money, clicks, etc.
When you use Dimension Widening you are uploading values for one or more dimensions or metrics.
You can upload values for existing dimensions/metrics or you can upload values for new dimensions/metrics that do not exist in Google Analytics.
When Google Analytics process the data it will join your custom data to the the existing data using something called a key.
The key binds your data, the data uploaded in a CSV file or sent programatically, to the Google Analytics data. When Google processes the custom data it will look at the value for the key, and then try to find the same value in the Google Analytics data.
If Google Analytics finds a matching keys then it will take the data in that row of the custom data and pull it into Google Analytics.
There are four basic steps to configuring Dimensioning Widening.
1. Identifying the data you want to import.
Step one is really simple, identify the data that you want to add to Google Analytics.
Remember, you can import a value for any dimension or metric that currently exists in Google Analytics. OR you can import values for custom dimensions and custom metrics that are not normally found in GA – more on this below.
When choosing the data you want to import ask yourself this – what data to I need to understand the behavior of my users? How can I make my analytics life easier by consolidating data in Google Analytics?
You also need to define your key. This is obviously critical. If you can’t define a key then you can’t import data.
2. Create the schema in Google Analytics.
Once you define your key and the dimensions/metrics you want to import it’s time to add the schema to Google Analytics. Think of this step as telling Google Analytics how to interpret the CSV file (or data feed) that you will import.
Choose a property in the admin section, then choose Data import and Dimension Widening.
To begin you need to name the data set you will import. You can actually upload multiple data sets (more on this later), so make sure you name it something very descriptive, like “Campaign Data” or “Content Information”.
Then choose the view where you would like the data applied.
TIP: Dimension widening will permanently change the data in a reporting view! It’s a good idea to test your dimension widening on a TEST view before applying it to your main reporting view.
Now add the schema. First, add the key that you’ve defined for your data.
Next, specify the dimensions and metrics that you want to add.
Here’s something cool – as you choose your key and dimensions Google Analytics will automatically show you the column headings that you will need to add to your CSV file.
Notice that they’re not the names that appear in the drop down boxes. They’re the dimension/metric names that are used in the API. Fear not – you don’t need to understand what they mean.
3. Build your CSV file.
Once you finish defining your schema choose save.
You’ll be presented with two options: get more details of your CSV file OR get an API key to upload your data programatically. Let’s focus on the Get Schema option.
Click the Get Schema button.
This window contains some really useful information. First, a list of the column headers that you need to add to your CSV file. This includes your key and all the other dimensions that you are adding to Google Analytics.
There’s also a way to download a CSV template for your specific data. The template is just an Excel file with the headers added to the first row.
4. Upload your CSV file or Send Data via API
Remember, there are two ways to add your data – via an API or manually via a file upload process. Let’s focus on the later – the file upload.
This isn’t too complicated, just click upload :) Once the file is uploaded Google Analytics will widen your data as it is processed.
NOTE: when you use Dimension Widening the data you import is NOT applied to historical data. Your data is only applied going forward.
I find that GA can process the file very fast (minutes). You may want to refresh your list often to determine if the new data has been added.
That’s it! That’s the basic process.
But you probably want to use Dimension Widening to import custom data, not data that’s already in Google Analytics. Let’s take a look at how to do that.
How to add Custom Data
You can also add custom dimensions and custom metrics to Google Analytics via dimension widening. The process is almost exactly the same. The only difference is that you must first define your custom dimensions or metrics in the Google Analytics admin section.
There’s not a lot of configuration here. Just give your dimension a name and choose a scope.
NOTE: You can only widen between dimensions and metrics of same scope. For example, you can’t widen from user scope Key to Hit scope dimensions. Check out this (somewhat old) article on Custom Variables to learn more about scope.
That’s it. Now you can choose these custom dimensions (or metrics) when you add your schema for Dimension Widening.
Then create your CSV file with the correct headers and upload your data.
Note: Custom Dimension and metrics are only available in Google Analytics customizations – this includes custom reports, custom segments and dashboards. They can also be used in certain analysis tools, like secondary dimensions.
An Example: Uploading simple publisher data
Let’s say I’m a publisher. I want to add the publication year, author for each article. My key to join my data with GA data is the URL of each page. I already defined two custom dimensions, one for page publication year and one for page author.
I’m going to define my data schema in Google Analytics.
Now I build my CSV file using the correct headers for my key and dimensions that I would like to widen.
Next I upload my file…
And finally, I have data in my custom dimensions. Here I can see the data in a Custom Report.
Best Practices for Managing CSV files
You might want to widen your data based on multiple keys. For example, you might want to widen your product data (using the product ID as a key) and your campaign data (using campaign name as a key).
In this case you’ll need to define two different schemas and upload two different CSV files. Make sure you name them something logical!
Another thing to consider is when to update your CSV files.
For example, let’s say that you’re a publisher, and you’re uploading new data about your content. But you’re publishing new content every day. And probably multiple times a day. You would need to upload a new CSV file every time you publish content. This is too manual. In case you probably want to consider a programmatic solution.
Use the CSV file for things that do not change often. Use the API for things that change a lot!
You could do something fancy, like add the data to a data layer, then pull it into some custom dimensions. No problem!
The point is that you don’t always have the time or the IT resource to implement the data collection. Even if you use a cool technology like tag management, it may be that the data you want to add comes from an isolated system. And that it would take too much effort to transport the data from it’s home all the way to the web server.
Dimension widening can be seen as a somewhat faster, less IT intensive way of joining your data together.
Things to be aware of…
Ok, a few things that you need to be aware of when using Dimension widening.
1. Your data is NOT applied to historical data. Your data is only applied going forward.
2. You can NOT widen on ALL dimensions. You can NOT widen on the following dimensions:
- custom variables
- product dimensions and metrics
- campaign dimensions
- time-based dimensions (hour, minute, etc)
- geo-dimensions (country, city, etc)
3. If you would like to expand your dimensions and populate Custom Dimensions you MUST use Universal Analytics. The reason is that Custom Dimensions only exist in Universal Analytics. They do not exist in the previous version of Google Analytics.
4. You can not change a schema once it has been entered into Google Analytics. You must delete the schema and then define your new schema.
I know some of these caveats may seem limiting, but remember, this is just the initial version. I know the team is working hard to expand the functionality.
Do you think you will use Dimension widening? If so how? Feel free to share your examples below!
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