What Makes Dynamic Administration Settings Synchronization a Big Deal?
In short, they make your life easier and better. Manually configuring variables in analytics tools should be a thing of the past and here’s why:
Dynamic Administration Settings Synchronization
Common Challenges to Manually Configuring Variables
The administration console is cumbersome. Most digital analytics tools store data in specific variables. These variables are often named in a way to tell end-users what data they contain. For example, Adobe Analytics captures data in Success Events, eVars, and sProps (until customers move to CJA or AEP). Google has similar variables as do most other analytics tools. These variables have to be defined and each has specific settings associated with them. For example, if you want to use an eVar to capture the Product SKU, you might configure it like this:
If you have hundreds of variables, you need an interface to configure them. This interface is typically known as an administration console and is only viewable by analytics tool administrators.
The task is unglamorous. For most analytics tools, adding and removing variables is a manual, time-consuming process.
It is critical that variable settings are correct. Since variable settings can have big impacts on data collection and attribution, the configuration of these variables needs to be accurate. For example, if you are an Adobe Analytics customer and use the incorrect variable type or expiration window, it can have a direct and adverse effect on your digital analytics data.
How Dynamic Administration Settings Synchronization
Works in Apollo
Apollo excels at reducing manual work. Since it contains all of the information needed to create or remove tool variables, it can automate the process to quickly and easily keep your analytics tool updated. Apollo knows which variables need to be added or removed based upon the currently selected business requirements.
In addition, the pre-built analytics solution designs inform Apollo of the specific variable settings that are required to match the best-practice solution. This means that your analytics team doesn’t have to manually configure variables or worry about whether they know the correct variable settings. To see this in action, let’s assume that we want to add a new business requirement to our implementation around tracking website visitors by a User ID. First, we select the business requirement.
Next, we tell Apollo which analytics tool variable we want to use for the User ID data we collect.
Of course, this will then dynamically update the data layer and tagging specifications (and tag management configuration).
But in order for analytics tool users to see the data, we need to update the variable name and settings in our analytics tool. To do this, we simply re-deploy and let Apollo do the rest (in this example we are using Adobe Analytics so Apollo will update the variables in the admin console):
In a few seconds, all required variables are configured in the analytics tool. These variables match the exact variable numbers specified in the Analytics Management System, which is critical since those variable numbers are also used in the tag management configuration. The analytics tool variables also have the correct settings needed to match the solution design. For example, you may notice that the User ID eVar is set to expire “Never.” That is based upon a best-practice in Adobe Analytics (to capture the User ID from a previous session so it is known even when a user returns, but isn’t logged-in).
What This Means for Your Business
Simplicity
The automatic configuration of analytics tool variables keeps all of the moving parts—from analytics tool variables to data layers to tag management configurations—in sync. Having all of this information in one unified platform with automation capabilities to make updates can be a huge time saver!
Business impact
This expedites the implementation process and helps business stakeholders get to their data quicker.