Each ad network has their own specific way of doing things. From measurement to setup process.
For example, we all know LinkedIn adds campaign groups as a top layer, rather than having ad groups. And how could I not mention that Facebook's 'ad groups' are actually labeled 'ad sets'.
Each network has their own custom ways to do things, and it's no different with their set of metrics.
Normalizing or blending data is taking data from multiple sources, and combining it in a way that can be compared apples to apples. Essentially, the metrics are pretty much the same, each source just labels them something different, so ‘normalizing’ takes those metrics, sifts and sorts them, and combines them into a single metric.
Here's an example...
Normalizing ad spend example
When compiling data across networks, you'll find some metrics that include the same data have different names. Ad spend is a great example. Here's what the different networks call spend:
Facebook Ads = Amount Spent
Google Ads = Cost
LinkedIn = Spent
If you export those different metrics from each network, you'll need to normalize the data to one general 'spend' column to be able to pivot and make charts out of your cross-network data. You can label the normalized metric whatever you'd like, but in this example I've used 'spend'.
Normalized metric: Spend
Once you've done that, you've normalized your cross-channel spend data. Viola! Data analyst here you come.
How we normalize at AdStage
Here at AdStage we partner with the different search and social channels to build a central hub for your ad data. A major part of this process is blending the data so you can view your cross-channel data all in one view, all in one widget.
Continuing with the example above, AdStage compiles all of the different spend KPIs per channel into one column called 'Spend'. You won't see 'amount spent' for Facebook, nor 'cost' for Google, just Spend for any network. Take a look:
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