The data tools your company uses take the raw data and process it for analysis in reports and query interfaces. If you do not understand how these analytics systems work, you should not be able to confidently trust the insights they provide.
Whether you use a tool like Google Analytics, Adobe Analytics, or Visual Website Optimizer, you should have a decent understanding of how data is processed from collection through processing all the way to the reports you look at in these tools’ user interfaces.
As a technical marketer, you need to be able to understand the idiosyncratic quirks of each analytics system. You need to be able to trace the lineage of a graph in the report all the way back to how it was collected. Otherwise, it will be difficult to trust anything that you see in the dashboards.
Consider this…
You’ve deployed tags for Google Analytics and for Facebook Ads on your online retail store website.
The tags collect data about user behavior. Sometimes they send platform-specific identifiers to the vendors. For example, Google Analytics collects identifiers that allow it to group the user’s hits together in the interface. Facebook collects identifiers that help correlate a visit with a Facebook ad click.
But when you compare the data across these two systems, you find that they report the same things differently. And sometimes even with different counts and aggregations!
Why does Google Analytics claim that you had 20 conversions from Facebook yesterday, but Facebook Ads reports say that you had 30?
Why does your own, custom-built analytics data store say that your website generated $30,000 in revenue yesterday, but Google Analytics only reports $10,000?
Which one should you trust? Which is the best representation of reality?
Ignorance is not bliss
It would be easy to shrug these differences off and just say that “Hey, they’re different platforms – of course they measure things differently!”.
But that might not satisfy your stakeholders who want to know which reports are the ones that best reflect reality. Furthermore, you are throwing money at these vendors – it would be unwise to keep spending if you don’t know what the return of that investment is.
The fact is that analytics systems are very complex things. They comprise multiple parts, and each part might work differently from corresponding parts in other, similar software.
The irony is that the bulk of the data that these analytics systems collect is almost identical regardless of the system in use. It might just be shaped differently.
For example, when the user buys something in your store, this activity could be called a “transaction”, an “order”, a “purchase”, or even a “conversion”, depending on the system in use. But they all measure the same thing!
The better you understand how analytics systems work, the easier it will be to uncover the idiosyncrasies of each unique tool that you work with.
Once you have a handle of how these tools differ, it will be much easier to look at all of your marketing data in unison.
There’s nothing more satisfying to a technical marketer than working with a data warehouse that has a clearly defined schema for marketing data across all of your marketing channels.