The Data Standardization Problem within Digital Advertising.


The world of digital advertising is evolving exponentially.

The abundance of digital advertising options means steep competition between platforms. Every option marketers have on where and how to place ads means an opportunity for a singular platform to try to win (and keep) our business. 

Because every advertising platform operates individually, each is fighting to own and maintain the bulk of your advertising budget. To differentiate themselves, each creates different tactics for hosting and distributing ads, different names for each of their measurement points, different ways of calculating those metrics. 

Understanding how each platform operates, speaks, and reports takes time. Looking at it in a silo makes sense, but looking at the big picture becomes confusing … and it’s meant to be. The different platforms want you to learn their way of advertising, see your campaign success without ad data standardization, and stick with them—because learning this process for every individual platform would be actual hell. 

But as nightmarish as learning each platform individually sounds, the real deterrent from trying to incorporate more than one advertising platform into our campaign strategies is when we try to compare performance across channels … and again, this is done very purposefully

If Facebook measures video views one way and YouTube measures them in another way, how on earth are marketers to inherently know which is correct? If we don’t know which is “right,” how are we to know whether our ads are successful? 

We can put the two data points against each other but at the end of the day, it wouldn’t be an apples-to-apples comparison. So, we’re back to guessing which is better … which is the exact opposite point of having performance data.

We haven’t even talked about whether or not the two platforms are actually measuring what we, the marketing organization, want to measure. Regardless of how many different platforms we’re running ads on, they’re still measuring what THEY want us to measure. Sure, they probably offer insight into how different sections of our audience are consuming ads—but do those segments align with our own? Does YouTube or Facebook or Google really understand the intricate nuances of our product, brand, or messaging and help us report on each?


The overarching problem facing marketers is:


How do we maintain a clear, unbiased view of how ads are performing across the board? How do we bring the disparity into alignment when we reach our audiences using multiple advertising platforms?


The answer is to extract data out of their siloed environments and then somehow make them play nicely together. We need a way to standardize our data—cut out the inherent bias of all the platform-specific metrics—and then organize it in a way that makes sense to us, and our own goals.

This of course creates a host of new problems … because ohmygosh that is going to take FOREVER and be a HUGE hassle and holy heck I DON’T WANNA.


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And again, this is the whole point—the separate platforms WANT this process to be painful. They want you to stick your head in the sand and stay loyal to the one you know the best. 

But we’re smart, gritty marketers and goshdarnit we will not be deterred by fragmented advertising platforms and their lack of data alignment. So, we take a deep breath and pour a fifth cup of coffee and start the process of exporting fifteen sets of data that are not comparable and after several masterful pivot tables and an all-nighter, we might have a general understanding of whether our campaigns are working and how. 


Here’s the thing—this world of “advertising fragmentation by design” that marketers live in is only going to get worse. Smart marketers are already performing Frankenstein-esque surgery to get even a little closer to understanding the holistic performance of their ad campaigns … but even that isn’t foolproof (just ask the villagers of Ingolstadt). 

This monster of a reporting process is never ending and it takes a lot of manual labor, a lot of time, and is never going to be completely accurate because of human error and also, the data was never designed to be combined. In fact, it was very purposefully designed to NOT play nicely with the data points from other platforms. 

So, how do we rectify this annoying and stress-inducing problem that we all seem to have? Well, in a “damn the man—save the empire” movement we could all invest in an Advertising Intelligence software to standardize our advertising data and then organize it in a way that makes sense to our own needs. Or, we can continue to wrestle pivot tables into submission and hope that we’re making the right decisions based on disparate data from platforms with their own agendas.

The choice is yours, but our money is on Advertising Intelligence.