For years, every marketing publication has been parroting the same message to us: Smart marketers make data-driven decisions.
But how intelligent are the decisions we’re making if the data isn’t reliable? With advertising data coming from multiple sources, measuring different performance points, and named completely different things—how do we compile it in a valid, organized way to make informed decisions about ongoing and future campaigns?
The good news is half the battle is recognizing where this disparity is coming from. Let’s take a look at three ways our digital advertising data is actually holding us back.
1. Paid advertising data are specific to the channel they originated from.
Wouldn’t it be nice if a “view” from Google was the same as a “view” from Facebook? Even though the labels are the same, the way these data points are calculated are completely different—so even if you try to combine or compare them, it’s not a true one-to-one relationship.
For example, Facebook and Google both provide a metric for video plays to completion. Facebook calls theirs a ThruPlay, while Google’s is labeled a TrueView. Unless you read the definitions of these metric labels, you might think they both track the same thing — the number of times a viewer watches your entire video. But you would be wrong. Here’s how the two platforms define these metrics:
- Facebook ThruPlay: The number of times your video was played to completion, or for at least 15 seconds.
- Google TrueView: A video ad in which a viewer watches 30 seconds of the video ad (or the duration if it is shorter than 30 seconds) or engages with your video, whichever comes first. Engagements include clicks to visit your website, call-to-action overlays (CTAs), cards, and companion banners.
So, if you’re running a video campaign across Facebook and Google, you’ve got some work to do to figure out how to make these two metrics speak the same language. Remember, this type of disparity is intentionally created from the separate ad platforms that don’t want us to use any platform other than their own. In order to truly compare the two, a common, standardized advertising language and measurement framework should be used.
2. None of the data are organized in a way that is meaningful to our team and goals.
You know your target audience and organization’s segmentation devices better than anyone else … including paid advertising platforms. Once you have campaign performance data, are you able to slice and dice it in a way that makes sense to your organization? Or, are you beholden to how the ad platform sees fit to display and report on performance? To make advertising data meaningful, digital advertisers need an ad data organization solution with three major, core capabilities. These include:
- Customizable data segmentation and labeling so you can evaluate performance and make informed decisions.
- Pixel event tracking that allows you to group post-click user activity from all of your platforms.
- No-code organization that uses friendly names, drag and drop functionality, batch processing, color coding, and label customization within one intuitive interface.
Remember, paid advertising platforms intentionally show you digital advertising performance metrics in the ways they prefer you interpret it, which may not align with your needs or goals.
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3. With all our advertising data siloed, illustrating success for stakeholders or clients is a pain.
Even if we get around the fact that the data sets are not standardized or organized in a meaningful way, there’s still the issue of actually illustrating success. While each separate platform might have an aesthetically pleasing way of viewing performance—none of that matters if our advertising campaigns stretch across multiple platforms. With each platform calculating, organizing, and now illustrating performance in a silo, marketers are forced to export each data set separately, and then create their own campaign performance data visualization. Not only is this an extremely time-consuming and difficult process, but due to the manual nature of the process, the opportunity for error is large.
In fact, according to industry analyst firm Gartner, data professionals spend more time prepping data to be analyzed than actually performing the analysis. Another study found that marketers spend 40% of their time gathering and cleaning data and only 11% finding insights and communicating them. And if you’re using spreadsheets to track all of your ad data, the potential for error is massive. According to a University of Hawaii study, 88% of all spreadsheets contain at least one error. If you find this data depressing, here’s a more affirmative stat: After just 90 days using Lumenad, one digital agency reduced reporting time by…we’re not making this percentage up… yep, you guessed it… 40%!
Now that we have an understanding of how our paid advertising data might actually be working against us, let’s go back to that first golden rule of marketing—make data-driven decisions. To do that, we can’t just inherently trust the data points put in front of us, we need to be advertising intelligent. As marketers, we need to be proactive in how we standardize, organize, and illustrate paid advertising data, so that our decisions aren’t based on data for the sake of data, but the right data as it makes sense to our stakeholders and goals.