This article is based on our brand spankin’ new e-book, “How to Solve the Metric Standardization Problem in Digital Advertising.” Want to put the pieces of your ad data puzzle together to create a beautiful picture of your cross-channel performance metrics? You should get your copy.
On Literally No One’s Holiday Wish List: More Confusing Ad Data
A Lumenad employee recently spotted a 15,000 piece jigsaw puzzle in a toy store and stood in front of the shelf, wondering: a) How big would your dining room table have to be to fit this thing? b) Would it ever get finished? c) How many pieces would get lost over the painstaking process of assembling it?
Today, the average advertiser must extract data from up to seven different data sources to get a clear picture of their advertising investment. And if you’re a digital marketer running campaigns for multiple clients, the amount of data you must extract and piece together is absolutely enormous.
4 client accounts X 7 data sources = 28 reports with raw, biased ad platform metrics
With ad budgets increasing (they rose by 45% from Q1 to Q2 2021), the flow of data isn’t going to recede any time soon. Brands with big budgets can afford to expand their channel strategies to capture more of their audience. From paid social and programmatic, to connected TV and display, the options for online advertising seem endless. And with each of these, the amount of fragmented data available to advertisers will exponentially rise.
Media Fragmentation: Taking a Jigsaw to Your Ad Campaign Performance Metrics
Media fragmentation can represent a frustrating problem for both advertisers and consumers. For advertisers, media fragmentation can make it difficult to find the right audience. According to Deloitte’s 15th annual digital media trends survey, released in February 2021, “consumers, now presented with endless choices, are smitten with varied content and flexible pricing options, creating persistent subscriber churn.”
Audiences constantly tempted by the next shiny new object aren’t stable, creating difficulties for advertisers. VP and GM of Digital Media at Merkle Inc, Megan Pagliuca agrees: “A consumer isn’t just interacting with one channel. They’re watching TV, they’re on Facebook, they’re searching. As we try to understand that challenge, the infrastructure around the media mix is primary. In other words, how do you get to the point where there’s one measurement number you can use across all channels to understand the consumer.” Going back to our puzzle analogy, we can ask:
“How do we transform all of that data into pieces that fit, make sense together, and create a complete picture of campaign performance?”
Interestingly, all the shiny new media objects aren’t necessarily making audiences happier either. TechCrunch commented on the effect of fragmentation on consumers: “The growing quantity and fragmentation of platforms are becoming more frustrating for users to manage.” From social media platforms to endless entertainment offerings like Netflix, Hulu, Disney+, and others, consumers are racking up platforms, losing track of their subscriptions, and wondering about the safety of their data.
Where does this leave advertisers? The only way to truly counteract fickle, frustrated audiences and optimize use of the advertising options available, is getting cross-channel advertising campaign analysis right. And that means transforming your ad campaign performance metrics.
WTF is Ad Campaign Data Transformation?
You might use words like blending, cleaning, normalizing, data modeling, structural manipulation, or merging to describe the painstaking task of using manual methods to make ad platform metrics speak the same language. No matter what you call it, it presents the same headache: trying to derive real insights (ones that accurately match up with your clients’ business goals) from inherently biased metrics created to make ad platforms look good.
Some of the biggest offenders? Adtech giants Facebook and Google bring in multi-billion dollar ad revenues each year. These walled data gardens keep a tight hold on their technology, information, and user data. They also label and define their ad campaign performance metrics differently, making it difficult for advertisers to truly evaluate cross-channel campaigns.
But they’re not the only ones who play this game with their metrics. Independent adtech companies, which include demand side platforms (DSPs) like The Trade Desk and Media Math, also have non-standard performance metrics. So if you’re running campaigns across paid social and programmatic platforms, you’re likely spending more time downloading, cleaning, and attempting to merge your data than you are actually analyzing it.
As any digital marketer knows, the technology landscape is rapidly evolving. Yes, that means more media fragmentation and more disparate data. But it also means more innovation in solutions to these problems. Which leads to the questions: Do I really have to continue to manually blend my ad campaign performance metrics? Is there a solution for that?
Whereas blending, merging, standardizing, etc. are challenging even for data professionals, transformation is automated. It makes the process of creating apples-to-apples metric comparisons accessible across agencies and brands, and provides a precise framework for delivering real insights and optimizations to stakeholders.
Beyond metric standardization, transformation also blends your campaign architecture. By deconstructing the hierarchical data to its most granular form, the data is freed to be normalized and organized however it makes sense to the advertiser’s unique business goals. If a digital marketer wants to do this herself, she would have to download and store data every single day, resulting in thousands and thousands of lines of data – an unmanageable puzzle of data points that aren’t made to form a bigger picture.
Puzzles can be fun when they’re well-constructed and manageable. But the advertising industry has long since left behind manageable ad campaign performance metrics. As media fragmentation grows, and the hold of major ad platforms on proprietary data doesn’t lessen, advertisers must begin looking for solutions now.