In recent years, the concept of ad verification virtually exploded onto the scene and became a fixture of digital ad management and media transactions. The reason for verification's growth was simple: Ever since the first banner ad was sold in 1994, advertisers have sought increasingly more sophisticated ways to verify that publishers are holding up their end of the deal. The first step, with the advent of the third-party ad server, was to establish an independent source of record -- initially for counting impressions and clicks, and later for measuring campaign performance against metrics like clicks and conversions.
Verification marked an evolution on this front by introducing an element of quality control. Beyond a simple proof of delivery for their ads, advertisers now expect to know how their ads were delivered: Were they viewable? Were they in a brand safe environment? Was their delivery subject to fraud? This type of insight has been an important and necessary component for buyers to evaluate the success of their campaigns.
But this kind of verification, today offered all across the industry by publishers, platforms, and point solutions alike, is also inherently limiting. It serves as an audit, yes. But in so doing, it assumes a decidedly negative orientation: Did the publisher do something they shouldn't have? The outcome of independent verification analysis today is largely to shift media spend away from violators.
This narrow use for verification, unfortunately, has been an epic missed opportunity. The right combination of data should help buyers discover which qualitative elements actually drive better performance, so they can then optimize spend and targeting based on those insights. This is especially true for advertisers leveraging technology capable of analyzing contextual relevance at the page level, across more granular subject areas.
We're now entering that next frontier. More than simply a delivery report against numerous verification data points, the most sophisticated vendors are now able to slice this data by performance, giving advertisers the ability to actually measure return on ad spend for different categories. For example, imagine you're a financial advertiser buying exchange inventory based on audience data -- such as people who searched for annuities. You might not be surprised if, in your verification report, you discover that pages with a financial context actually had much higher CTRs and conversion rates than your campaign benchmark. But what if you also learned that, within the finance vertical, pages with a "retirement planning" context outperformed pages with an "investing" context, 2:1? I'll bet you'd start targeting retirement planning pages on your next programmatic buy, and that if you did, you would see an exponential boost in your return on ad spend.
This concept has already been adopted in the realm of attribution, where key performance indicators (KPIs) are evaluated according to users' exposure to a particular media mix, with the ultimate goal of effectively distributing spend across channels. Substitute media channel with media relevance and that is effectively the next opportunity for verification. When we look at KPIs against targetable elements like contextual category, page structure, mobile app popularity, or a host of other impression-level qualitative attributes, we can let the data tell a powerful story -- where media relevance is yet another lens to evaluate and optimize performance.
Considered this way, the possibilities are endless. Advertisers might discover that certain campaigns do better at certain times of day, on certain devices, even under certain weather conditions. Performance may vary by video player type, viewability threshold, or the number of competing ads on a page. Verification should be much more than an audit. In order to capture its value, advertisers need to start thinking about it more broadly.