The definition of optimization is “an act, process, or methodology of making something (as a design, system, or decision) as fully perfect, functional, or effective as possible; specifically: the mathematical procedures (as finding the maximum of a function) involved in this,” according to Merriam-Webster. And a false positive is “relating to or being an individual or a test result that is erroneously classified in a positive category…because of imperfect testing methods or procedures.” By combining the two concepts, we paint a fairly accurate and frightening depiction as to what is happening today in the world of online display advertising.
Few dare to admit it, but in today’s world of advanced targeting technology, audience buying, advertising exchanges, real-time bidding, machine learning algorithms, and the like, the positive is generally false and therefore the industry is simply throwing smarter money away. How so? Too often the key performance indicator of an online campaign is some form of view-through conversion. Simply put, view-through conversion gives credit to the party responsible for showing the “last ad” prior to an online conversion. While the current advertising technology has brought about some remarkable advances in the ability to accurately target an audience, the advances have also enabled the exploitation of the “last ad wins” model.
If all that matters to a buyer is the fact that you served the last ad on a consumer’s machine before conversion, the buyer shouldn’t care if the ad is served above the fold, below the fold, to the left, right, or center of the page. The buyer also wouldn’t care if the page is brand safe, brand appropriate, contextually relevant to the brand or the consumer audience, or if there is more than one ad or competing brand on the page. Even more appalling is the fact that in this model the buyer shouldn’t care if the ad is served 1,000 times to the same user (notice the use of “served” and not “seen”), or that the user is retargeted 1,000 times, or ended up visiting your site as a result of some other paid media.
This could be the result of optimization – optimization to a false positive is more easily achieved now than ever. Imagine if one treated his personal financial investments the same way – it would be akin to sinking 100 percent of your investment portfolio in a Ponzi scheme and continuing to make deposits even when the scam has been exposed.
If we aim to improve the quality and effectiveness of online display advertising, we need to accept that there currently exists no perfect measurement. We have to be bold enough to move away from the detrimental “view through” or “last ad” model, or what I call the “false positive.” Given the current global economic and privacy environment, it’s of immense importance to optimize the distribution of each ad impression to inventory to make a true marketing impact. We constantly struggle to define true marketing impact and therefore struggle to map a process of refining the delivery of the performing advertising. Perhaps we should pay less attention to the determinant measure and apply more common sense to the act of advertising itself.
Logic dictates that the key to successfully investing in advertising in an oversupplied marketplace is to harness data that is unique to the advertiser or process information that is unknown to competitive buyers, and combine it with the relevance of the Web property where the ad is placed. Consider this pre-optimization, the act of more accurately evaluating where and how advertising will appear, along with its total reach and frequency ahead of purchase. Perhaps the new measure of effectiveness should be old measures such as reach and frequency.
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