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As more impressions become available on ad exchanges, media buyers are taking advantage of the additional available inventory. However, some of the inventory available is of very low quality and often below the fold or loaded at the bottom of a webpage. If the user doesn’t scroll down far enough to see it, that impression will not be seen, but will still count as an impression for purposes of the advertiser’s bill.

Advertisers are looking for tools and methods to optimize media buys to maximize the impact of media investments, while taking into account viewability data. For that purpose, many advertisers leverage various marketing attribution methods and technologies. When using last-touch attribution methods, one assigns 100% of the credit of a conversion to the last impression served to the user, even if the impression was not viewable. This attribution method is commonly used for display by advertisers using 3rd-party ad servers. However, a not-viewed impression by definition has had zero effect on a consumer’s behavior and therefore should be assigned zero credit for his or her conversion. Even in simple last-touch attribution methods, only viewable impressions should be assigned credit for the last touch, yet the ad server is not able to determine the viewability of all impressions.

More sophisticated, MTA methods are designed to measure the impact of marketing touchpoints on consumer behavior. Good attribution models are designed to determine how much more likely a consumer is to convert after he or she is exposed to a particular impression. Since consumers are exposed to multiple touch points across their journeys, marketers want to understand how each point influences their purchase. Knowing the touchpoints’ relative importance enables marketers to allocate resources properly across the different media channels.

Matt Scharf at Adobe and others have argued in various articles that marketing attribution platforms need to exclude outof-view ads so that credit is not assigned to non-viewable impressions and marketers and their media partners are incentivized to target higher-quality inventory. The challenge is that not all impressions or placements are measurable for viewability, so it is not currently possible to determine viewability for each impression and exclude non-viewable ads from attribution. However, it is possible to estimate the average viewability of a placement and to insert that information into multi-touch attribution models. At Conversion Logic, we have observed that placement viewability remains relatively stable over time.

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