The winners and losers of content recommendation’s data diet
WhizzCo’s Bill Nolte writes on WARC:
The lack of auctions in content recommendation means that publishers are denied the ad performance data they normally receive via programmatic exchanges.
For example, with programmatic advertising, if a publisher blocks Ford and GM to run ads from Toyota exclusively, they can still see how much Ford and GM bid on those impressions via the exchange. This enables them to calculate the real cost of running Toyota exclusively. This data can also be used to determine if, and when it’s time to terminate such an exclusive deal with Toyota and accept ads from other automotive manufacturers.
It’s the transparency inherent in the programmatic auction process that provides participants with a truer value exchange. An advertiser losing a bid understands that they will need to bid more in the future. For every bid won, that advertiser can calculate with some proximity the incremental value of that ad to understand how much should be bid in future auctions. The publisher also understands the value of each user across each content section, based on the amount, quantity, and source of bidders. This data gives publishers greater control to make more intelligent decisions, regarding advertising as well as editorial matters.
So, who’s benefitting from the publisher’s lack of data, transparency, and control?
Content-recommendation advertisers. These advertisers know exactly how many cents to bid for each ad to earn a desirable return on investment. And they benefit from the publisher’s lack of knowledge regarding the opportunity cost of running their ads instead of others.
The increasing importance of content to advertisers provides publishers with an opportunity to take back control over content-recommendation advertising. First, they can make quality ads and content a priority, rejecting lower-quality advertisers while working with the vendors and advertisers to ensure that they only accept advertisers that meet their guidelines, as they do with display, video, etc. Second, they need to demand greater transparency from their content recommendations vendors regarding bid volume and pricing to empower making smarter advertising and editorial decisions based on more complete data.