Programmatic advertising continues to grow in popularity, and is the main media-buying channel for advertisers across the globe. As the mobile industry evolves, it’s not difficult to see why app marketers are looking to programmatic media as the solution. Mobile users have diversified interests and behavior, making it much more difficult for app advertisers to select, negotiate, buy and optimize media using traditional methods.
Programmatic media effectively speeds up the process by eliminating the negotiation between the buyers (advertisers) and sellers (publishers), saving both time and money. This allows app advertisers to set up rules for real-time bidding in a way that ensures the ad will reach the target users. Not only does automated media buying save app advertisers a lot of time, but it is also cost effective. All an app advertiser must do is to bid on the desired media inventory at the desired cost per mille (CPM). If that CPM is above the minimum price the publisher has set and is highest amongst other bidders, the app advertiser will win the auction.
In a programmatic environment, there are two different levels of price floors a publisher can set: hard and soft price floors.
Hard Price Floor (HPF)
Sellers could set a minimum price, also known as hard floor, in which they can tolerate for their media inventory in the ad exchanges. Bids that are below this minimum price are simply discarded. This means sellers will not take any bids less than the hard price floor, not even a cent less than the hard floor price.
Soft Price Floor (SPF)
Soft floor, on the other hand, is more of a threshold that sellers can set to capture what would otherwise be missed opportunities, especially when all buyers set bids that are only slightly less than the floor.
With soft and hard price floors in place, the programmatic media buying landscape can be divided into three different scenarios:
- If the highest bid is greater than the soft floor, a second-price auction takes place. The highest bidder wins the first slot. The second-highest bidder wins the second slot, and so on. However, with a second price auction, the highest bidder pays the price bid by the bidder below them e.g., the highest bidder pays the price bid by the second-highest bidder.
- If the highest bid is less than the soft price floor but greater than the hard price floor, a first-price auction takes place. In this scenario, the winners pay the exact price they’ve bid.
- If the highest bid is less than the hard price floor, then there is no deal. This means that publishers will not be selling their media inventory at the bid price.
For years, the second-price auction model has been the gold standard for auctioning mobile ad inventory. This auction model often encouraged aggressive bidding, given the likelihood that the successful advertiser would pay a lower price. While advertisers often benefited from this strategy, publishers were not able to maximize the value of their inventory and sell it for its true value to advertisers.
As the mobile industry evolved, more and more bidders began supporting first-price auctions. While much of web-based programmatic supply transitioned to first-price auctions more than four years ago, 2019 was the year when mobile in-app inventory moved to first-price.
Strategies for Programmatic Media Buyers
To bid at the optimal price, advertisers should find a way to detect hard and soft price floors and adjust bidding strategies accordingly. Machine learning algorithms can be utilized to predict hard price floors and avoid ineffective bidding. At the same time, the algorithm can be used to predict soft price floors to avoid overpaying for the media inventory.
If advertisers can predict the hard price floor, they can adjust the bid accordingly to make sure that the bid is not discarded for being too low. This way, they will not lose out on any impressions. On the other hand, if the advertisers know the soft price floor, they can adjust the bid accordingly to make sure they do not exceed the soft price floor and enter the first price auction.
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