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Publisher's Price Floors in First-price Auctions

Google's move to the first-price auction will have a major impact on the industry including how publishers price their impressions and use price floors
Most of top-tier SSPs have already adopted the first-price auction as a primary model to transact on programmatic impressions. However, Google has just announced its upcoming switch to the new auction model in Google AdExchange. This move will reduce market inefficiencies associated with multiple second-price and first-price auctions running for the same impression:
Higher bidders sometimes lose because they participate in the first auction instead of the final one. For example, a buyer with a $7 bid competing against a $3 bid will clear at $3.01, which seems great until that buyer loses to a $4 bid in Google's exchange bidding auction. In the future, all three bids will compete at the same time, which will allow the highest bidder to win.

What's different in first-price auctions?

Google's change will have a major impact on the industry including how publishers price their impressions and use price floors.

In the second-price auction environment, price floors are intended to find the sweet spot between the winning bid and the second-highest bid to increase the closing price and, therefore, overall revenue for the seller. This strategy also allowed to pass higher bids to an ad server and optimize transactions for impressions that are sold in multiple auctions with different mechanics.

With all SSPs and Exchanges switching to the first-price auction, there is no need to minimize the gap between two highest bids as buyers will pay exactly the price they bid. Publishers will need to reevaluate their pricing strategy and change the way they use price floors.
Many industry observers argue the market will become more straightforward and predictable with first-price auctions as buyers acquire impressions by simply paying the price of their bid.

However, the first-price auction model isn't without its flaws. The main difference for buyers in the first-price auction in comparison to the second-price is that they do not know the second-highest bid. Therefore, they can't be sure they are not overpaying for an impression. Natural market tendencies would encourage advertisers to try and bid as little as possible and still get the impression. They would employ aggressive bid strategies to try and win with a bid that's ultimately only $0.01 higher than their estimation of the next advertiser's bid.

Auction 1
Buyer 1 wins the auction and pays $5. However, the buyer has a dilemma now: what if they could bid lower and still win the impression. So in the next auction, this buyer bids $0.5 lower to identify the competition for similar impressions.
Auction 2
So Buyer 1 still wins even with a lower bid. In the next auctions, they will try to lower their bids further until they stop winning impressions. A publisher could get $5 CPM but overtime the CPM goes down as buyers are constantly trying to reduce prices.

Bid shading

The buy side applies sophisticated automated algorithms to test bids and find optimal CPM/Win Rate ratio. Similar buyer's CPM-reducing techniques are called Bid Shading. These algorithms use historic closing prices and Win Rate (% of auctions won) to come up with the next bid.

Price floors protect publisher's inventory value over time

In the first-price auction environment, price floor optimization doesn't allow buy-side algorithms to lower your CPMs over time, not in a single auction.

If you analyzed historic closing prices and set a $4.9 floor for both auctions in the example above, Buyer 1 would have lost the second auction with $4.5 bid. As a result, the buy-side algorithm would bid higher in the next auction to improve its Win Rate.

A floor price can function as an extra, aggressive bidder in the auction that prevents buyers from lowering their bids too far. But when price floors are set too high, publishers lose out on revenue if other bidders drop out of the auction.
Similar to buyers, publishers need to adopt automatic pricing technologies such as Roxot Revenue Lift to protect their inventory value from bid shading and other CPM-reducing techniques.

For instance, Roxot uses machine learning and historic CPMs for different audiences across the entire market to find optimal prices for every auction. These prices balance high CPMs and Fill Rate.

Price floor limitations in the first-price auctions

The main limitation the first-price auction mechanics introduced is the inability to a/b test different pricing strategies in the short term.

"When you randomly alter the bid floors, and buyers aren't aware of it, their behavior won't adapt in the short term," Shengwu Li said.

Publishers may overcome this shortcoming by building their pricing strategy on market-wide bid data. To protect themselves from buyers shifting their budgets to other websites, publishers should analyze how advertisers buy similar audiences on other properties.

This can be achieved by partnering with leading dynamic pricing companies like Roxot who know how buyers value different audiences across the entire market. This information helps publishers to balance high CPMs and Fill Rate and make sure they are selling their inventory for the highest prices without risking losing buyers in the long term.