Algorithmic Unified Pricing Rules (AUPR) optimization

Problem

In the rapidly developing field of online advertising, it is becoming increasingly difficult for publishers to gain maximum revenue from their ad inventory. Especially since the introduction of the first-price auction model by Google, there have been very few providers of efficient pricing strategy and even those often act as a black-box. They do not reveal the real market value of the ad inventory, nor the factors driving the price. This results in either undercutting the price followed by potential reputational damage due to lower ad quality, or overshooting the price followed by unfulfilled KPIs and a loss in potential revenue.

How to set an optimal floor price to maximize revenues from programmatic ad auctions? What is the real value of a publisher’s ad inventory?

Solution

The machine learning-based solution maximizes revenues from programmatic ad auctions enabled by an optimally established pricing strategy. This unique, cutting-edge solution automates UPR management and optimizes floor prices on the ad-unit level.

Initially, by leveraging data from hundreds of publishers, an optimal pricing strategy is designed using AI. This uncovers the real market value of the ad inventory. Subsequently, the solution constantly monitors all the relevant price-affecting variables and automatically adjusts the prices of ad units to get the most from each user impression. This is accompanied by weekly reports that measure the effectiveness of the pricing strategy to assure the ad inventory is utilized to its full potential.

Benefits​

• Average revenue uplift of 21%
• Save time and resources devoted to UPR management 
• Measure the effectiveness of the pricing strategy via weekly reports
• Up-to-date UPRs – continuously readjusted based on price-affecting factors, e.g. traffic, “viewability,” competitors, etc.
• Inventory protection against bid-shading devaluation