Roxot blog
Publisers' inventory prices lag far behind the real demand OR why publishers need price floors
The global ad market is a complex system. With communication between publisher and advertisers full of middlemen and turnovers, vital information about supply and demand rarely reaches publishers...
Publishers are left guessing & blindly setting prices for their inventory
The global ad market is a complex system. With communication between publisher and advertisers full of middlemen and turnovers, vital information about supply and demand rarely reaches publishers. Without the right information, publishers aren't making informed decisions - decisions like what their inventory should cost, what ad exchanges to partner with, and what content to generate - decisions that lose revenue.
Header bidding, one of the most recent, frenzied trends in the industry, only partially solves the problem. Allowing multiple demand sources to bid on the same inventory means savvy publishers can increase competition and bid density for their inventory, driving up their website's revenue.
Header bidding also provides publishers with more data about ad auctions which can be used to optimize their revenue strategy. However, publishers still don't have the tools to effectively use this information. They still can't connect CPMs/RPMs with other relevant influences on demand for their inventory. Why exactly, is revenue this month down from last, up from last week, etc? What really drives our revenue? Without this information, publishers are left guessing and blindly setting prices for their inventory, often lagging far behind the real demand.
The global ad market is a complex system. With communication between publisher and advertisers full of middlemen and turnovers, vital information about supply and demand rarely reaches publishers. Without the right information, publishers aren't making informed decisions - decisions like what their inventory should cost, what ad exchanges to partner with, and what content to generate - decisions that lose revenue.
Header bidding, one of the most recent, frenzied trends in the industry, only partially solves the problem. Allowing multiple demand sources to bid on the same inventory means savvy publishers can increase competition and bid density for their inventory, driving up their website's revenue.
Header bidding also provides publishers with more data about ad auctions which can be used to optimize their revenue strategy. However, publishers still don't have the tools to effectively use this information. They still can't connect CPMs/RPMs with other relevant influences on demand for their inventory. Why exactly, is revenue this month down from last, up from last week, etc? What really drives our revenue? Without this information, publishers are left guessing and blindly setting prices for their inventory, often lagging far behind the real demand.
Publishers need not only data processing & collecting
Publishers need a solution that processes data about visitors, content, bids, ad placements, and networks/exchanges to understand what's getting the most revenue. These insights shed light on which content to write, what visitors to attract, how to set up placements, and what ad networks provide the most return. However, most of the information received from data processing isn't actionable.
First of all, certain variables are almost impossible to change. Knowing which browsers get you the most revenue is great, but difficult to incorporate into your monetization strategy. Besides paid advertising with advanced targeting, there are few ways to attract significant traffic from a particular browser to your website.
Secondly, ad auction parameters (browser, OS, country, time of the day, content type, ad placement and many more) influence final CPM in combination rather than separately. And it's easy to get overwhelmed when imagining the number of different possible parameter combinations.
In a nutshell, publishers need not only the data processing and collecting, but also the help of machines to automatically optimize ad auctions.
Publishers need a solution that processes data about visitors, content, bids, ad placements, and networks/exchanges to understand what's getting the most revenue. These insights shed light on which content to write, what visitors to attract, how to set up placements, and what ad networks provide the most return. However, most of the information received from data processing isn't actionable.
First of all, certain variables are almost impossible to change. Knowing which browsers get you the most revenue is great, but difficult to incorporate into your monetization strategy. Besides paid advertising with advanced targeting, there are few ways to attract significant traffic from a particular browser to your website.
Secondly, ad auction parameters (browser, OS, country, time of the day, content type, ad placement and many more) influence final CPM in combination rather than separately. And it's easy to get overwhelmed when imagining the number of different possible parameter combinations.
In a nutshell, publishers need not only the data processing and collecting, but also the help of machines to automatically optimize ad auctions.
Removing the guess work
Machine learning technologies will help publishers identify patterns to build large groups of user types and their mean CPM. Based on these user types, algorithms will be able to build hypotheses about what a particular impression should be priced in real time. Each and every visit to the website contributes to the process: the algorithm matches the user with one of the groups and uses that mean CPM to precisely set the price floor for each ad network/exchange participating in the auction. Thanks to header bidding, publishers won't risk their revenue when the machine learning algorithm operates from its own adaptor; the highest bid always wins.
Roxot stops lost revenue by providing publishers with the technology they need to protect their position on the global ad market and to make informed decisions. Our machine-learning algorithm analyzes incoming data about visitors, content, bids, ad placements, and networks/exchanges. It adapts to and understands what's hot in the market, providing actionable insights into what's getting the most revenue and automatically pricing each impression with dynamic price floors. Roxot removes the guess work and literally raises your bottom line.
Machine learning technologies will help publishers identify patterns to build large groups of user types and their mean CPM. Based on these user types, algorithms will be able to build hypotheses about what a particular impression should be priced in real time. Each and every visit to the website contributes to the process: the algorithm matches the user with one of the groups and uses that mean CPM to precisely set the price floor for each ad network/exchange participating in the auction. Thanks to header bidding, publishers won't risk their revenue when the machine learning algorithm operates from its own adaptor; the highest bid always wins.
Roxot stops lost revenue by providing publishers with the technology they need to protect their position on the global ad market and to make informed decisions. Our machine-learning algorithm analyzes incoming data about visitors, content, bids, ad placements, and networks/exchanges. It adapts to and understands what's hot in the market, providing actionable insights into what's getting the most revenue and automatically pricing each impression with dynamic price floors. Roxot removes the guess work and literally raises your bottom line.
