How publishers sell ad inventory

By Eric Picard (Originally published on iMediaConnection, August 09, 2012)

Ad inventory is typically broken down into four buckets: sponsorships, premium guaranteed, audience targeted, and remnant. Each of these buckets can be sold through a variety of sales channels.

Revenue distribution across this “layer-cake” inventory model flows downward — with the vast majority of inventory coming from premium and a significantly lower amount of revenue coming from the remainder:

The process of an advertising sale begins with the media buyer, who sends a request for proposal (RFP) document to numerous publishers. These RFPs typically are written in prose and define the overall goals of the advertiser in question, and of the specific campaign being executed. A typical RFP has between 50 and 100 elements that are laid before the publisher as acceptable or desirable outcomes, and these elements (attributes or attributes of the buy) are generally descriptors of the audience, of the media the advertiser is looking to run on, of the acceptable (and unacceptable) content to be associated with, etc.

Advertising inventory is the base unit sold by a publisher to an advertiser. It is measured in “impressions,” which are defined as an opportunity to show an advertisement to a person. Impressions at their most basic are blank vessels made up of opportunity. Inventory is generally defined in advance by the seller based on a variety of factors, and it is these predefined impressions that are contractually agreed up on between buyer and seller.

Nearly all impressions sold are made up initially of one or two media attributes based on content association (e.g., MSN>Entertainment or MSN>Entertainment>Celebrities; Yahoo>Autos, or Yahoo>Autos>News). Or they’re sold just based on category — in some cases blind, meaning without the knowledge of which publisher the impression ran on. Further refinement of the inventory is based on other attributes such as above the fold, rich media units, or a variety of quality scores. Additional media attributes included in the definition of a piece of sold inventory include various types of targeting and other types of intelligence and filtering such as inventory quality scores and contextual targeting.

Beyond media attributes, there are numerous audience-based targeting attributes available for the buyer to request, or for the seller to offer. These include such attributes as geographic, demographic, psychographic, behavioral, etc.

It is the combination of these various attributes that define the inventory that is sold. Inventory is sold in a number of ways, including on a guaranteed basis (a buyer contracts with a seller for a fixed volume of inventory between specific dates) and on a non-guaranteed basis (if inventory is available that matches, it will be sold, but the seller doesn’t make any guarantees on volume).

In order to predict how much inventory will be available, publisher ad platforms need to look at historical data with seasonality and apply some very sophisticated algorithms to make a guess as to how much inventory will be available during specific date ranges. These “avails,” as they are called, become the basis for how all guaranteed ad sales are done.

But ad inventory has many very complex and difficult-to-predict issues that are endemic to the problem — the problem of predicting how many impressions will exist in a specific month is sort of like imagining how many cars will cross the Golden Gate Bridge in a given week. Predicting this based on historical data isn’t too hard. And predicting the color of the various cars that might cross the bridge is probably feasible with some degree of accuracy. Maybe even predicting the general destinations of the cars crossing the bridge is possible. But trying to predict how many red Toyotas driven by women with an infant in the car who have red hair and who make more than $125,000 annually is probably not a solvable problem.

This is akin to the requests given on a daily basis regarding ad targeting. This type of prediction is extremely technically challenging; nobody has been able to accurately predict how much ad inventory will be available in advance for more than three to four targeting attributes in advance. Therefore, publishers rarely will sell inventory that contains more than three to four attributes because this causes an immense amount of work during the live ad campaign for the publisher’s ad operations team. (They must monitor ad delivery carefully and adjust numerous settings in order to ensure delivery of the campaign.)

Inventory is sold within a contract called an insertion order (I/O), and each sold element is typically called a “line item” on the I/O. Line items correspond to a variety of attributes within the publisher’s inventory management systems. A simple example would be MSN>Entertainment. But a more complex example would be MSN>Entertainment>Women>18-34.

Beyond a typical guaranteed media buy, there are several other mechanisms for selling ads. Some ads are re-sold by a third party such as an ad network (examples include Collective Media, ValueClick,, etc.). Some ads are sold through an automated channel such as a supply-side platform, or SSP (examples include Rubicon, Admeld, PubMatic, etc.). There are also ad exchanges that can sit in the middle of all the transactions, and as the industry has matured, the difference between an exchange and an SSP has become less clear. These exchanges and SSPs then create a marketplace that allows ad networks and various demand-side platforms (DSPs) to compete for the inventory in real time. We’ll refer to this as real-time bidding (RTB) even though in some cases this term doesn’t apply exactly.

The management systems for buying RTB inventory are generally called demand-side platforms (DSPs). In RTB media buys, it is extremely rare to have more than three to four targeting attributes (just like in guaranteed media buys), not because of prediction but because inventory that exists for each campaign or line item that contains more than three to four attributes delivers with extremely low volume. In fact, the amount of inventory available on a per-impression basis as you layer on more targeting attributes generally drops significantly with each new attribute.  This means that a typical line item for an RTB campaign would look very much like the one for a guaranteed buy: Entertainment>Women>18-34.

For a DSP to spend an entire media buy at more than four targeting attributes, the buyer would have to manually create hundreds or thousands of ad campaigns that each would then be manually optimized and managed. It isn’t actually feasible to do this at scale manually.

In summary
In a perfect world, advertisers would be able to find all available ad inventory that matches their goals, with as many attributes as exist on all impressions. The problem is that existing inventory management and ad serving systems are not designed to deal well with more than two to three concurrent targeting attributes, whether for guaranteed media buys or RTB.

So why do advertisers and publishers prefer to sell ads on a guaranteed basis?

Inventory guarantees serve several purposes. The most critical is predictability; media buyers have agreed with the advertiser on a set advertising budget to be spent on a monthly basis throughout the year. They are contractually obligated to spend that budget, and it is one of their primary key performance indicators. Publishers like to have revenue predictability as well, which is solved by selling a guarantee on volumes for a fixed budget.

For all the innovation in the ad-tech space over the last decade, it’s fairly impressive that very few of the core problems of a publisher have been solved. At the end of the day, 60-80 percent of the revenue that publishers bring in comes from their premium inventory, sold on a guaranteed basis — which represents generally less than half of all their available inventory. Nearly all the ad technology innovation in the last decade has focused on what to do with that other half in order to raise the median price of that revenue from nearly zero to a bit more than zero.

It seems to me that there is an opportunity to focus on something else. (And you might imagine that I’m doing just that.)


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