Banner Ad: Please Fix Your Pacing Algorithm
Decorative Header Image

Using Proportional Control for Better Campaign Pacing

My post was originally published on the Rubicon Project blog. It was written with contributions from Dr. Neal Richter and Jonathan Zhuang.

Pacing algorithms come in a few basic forms at the Rubicon Project. The most basic is one called “as fast as possible” which can hardly be shown to do anything that resembles pacing. The Pacing controller is supposed to spread out impressions served for a campaign throughout the day. In general it takes the goal of impressions to serve in a given day as input and calculates a serving schedule for the campaign.

Estimated Curve of Available Impressions

A naive pacing algorithm will break the day up into 24 segments, let’s call them hours. It will allocate equal amounts of campaign impression for each hour and then recommend the campaign get impressions until the hour’s allocation is exhausted. This algorithm has a couple of pretty big flaws. Primarily it tends to serve the campaigns at the beginning of each hour and then once the allocated impressions are used up it stops serving until the next hour. The second flaw is that it has no understanding of the traffic distribution throughout the day. The 1AM hour doesn’t have the same amount of traffic coming in as the 10AM hour. If the inventory is relatively scarce the campaign will under-serve during the early hours, catch up during the peak hours, and then under-serve again in the later hours. Ultimately campaigns using this algorithm may not achieve their goals at the end of the day and it won’t serve evenly in a given hour. Read more

Frequency Cap Primer

This is the first part in a series on The Basics of Online Advertising.  I’ll be posting a new entry each week for the next four or five weeks.

What is Frequency Capping?

Frequency capping is the act of placing a restriction on an advertising campaign that mandates that are particular user only see an ad a fixed number of times over a given period. This usually takes the form of impressions/day/user (or impressions/hour/user). In an ad serving system this will show up in two ways:

  • Frequency Cap: X Impressions / Y Hours
  • Frequency Cap: X Impressions / Y Days

The X and Y in these settings are usually variables. The Y tends to have predefined drop downs in the interface like 12 hours, 24 hours, 36 hours or 1 day, 2 days, 3 days.

It is common to refer to frequency caps at one per day as the “tightest” cap. Increasing the frequency is referred to as “loosening” the frequency cap. These phrases are common in the industry.

Why choose to apply Frequency Capping? Read more

In a world without cookies

I’m hoping your mental audio kicked in with an interpretation of a movie trailer with a Don LaFontaine voiceover when you read the title. I wrote this post in response to a lot of articles written from a position of fear from the advertising industry at the prospect of web browsers shipping with 3rd party cookies disabled. Disclaimer: The opinions expressed here are my own and should not be construed as the opinions of my employer, associations or other groups I happen to belong to.

There’s a lot of highly visible worry in the news lately about online advertising losing the ability to set 3rd party cookies in a web browser. This technology is used to perform a variety of seemingly critical tasks: retargeting, audience targeting, frequency capping, user identification for RTB and probably a hundred other things – most of which I try not to know in detail.

The biggest concern seems to be that this growing part of the industry gets turned upside down if more browser companies decide to ship their products with 3rd party cookie disabled by default. Apple did this with their Safari browser which has been one component responsible for slowing down advertiser adoption of iOS devices. But advertisers have alternatives (like: display ads in other browsers, keywords, and online video ads) that they’re more comfortable with anyway, so there’s no telling how much of an impact the lack of 3rd party cookies on iPhones and iPads really has on the growth of the mobile ad revenue stream. Read more

What data does a DSP have access to when bidding on an ad exchange?

Ie, what types of information are contained within the cookies made available? Thanks!
This question was asked on Quora.com, below is my answer.

Identity DataIn a typical RTB transaction there’s a user ID, pulled from the user’s cookie or some form of server side system, which is passed to the DSP from the SSP. That ID is, in most cases, the DSPs record locator for the user’s information. Most DSPs have a server side data store where this information is housed, updated and augmented from a variety of sources including data companies like Blue Kai and Excelate and their ilk. DSPs may also be collecting and distilling information based on bid request activity from that user (although most SSPs put language into the contracts governing the use of this “bid stream” data) or retargeting data gathered for their customers. This type of data system is generally referred to as a Data Management Platform (DMP) in the industry. While there are some stand-alone DMPs out there, more and more DSPs are integrating or building their own.

There are a variety of other bits of information about the ad impression that get passed in the bid stream. To get a sense of what might be passed you can look at the Open RTB API (http://code.google.com/p/openrtb/). It is, of course, very technical but there are grids that list out the information being exchanged.

What are the revenue models for SSPs and DSPs?

 What kind of gross margins do they earn? Specifically do SSP/DSPs earn a % of ad spend? If so what is it? Or do they operate on an arbitrage model, straight fee model? I’m looking for specific numbers/percentages. Do the same models apply to both desktop and mobile RTB?
This question was asked on Quora.com, below is my answer.

I can answer some of these questions, but I won’t go into specifics about the percentages since I can only really give you insight into one company with any confidence.

SSP

Revenue Models

On the SSP side the revenue model is based on a percentage of revenue flowing to the publisher. This works well as it lines up the SSP’s incentives with the publisher. The more money the publisher makes – the more money the SSP makes. SSP’s are generally geared toward serving the publishers so that alignment makes for a good relationship.

DSP

On the DSP side I know of two prevalent revenue models. The most prevalent has profits tied to a percentage of spend. This is taking money the same way the SSPs do, so it requires the advertisers that use the DSP to trust that all the algorithms and technology is: A – getting them a good price, and B – fulfilling the requirements of the campaign in the most optimal way. The major DSPs seem to have a good handle on these two elements and appear to be doing fine, even though the financial model doesn’t inherently lend itself to support “A”.  Read more