Pub matic.adpriceprediction.2009

Technology White Paper

Technical Level of White Paper:

Technology Outlined:Ad Price Prediction

Technology Benefits:• Improve monetization of every single publisher ad impression • Solves ad network defaulting problem for publishers• Two new technologies improves algorithm accuracy• Works with ad networks, ad exchanges, and

non-guaranteed insertion orders

Ad Price Prediction: 2nd Generation Ad Revenue Optimization for Publishers

Table of Contents

Executive Summary.......................................................................................................... 3

The Need for Real-Time Ad Revenue Optimization.......................................................... 4

Three Levels of Ad Optimization....................................................................................... 6

Ad Price Prediction: How It Works..................................................................................... 8

Ad Price Prediction Publisher Case Studies.................................................................... 12

Conclusion....................................................................................................................... 12

Published by PubMatic, 2009 2








Executive Summary The non-guaranteed segment of online advertising is the highest-growth online advertising cat-egory and will reach $11 billion by 2013, according to a recent in-depth report by ThinkEquity. Rapid innovation by companies within the ecosystem, particularly ad networks and ad exchang-es, is enabling publishers to significantly increase the ad revenue made from their non-guaran-teed inventory. Nonetheless, challenges exist to maintain sustainable growth.

Along with the growth of non-guaranteed ad inventory, the rise in the number of ad networks and ad exchanges over the past few years has created a need for them to diversify themselves by focusing on different audiences and targeting capabilities. For example ad network “X” might be better suited than ad network “Y” to monetize a specific ad impression while ad network “Y” might be better able to monetize another impression than ad network “X.” Publishers can benefit from this diversification, and maximize the value of each ad impression, if they have the ability to match the optimal ad network or ad exchange to each impression in real-time. Over the last three years with a team of over 40 engineers and statisticians, PubMatic has developed optimization technology and invented a whole new category of service provider that enables publishers to do this.

PubMatic developed the first and only real-time optimization solution in 2006. Since then, Pub-Matic has collected hundreds of billions of data points through a machine learning approach and has introduced two new technologies in 2009 that have enabled the technology to enter into a new, more precise phase of real-time optimization: Ad Price Prediction.

Ad Price Prediction matches the optimal ad network or ad exchange with every ad impression, on behalf of the publisher, in real-time. It enables the publisher to significantly grow their ad revenue as they also manage increasing amounts of non-guaranteed inventory.

For large online publishers with millions of ads shown per day, this means that millions more of their advertisements are optimized than otherwise would be if they used a manual ad operations approach to working with ad networks.

This white paper describes in detail why PubMatic developed the sophisticated algorithms that power the Ad Price Prediction technology, how it increases publisher ad revenue, and several publisher case studies.









Published by PubMatic, 2009

I: The Need for Real-Time Ad Revenue OptimizationPublishers that seek to increase their ad revenue from non-guaranteed inventory face signifi-cant challenges. While limited ad operations could be performed on a daily or weekly basis to improve revenue, a real-time, technology based solution that not only helps manage ad net-works, but also ensures that the publisher is getting the most revenue possible for every single impression, is needed.

Five reasons why a real-time solution is needed to ensure publishers get maximum ad revenue

4

1. Static Ad Network Daisy Chains Are Ineffective: Ad pricing from ad networks changes constantly throughout the day and with a manual solution the publisher often doesn’t have the highest paying ad network at the top of their static daisy chain.

2. More Ad Networks Need to Compete for Every Single Ad Impression: With a manual solution, managing multiple ad networks is challenging. The result is that publishers often don’t have enough relationships with ad networks that specialize in monetizing different segments of their audience. Therefore, ads are often delivered from ad networks that value the ad impression less than a different ad network would that is trying to reach that specific audience.

3. Ad Networks Default Often, And It’s A Big Problem: PubMatic has found that ad networks default 56% of the time on average and as much as 87% of the time according to a study conducted in April of 2008. As ad targeting becomes increasingly dependent on both the user and the ad context, defaulting will only increase.








4. Low Quality Ads Have Low Click-Through Rates: Managing multiple ad networks with a completely manual solution is challenging. Fewer ad networks mean the options for ads that can be served are limited, and that can result in served ads that are unattractive to the user which negatively impacts the click through rate.

5. One Solution Is Needed for Non-Guaranteed I.O.s, Ad Networks, and Exchanges: Publishers increasingly need to manage all of their non-guaranteed demand, whether it’s from ad networks, ad exchanges, or direct advertiser insertion orders, from the same buck-et of non-guaranteed inventory.


PubMatic identified the challenges that publishers face at the onset of building out its ad revenue optimization technology, and continues to advance it based on the needs of the publisher and market growth.

PubMatic’s Ad Revenue Optimization Advances With the Growth of the MarketReal-Time Technology Provides a Long-Term Monetization Strategy for Non-Guaranteed Inventory

PubMatic Real-Time Technology Releases

2006

Non-Guaranteed Segment GrowthIn Online Advertising

$11 B in publisher ad revenue, 34% of total publisher display ad revenue by 2013**

30% of publisher inventory sold through ad networks - 2007*

$4.1 B in non-guaranteed publisher ad revenue in 2008**

15% non-guaranteed ad revenue growth - 2009**

30% non-guaranteed ad revenue growth for 2011**

Default optimization - 2007

Bidding API for ad networks - 2009

2nd Generation Ad Optimization for Publishers

Ad Price Prediction

2013

Frequency optimization - 2008

PubMatic pioneers ad optimization category, launches real-time solution for publishers - 2006








• PubMatic has over 6,000 publishers using the optimization platform, which continually provide rich data that contributes to the machine learning.

• The longer machine learning is working, the more precise and accurate it becomes. • The data collected through machine learning is used for predictive modeling and is

the basis of PubMatic’s Ad Price Prediction technology

PubMatic’s Machine Learning ApproachMachine Learning is based on algorithms that improve automatically through experience. It includes data-mining that processes more than 100,000 data transactions per second.

*Bain/IAB Digital Pricing Researcg, August 2008 **ThinkEquity - The Opportunity in Non-Premium Display Advertising, May 2009


II: Three Levels of Optimization

No other company offers optimization in real-time

Manual In-House Ad Operations

Automated Algorithms+

Operations Support (Real-Time Optimization)

$$$

$$

$

Publ

ishe

r Rev

enue

Wee

kly

or M

onth

ly

Dai

ly o

r Wee

kly

Rea

l Tim

e

Manual Outsourced Ad Operations








In order for large publishers to truly maximize their ad revenue made from ad networks and ex-changes, optimization is needed. Publishers can optimize in three principal ways with varying degrees of success:

1. Manual In-House Ad Operations (Weekly or Monthly Optimization): Most large publishers have an ad operations team that works directly with ad networks to manually optimize them. They do this by logging into the ad networks and finding historical pricing and then setting up their “daisy chains” accordingly. The frequency of the optimization usually ranges from weekly to monthly, depending on human resources. This type of optimiza-tion does provide revenue lift in the vast majority of cases, but the inherent problem is that pub-lishers are using historical data and are limited to a very small number of data points by which they can make optimization decisions. As the number of ad network relationships increases, this approach requires correspondingly more human resources to optimize.

2. Manual Outsourced Ad Operations (Daily or Weekly Optimization): In an effort to escape the resource trap of manual in-house ad operations, some publish-ers outsource the management of ad network relationships to third party vendors. There are often resource and expertise benefits, as an outsourced service provider has typically identified best practices, has ongoing relationships with key ad networks, and can often provide human resources at a cheaper cost.

However, despite the efficiencies gained in resource cost, there is typically only marginal im-provement in revenue that is generated from the use of third party vendors. These vendors rely on the same historical data and limited number of data points to make ad serving decisions, and as a result cannot significantly increase publisher revenue.

3. Automated Algorithms + Operations Support (Real-Time Optimization): This solution is the only solution that can best monetize every single ad impression. Hav-ing ad operations support is critical to a publisher in order to simplify ad network management and ensure that unwanted ads do not appear on their site, but only real-time algorithms can predict which ad network will pay the most for any given impression, 24 hours a day, 7 days a week. Real-Time algorithms can use significantly more data points to make ad serving deci-sion, such as geography, frequency, context, demographic information and more. In addition, these algorithms can make a unique decision in real-time for each and every ad impression. More data and real-time decisions yield significantly higher publisher revenue.










III: Ad Price Prediction: How It Works PubMatic’s Ad Price Prediction technology decides, in real-time, which ad network, ad ex-

change, or non-guaranteed insertion order is best able to monetize an ad impression for a publisher.

8

3

Ad NetworkAd Network

Ad ExchangeAd Network

2

Page Request andAd Impression Analyzed

Ad networks and ad exchanges filtered based on geo, ad size, creative and more

Flat CPM campaigns sold through a publisher’s sales force can also compete for the ad impression

The algorithms consider frequency as each time an impression is seen, the value drops

Should the selected ad network or ad exchange default, the steps are repeated and the next highest paying one is selected

Algorithms determine pricing fromad networks and ad exchanges

Highest paying ad network orad exchange is selected

Filter

Determine Pricing

Select

Ad Delivered from Highest Paying Ad Network

$ $ $

Bidding API

Machine Learning+

Ad Network

Ad

Price

PredictionInside the technology:

$ $ $








Detailed Flow Description:

The user on a publisher’s website makes a page request. The impression is then analyzed for dozens of different data points including context, frequen-cy, geography, day part, browser, user demographics, and more.

Ad serving entities, including ad networks, ad exchanges, and non-guaranteed insertion orders are then filtered. The filtering process takes into consideration the analyzed impression and user data as well as the creative policy of the publisher. For example, ad networks that serve suggestive or alcohol ads will not make it through the filter if the publisher’s business rules require that suggestive or alcohol ads not be shown on their website.

Algorithms determine pricing from Ad Networks, Ad Exchanges, and Non-Guaranteed Entities. From the eligible ad serving entities that passed through the first filter, PubMatic’s algorithms process over 100,000 of data points per second to decide which ad serving option is best able to monetize the impression. Data is collected from PubMatic’s machine learning algorithms and decisions are made in real-time based on learned pricing behaviors and dynamic pricing data delivered from ad networks via the real time bidding API (application programming interface).

A key advantage for publishers in this process is PubMatic’s ability to determine ad pricing based on how many times the user has seen a particular ad. Ad networks generally value the first impression a user sees more than the second impression, and so forth. Ad networks then allocate the highest paying campaigns to the first user impression, followed by the next highest paying ad campaign and so on. The algorithms take this frequency pricing into consideration and will choose not to show an ad if the user has seen it enough times that the value is too low. Because frequency capping is a part of most campaigns today, this technology is incredibly valuable and provides significant and long-term revenue lift.










Pre

dict

ed e

CP

M

Frequency eCPM Curve

Ad Network 1

Ad Network 2

Ad Network 3

Ad Network 4

Optimal Ad Network Allocation(based on frequency eCPM curves)

Area reflects publisher revenue liftdue to optimal decision-making

1 2 3 4 5 6 7 8 9 10

11-1

5

16-2

0

21-2

5

>25

$3.60

$3.00

$2.40

$1.80

$1.20

$0.60

$0.00

The Highest Paying Ad Serving Entity is Selected and Then Delivers the Ad to the User. Once PubMatic selects the entity that is best able to monetize the ad impression, an ad request is sent to that entity. The ad is then served directly from that entity, whether it is from an ad network, ad exchange, or from an insertion order.

*If the selected ad entity were to default, the previous steps would be repeat and the next highest paying ad serving entity is selected.

10








Dynamic Default Optimization

eCP

M

Ad Networks

Impression 1

A B C D E

Impression 2

Ad Networks

D C A B E

Impression 3

Ad Networks

C B E D A

Ad network pricing changes constantly, therefore adjusting static daisy chains weekly, or even daily, isn’t enough to maximize yield. Dynamic Default Optimization updates the daisy chain in real time, for every impression, which ensures that the impression goes to the highest paying ad network.










IV: Ad Price Prediction Publisher Case StudiesPublishers using Real Time Optimization consistently see higher ad pricing as compared to manual ad operations solutions, whether in-house or outsourced. The following case studies based on three large publishers highlight the increased pricing (eCPM) generated from PubMatic’s Real Time Optimization solution using automated algorithms..

12








Publisher Case Study 1:88% Ad Revenue Lift Real-Time Optimization vs.


Site Type: NewsSite Reach: 10MM+ Global Users



Site Type: Social Network Site Reach: 15MM+ Global Users



Site Type: Women’s InterestSite Reach: 30MM+ Global Users

Other Yield Optimizer PubMatic

$1.09

$0.58

88% Lift

81% Lift

92% Lift


$1.21

$0.67


$0.50

$0.26

eCPM

seC

PMs

eCPM

s


V: ConclusionAs ad inventory continues to grow, new methods of ad revenue optimization must be adopted by large publishers if they are to protect and improve the value of their advertising space. Manual ad operations, either in-house or with outsourced help, does improve revenue ad reve-nue lift in most cases, but those options are not feasible for long-term revenue growth. Because the non-guaranteed segment of online advertising is expected to grow to $11 billion by 2013, publishers must adopt a specific strategy for this segment of inventory.Due to the volatility of online ad pricing, and because no single ad network can guarantee the highest price for a publisher’s ad space all of the time, only a real-time optimization solution can ensure that every impression is monetized by the highest paying source.PubMatic offers publishers the most advanced method of ad revenue optimization available: Ad Price Prediction technology (automated algoritms) in addition to full service team support for the publisher’s ad operations team. Publishers that have been using PubMatic’s solution regularly see ad revenue lift ranging from 30-300%.

About PubMatic

PubMatic is a global Ad Revenue Optimization company that provides online publishers with a full service solution to manage and monetize non-guaranteed ad inventory. PubMatic’s real-time ad price prediction technology ensures that online publishers get the most money from their advertising space by deciding in real-time which ad network or exchange can best mon-etize each impression.

There are currently over 6,000 large and medium publishers working with PubMatic. PubMatic is venture backed by Draper Fisher Jurvetson, Nexus India Capital, and Helion Ventures.

Contact PubMatic

If you are a large online publisher interested in learning more, please contact Josh Wetzel, VP Publisher Solutions. [email protected]

If you are an ad network interested in learning more, please contact Jeanne Houweling, VP Business Development and Advertising Sales. [email protected]








Pub matic.adpriceprediction.2009

Technology

Transcript of Pub matic.adpriceprediction.2009