• Save
PubMatic Ad Price Prediction 2009
Upcoming SlideShare
Loading in...5
×
 

PubMatic Ad Price Prediction 2009

on

  • 1,998 views

Technology White Paper / Ad Price Prediction: 2nd Generation Ad Revenue Optimization for Publishers

Technology White Paper / Ad Price Prediction: 2nd Generation Ad Revenue Optimization for Publishers

Statistics

Views

Total Views
1,998
Views on SlideShare
1,996
Embed Views
2

Actions

Likes
4
Downloads
49
Comments
0

2 Embeds 2

http://www.lmodules.com 1
http://www.linkedin.com 1

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

PubMatic Ad Price Prediction 2009 PubMatic Ad Price Prediction 2009 Document Transcript

  • Technology White Paper Ad Price Prediction: 2nd Generation Ad Revenue Optimization for Publishers 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 Technical Level of White Paper:
  • Ad Price Prediction: 2nd Generation Technology Ad Revenue Optimization for Publishers White Paper Table of Contents Executive Summary.......................................................................................................... 3 The Need for Real-Time AdAd Price Prediction: 2nd Generation Revenue Optimization.......................................................... 4 Ad Revenue Optimization for Publishers Three Levels of Ad Optimization....................................................................................... 6 Ad Price Prediction: How It Works.....................................................................................8 Ad Price Prediction Publisher Case Studies....................................................................12 Conclusion.......................................................................................................................12 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 Technical Level of White Paper: Published by PubMatic, 2009 2
  • Ad Price Prediction: 2nd Generation Technology Ad Revenue Optimization for Publishers White Paper 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 adGeneration exchang- Ad Price Prediction: 2nd networks and ad es, is enabling publishers to significantly increaseOptimization for Publishers Ad Revenue 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. Technology Outlined: PubMatic developed the first and only real-time optimization solution in 2006. Since then, Pub- Ad Price Prediction Matic has collected hundreds of billions of data points through a machine learning approach and has introduced two new Technology Benefits: technologies in 2009 that have enabled the technology to enter into a new, more precise phase of real-time optimization: publisher • Improve monetization of every single Ad Price Prediction. ad impression Ad Price Prediction matches the optimal ad defaulting or ad for publishers • Solves ad networknetwork problemexchange with every ad impression, • Two new technologies improves algorithm accuracy on behalf of the publisher, in •real-time. It enablesexchanges, and Works with ad networks, ad the publisher to significantly grow their ad revenue as they also manage increasing amounts of non-guaranteed inventory. non-guaranteed insertion orders For large online publishers with millions of ads shown per day, this means that millions more Technical Level of White Paper: 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 3
  • Ad Price Prediction: 2nd Generation Technology Ad Revenue Optimization for Publishers White Paper I: The Need for Real-Time Ad Revenue Optimization Publishers 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,Ad Price based solution that not only helps manage ad net- technology Prediction: 2nd Generation works, but also ensures that the publisher is getting the most revenuefor Publishers Ad Revenue Optimization possible for every single impression, is needed. Five reasons why a real-time solution is needed to ensure publishers get maximum ad revenue 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 Technology Outlined: monetizing different segments of their audience. Therefore, ads are often delivered from ad networks that value the ad Ad Price Prediction than a different ad network would that is trying impression less to reach that specific audience. Technology Benefits: • Improve monetization of every single publisher ad impression 3. Ad Networks Default Often, And It’s A Big Problem: • Solves ad network defaulting problem for publishers PubMatic has found that ad Two new technologies improves algorithm accuracy • networks default 56% of the time on average and as much as 87% of the time according to a study conductedexchanges,of 2008. As ad targeting becomes • Works with ad networks, ad in April and non-guaranteed insertion orders increasingly dependent on both the user and the ad context, defaulting will only increase. Technical Level of White Paper: 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. Published by PubMatic, 2009 4
  • Ad Price Prediction: 2nd Generation Technology Ad Revenue Optimization for Publishers White Paper 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. Ad Price Prediction: 2nd Generation PubMatic’s Ad Revenue Optimization Advances With the Growth of the Market Ad Revenue Optimization for Publishers Real-Time Technology Provides a Long-Term Monetization Strategy for Non-Guaranteed Inventory Non-Guaranteed Segment Growth PubMatic Real-Time In Online Advertising Technology Releases $11 B in publisher ad revenue, 34% of total publisher display ad revenue by 2013** 2013 Ad Price Prediction 30% non-guaranteed 2nd Generation Ad Optimization ad revenue growth for 2011** for Publishers 15% non-guaranteed Bidding API for ad networks - 2009 ad revenue growth - 2009** Technology Outlined: $4.1 B in non-guaranteed publisher Frequency optimization - 2008 ad revenue in 2008** Prediction Ad Price 30% of publisher inventory sold Benefits: Technology Default optimization - 2007 2007* through ad networks• -Improve monetization of every single publisher ad impression PubMatic pioneers ad optimization • Solves ad 2006 network defaulting problem for publishers category, launches real-time • Two new technologies improves algorithm accuracy publishers - 2006 solution for • Works with ad networks, ad exchanges, and non-guaranteed insertion orders *Bain/IAB Digital Pricing Researcg, August 2008 **ThinkEquity - The Opportunity in Non-Premium Display Advertising, May 2009 Technical Level of White Paper: PubMatic’s Machine Learning Approach Machine Learning is based on algorithms that improve automatically through experience. It includes data-mining that processes more than 100,000 data transactions per second. • 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 Published by PubMatic, 2009 5
  • Ad Price Prediction: 2nd Generation Technology Ad Revenue Optimization for Publishers White Paper II: Three Levels of Optimization No other company offers optimization in real-time Ad Price Prediction: 2nd Generation $$$ Ad Revenue Optimization for Publishers Publisher Revenue Real Time $$ Daily or Weekly Weekly or Monthly 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 Manual In-House Manual Outsourced Automated Algorithms Ad Operations Ad Operations Technical Level of White Paper: + Operations Support (Real-Time Optimization) Published by PubMatic, 2009 6
  • Ad Price Prediction: 2nd Generation Technology Ad Revenue Optimization for Publishers White Paper 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: Ad Price Prediction: 2nd Generation 1. Manual In-House Ad Operations (Weekly or Monthly Optimization): Most large publishers have an Revenue Optimization for Publishers to Ad ad operations team that works directly with ad networks 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 Technology Outlined: best practices, has ongoing relationships with key ad networks, and can often provide human Ad Price Prediction resources at a cheaper cost. Technology Benefits: • Improve monetization of every single publisher ad impression However, despite the efficiencies gained in resource cost,for publishers • Solves ad network defaulting problem there is typically only marginal im- provement in revenue that is generated from theimproves algorithm accuracy • Two new technologies use of third party vendors. These vendors rely on the same historical data andWorks withnumber ofad exchanges, and make ad serving decisions, and • limited ad networks, data points to non-guaranteed insertion orders as a result cannot significantly increase publisher revenue. Technical Level of White Paper: 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. Published by PubMatic, 2009 7
  • Ad Price Prediction: 2nd Generation Technology Ad Revenue Optimization for Publishers White Paper 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. Ad Price Prediction: 2nd Generation Page Request and Ad Revenue Optimization for Publishers Ad Impression Analyzed tw ork Ne Ad e Ad Exchang Ad N etwor k ork Netw Ad Filter Ad Ad networks and ad exchanges filtered based Price on geo, ad size, creative and more Prediction Technology Outlined: Inside the Ad Price Prediction technology: 2 Determine Pricing Technology Benefits: Flat CPM campaigns sold through Improve monetization of every single publisher sales force can also Algorithms determine• pricing from a publisher’s ad impression compete for the ad impression ad networks and ad exchanges • Solves ad network defaulting problem for publishers The algorithms consider frequency Bidding• API with ad networks, ad exchanges, andaccuracy • Two new technologies improves algorithm time an impression is seen, Works as each the value drops +non-guaranteed insertion orders Machine Learning Technical Level of White Paper: $$ $ 3 Select Should the selected ad network or ad exchange Highest paying ad network or default, the steps are repeated and the next ad exchange is selected highest paying one is selected $$$ Ad Network Ad Delivered from Highest Paying Ad Network Published by PubMatic, 2009 8
  • Ad Price Prediction: 2nd Generation Technology Ad Revenue Optimization for Publishers White Paper 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- Ad Price Prediction: 2nd Generation cy, geography, day part, browser, user demographics, and more. Ad Revenue Optimization for Publishers 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 Technology Outlined: to decide which ad serving option is best able points per second to monetize the impression. Data is collected from PubMatic’s machine learning algorithms and Ad Price Prediction decisions are made in real-time based on learned pricing behaviors and dynamic pricing data delivered from ad networks via the real Benefits: Technology time bidding API (application programming interface). • Improve monetization of every single publisher ad impression A key advantage for publishers in ad network defaulting problem for publishers • Solves this process is PubMatic’s ability to determine ad pricing • Two new technologies improves algorithm accuracy based on how many times the Works with adseen a particular ad. Ad networks generally value the • user has networks, ad exchanges, and first impression a user sees more than theinsertion orders non-guaranteed second impression, and so forth. Ad networks then allocate the highest paying campaigns to the first user impression, followed by the next highest Technical Level of White Paper: 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. Published by PubMatic, 2009 9
  • Ad Price Prediction: 2nd Generation Technology Ad Revenue Optimization for Publishers White Paper Frequency eCPM Curve $3.60 Ad Price Prediction: 2nd Generation $3.00 Ad Revenue Optimization for Publishers lift Area reflects publisher revenue due to optimal decision-making $2.40 Predicted eCPM $1.80 Ad Network 1 Ad Network 2 $1.20 Ad Network 3 Ad Network 4 $0.60 Optimal Ad Network Allocation (based on frequency eCPM curves) Technology Outlined: $0.00 Ad Price Prediction 1 2 3 4 5 6 7 8 9 10 >25 11-15 16-20 21-25 Technology Benefits: • Improve monetization of every single publisher ad impression • Solves ad network defaulting problem for publishers • Two new technologies improves algorithm accuracy The Highest Paying Ad Serving Entity is Selected and Then • Works with ad networks, ad exchanges, and Delivers the Ad to the User. non-guaranteed insertion orders Once PubMatic selects the entity that Level of White Paper: Technical 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. Published by PubMatic, 2009 10
  • Ad Price Prediction: 2nd Generation Technology Ad Revenue Optimization for Publishers White Paper Dynamic Default Optimization Impression 1 Ad Price Prediction: 2nd Generation Impression 2 Impression 3 Ad Revenue Optimization for Publishers eCPM Technology Outlined: Ad Price Prediction A B C D E D C A B E C B E D A Technology Benefits: • Improve monetization of every single publisher Ad Networks ad impression Ad Networks Ad Networks • Solves ad network defaulting problem for publishers • Two new technologies improves algorithm accuracy • Works with ad networks, ad exchanges, and Ad network pricingnon-guaranteed insertion orders therefore adjusting static changes constantly, daisy chains weekly, or even daily, isn’t enough to maximize yield. Technical Level of White Paper: 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. Published by PubMatic, 2009 11
  • Ad Price Prediction: 2nd Generation Technology Ad Revenue Optimization for Publishers White Paper IV: Ad Price Prediction Publisher Case Studies Publishers using Real Time Optimization consistently see higher ad pricing as compared to manual ad operations solutions, whether in-house or outsourced. The following case Ad Price Prediction: 2nd Generation studies based on three large publishers highlight the increased pricing (eCPM) generated from PubMatic’s Real Time Optimization solution using automated algorithms.. Ad Revenue Optimization for Publishers Publisher Case Study 1: 88% Lift $1.09 88% Ad Revenue Lift eCPMs Real-Time Optimization vs. Manual Outsourced Ad Operations $0.58 Site Type: News Site Reach: 10MM+ Global Users Other Yield Optimizer PubMatic Technology Outlined: 81% Lift $1.21 Publisher Case Study 2: Prediction Ad Price 81% Ad Revenue Lift eCPMs Real-Time Optimization vs. Technology Benefits: $0.67 • Improve monetization of every single publisher Manual Outsourced Ad Operations ad impression • Solves ad network defaulting problem for publishers Site Type: Women’s Interest Two new technologies improves algorithm accuracy • Site Reach: 30MM+ Global •Userswith ad networks, ad exchanges, and Works non-guaranteed insertion orders Other Yield Optimizer PubMatic Technical Level of White Paper: Publisher Case Study 3: 92% Ad Revenue Lift eCPMs Real-Time Optimization vs. 92% Lift Manual Outsourced Ad Operations $0.50 Site Type: Social Network $0.26 Site Reach: 15MM+ Global Users Other Yield Optimizer PubMatic Published by PubMatic, 2009 12
  • Ad Price Prediction: 2nd Generation Technology Ad Revenue Optimization for Publishers White Paper V: Conclusion As 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. Ad Price Prediction: 2nd Generation Manual ad operations, either in-house or with outsourced help, does improve revenue ad reve- Ad Revenue Optimization for Publishers 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%. Technology Outlined: About PubMatic Ad Price Prediction PubMatic is a global Ad Revenue Optimization company that provides online publishers with Technology Benefits: a full service solution to manage andmonetization of every single publisherad inventory. PubMatic’s real- • Improve monetize non-guaranteed ad impression time ad price prediction technology ensures that online publishers get the most money from • Solves ad network defaulting problem for publishers their advertising space by deciding intechnologies improves ad network or exchange can best mon- • Two new real-time which algorithm accuracy etize each impression. • Works with ad networks, ad exchanges, and non-guaranteed insertion orders There are currently over 6,000 large and medium publishers working with PubMatic. PubMatic Technical Level of White Paper: 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. Josh.Wetzel@PubMatic.com If you are an ad network interested in learning more, please contact Jeanne Houweling, VP Business Development and Advertising Sales. Jeanne.Houweling@PubMatic.com Published by PubMatic, 2009 13