Online Advertising€¦ · $ Online Ads: # Banner Ads, Sponsored Search Ads, Pay-per-Sale ads. $...

Web Science & Technologies University of Koblenz ▪ Landau, Germany

Online Advertising

Steffen Staab

Introduction to Web Science Slide 2 of 71 http://west.uni-koblenz.de

Topics

§  Introduction to online advertisement w  Understanding the participants and their roles. w  Targeted advertising.

§  Privacy Issues §  Solutions

w  User based solutions w  Collaborative solutions

§  Conclusions


Introduction

§  Online Advertising plays a critically important role in the Internet world.

§  advertising is the main way of profiting from the Internet, the history of Internet advertising developed alongside the growth of the medium itself


Facts and short history

§  First internet banner, 1994, AT&T.

§  Also in 1994, the first commercial spam, a "Green Card Lottery".

§  The first ad server was developed by FocaLink Media Services and introduced on 1995.

§  In March 2008, Google acquired DoubleClick for US$3.1 billion in cash.


Parties

§  Advertiser w Got money, wants publicity w  e.g., Coca-Cola

§  Publisher w Got content, wants money w  Cnn.com

§  Ad-network w Got advertising infrastructure, wants money w  e.g., Google AdSense, Yahoo

§  Consumer w Wants free content


Ad embedding


Business Model

q CPM = Cost Per thousand impressions ¤ Impression: user just sees the ad. ¤ Rates vary from $0.25 to $100

q CPC = Cost Per Click ¤ This is the cost charged to an advertiser

every time their ad is "clicked" on ¤ Rates around 0.3$ per click


Click fraud

§  clicking on an ad for the purpose of generating a charge per click without having actual interest.

§  Might be: w  The publisher w  Advertiser’s competitor w  The publisher’s competitor

§  Ad-networks deal with it by trying to identify who clicks on the ads.


Online Advertising and Ad Auctions at Google

Vahab Mirrokni

Google Research, New York


n  At the beginning: Traditional Ads q  Posters, Magazines, Newspapers, Billboards.

n  What is being Sold:

q  Pay-per-Impression: Price depends on how many people your ad is shown to (whether or not they look at it)

n  Pricing: q  Complicated Negotiations (with high monthly premiums...)

q  Form a barrier to entry for small advertisers

Traditional Advertising


n  Online Ads:

q  Banner Ads, Sponsored Search Ads, Pay-per-Sale ads. n  Targeting:

q  Show to particular set of viewers. n  Measurement:

q  Accurate Metrics: Clicks, Tracked Purchases. n  What is being Sold:

q  Pay-per-Click, Pay-per-Action, Pay-per-Impression n  Pricing:

q  Auctions

Advertising on the Web


1994: Banner ads, pay-per-impression

Banner ads for Zima and AT&T appear on hotwired.com.

1998: Sponsored search, pay-per-click 1st-price auction

GoTo.com develops keyword-based advertising with pay-per-click sales.

2002: Sponsored search, pay-per-click 2nd-price auction

Google introduces AdWords, a second-price keyword auction with a number of innovations.

1996: Affiliate marketing, pay-per-acquisition

Amazon/EPage/CDNow pay hosts for sales generated through ads on their sites.

History of Online Advertising


Banner Ads


n  Pay-per-1000 impressions (PPM): advertiser pays each time ad is displayed q  Models existing standards from magazine, radio, television q  Main business model for banner ads to date q  Corresponds to inventory host sells

n  Exposes advertiser to risk of fluctuations in market q  Banner blindness: effectiveness drops with user experience

n  Barrier to entry for small advertisers q  Contracts negotiated on a case-by-case basis with large minimums

(typically, a few thousand dollars per month)

Pay-Per-Impression


n  Pay-per-click (PPC): advertiser pays only when user clicks on ad q  Common in search advertising q  Middle ground between PPM and PPA

n  Does not require host to trust advertiser n  Provides incentives for host to improve ad displays

Pay-PerClick


n  Advertisements sold automatically through auctions: advertisers submit bids indicating value for clicks on particular keywords q  Low barrier-to-entry q  Increased transparency of mechanism

n  Keyword bidding allowed increased targeting opportunities

Auction Mechanism


n  Initial GoTo model: first-price auction q  Advertisers displayed in order of decreasing bids q  Upon a click, advertiser is charged a price equal to his bid q  Used first by Overture/Yahoo!

n  Google model: stylized second-price auction q  Advertisers ranked according to bid and click-through-

rate (CTR), or probability user clicks on ad q  Upon a click, advertiser is charged minimum amount

required to maintain position in ranking

Auction Mechanism


Graph from [Zhang 2006]

n  Bidding history in Yahoo! First-Price Auction:

Bidding Patterns


Graph from [Zhang 2006]

Bidding Patterns


4

Targeting Populations

Advert Creation

Keyword Selection

Bids and Budget

3 2 1

“You don’t get it, Daddy, because they’re not targeting you.”

Bidding Process


4


Advert Creation

Keyword Selection

Bids and Budget

“Here it is – the plain unvarnished truth. Varnish it.”

3 2 1

Bidding Process


Ad title Ad text

Display url


4


Advert Creation

Keyword Selection

Bids and Budget

“Now, that’s product placement!”

3 2 1

Bidding Process


4


Advert Creation

Keyword Selection

Bids and Budget

3 2 1

Bidding Process


Daily Budget


n  A repeated mechanism! n  Upon each search,

q  Interested advertisers are selected from database using keyword matching algorithm

q  Budget allocation algorithm retains interested advertisers with sufficient budget

q  Advertisers compete for ad slots in allocation mechanism q  Upon click, advertiser charged with pricing scheme

n  CTR updated according to CTR learning algorithm for future auctions

Auction Mechanism


n  Click-through rate (CTR): a parameter estimating the probability that a user clicks on an ad

n  A separate parameter for each ad/keyword pair n  Assumption: CTR of an ad in a slot is equal to the

CTR of the ad in slot 1 times a scaling parameter which depends only on the slot and not the ad

n  CTR learning algorithm uses a weighted averaging of past performance of ad to estimate CTR

Click-Through Rates


Advertiser

A

B

C

Bid Allocation Price

$10 2 $5

$5

$50

X

1

$0

$10

per click!

Ad slot 1

Ad slot 2

Keyword

Algorithmic search results

(Old) Yahoo! 2nd-Price Auction


Advertiser

A

B

C

Bid CTR Bid x CTR Allocation Price

$10 0.10 1.0 2 $5

$5

$50

0.50

0.01

2.5

0.5

1

X

$2

$0

(expected bid per impression)

per click!

Ad slot 1

Ad slot 2

Keyword

Algorithmic search results

Google Single-Shot Auction


n  Exact match: keyword phrase equals search phrase n  Phrase match: keyword phrase appears in search

(“red roses” matches to “red roses for valentines”) n  Broad match: each word of keyword phrase appears

in search (“red roses” matches to “red and white roses”)

n  Issues: q  Tradeoff between relevance and competition q  How to handle spelling mistakes

Keyword Matching


n  Basic algorithm q  Spread monthly budget evenly over each day q  If budget leftover at end of day, allocate to next day q  When advertiser runs out of budget, eliminate from

auctions

n  Issues: q  Need to smooth allocation through-out day q  Allocation of budget across keywords

Budget Allocation


Keyword Price in 3rd slot # of Keywords

$20-$50 2

$10.00 - $19.99 22

$5.00 - $9.99 206

$3.00 - $4.99 635

$1.00 - $2.99 3,566

$0.50 - $0.99 4,946

$0.25 - $0.49 5,501

$0.11 - $0.24 5,269

n  PPC of most popular searches in Google, 4/06

Typical Parameters


Keyword Top Bid 2nd Bid mesothelioma $100 $100

structured settlement $100 $52

vioxx attorney $38 $38

student loan consolidation $29 $9

n  Bids on some valuable keywords n  CTRs are typically around 1%

Typical Parameters


n  Avoiding click fraud n  Bidding with budget constraints n  Externalities between advertisers n  User search models

Typical Parameters


n  Adwords FrontEnd: Bid Simulations q  Clicks and Cost for other bids.

n  Google Analytics q  Traffic Patterns, Site visitors.

n  Search insights: q  Search Patterns

n  Interest-Based Advertising q  Indicate your interests so that you get more relevant ads

Measurement: Information


AdWords FrontEnd


Web Analytics


47

n  Distinguish Causality and Correlation. n  Experimentation:

q  Ad Rotation: 3 different creatives q  Website Optimizer q  E.g. 6000 search quality experiments, 500 of

which were launched.

n  Repeated experimentation: q  Continuous Improvement (Multi-armed bandit)

Re-acting to Metrics


48

n  Google Ad Systems: q  Sponsored Search: AdWord Auctions. q  Contextual Ads (AdSense) & Display Ads (DoubleClick) q  Ad Exchange q  Social Ads, YouTube, TV ads.

n  Bid Management & Campaign Optimization for Advertisers q  Short-term vs. Long-term effect of ads.

n  Planning: Ad Auctions & Ad Reservations. q  Stochastic/Dynamic Inventory Planning q  Pricing: Auctions vs Contracts

n  Ad Serving q  Online Stochastic Assignment Problems

Other Online Advertising Aspects


49

n  Efficiency, Fairness, Smoothness. n  Sponsored Search: Repeated Auctions, Budget

Constraints, Throttling, Dynamics(?) n  Display Ads: Online Stochastic Allocation

q  Impressions arrive online, and should be assigned to Advertisers (with established contracts) n  Online Primal-Dual Algorithms. n  Offline Optimization for Online Stochastic Optimization: Power

of Two Choices.

q  Learning+Optimization: Exploration vs Exploitation??

n  Ad Exchange Ad Serving: Bandwidth Constraints. n  Social Ads: Ad Serving over Social Networks

Ad Serving


Itay Gonshorovitz Foundation of privacy

TARGETED ONLINE ADVERTISING


Online behavioral advertising

§  Online behavioral advertising refers to the practice of ad-networks tracking users across web sites in order to learn user interests and preferences.

§  Benefits

w Advertisers targets a more focused audience which increases the effectively.

w Consumer is “bothered” by more relevant and interesting ads.


How ad-networks match ads

§  Most behavioral targeting systems work by categorizing users into one or more audience segments.

§  Profiling users based on collected data w  Search history – analyzing search keywords w  Browse history - analyzing content of visited pages w  Purchase history w  Social networks w Geography


How Ad-Networks track users

§  Cookies w  3rd Party cookies w  Flash cookies

§  Web bug §  IP address §  User-agent Headers

w  Browser + OS w More than 24,000 signatures


Levis.com case study


Privacy

§  Tracking and categorizing users by the ad-networks tend to violate user’s privacy.

§  The gathered information, linked with the users real identity, form a violation of privacy in its most basic form.

§  For example, if a person is searching the web for information on a serious genetic disease, that information can be collected and stored along with that consumer's other information - including information that can uniquely identify the consumer.


So… What we have so far?

§  User - Preserve his privacy §  Ad-Network & Publisher –

w Maintain targeting and preserve their effectiveness and income

w  Still want to be able to fight click fraud §  Questions:

w  Do the two goals necessarily conflict? w Or can they be both achieved?


Naive (paranoid) solution

§  Surf only across anonymizing proxies. w  TOR

§  Surf in private mode §  Advantages

w  Effective from the user’s perspective.

§  Disadvantages w  Are proxies really anonymizing? w  Very awkward w  Slower w  Damages targeted advertising


TrackMeNot (Howe, Nissenbaum, 2005)

§  Implemented as a Firefox plugin. §  Achieves privacy through obfuscation. §  Generates noisy queries. §  Starts with fixed a seed query list and evolve queries base

on previous results. §  Mimics user behavior so fake queries be indistinguishable:

w Query timing w  Click through behavior


TrackMeNot

§  Advantages w  Simple

§  Disadvantages w  Still the real queries can be connected to real identity. w Might have problems with offensive contents. w  Again, damages targeted advertising


Privad (Guha, Reznichenko, Tang , et al., 2009)

§  Require client software:

w saves locally database of ads (served by the ad-network) w Learn user interests in order to match ads. w Match add from the local database according

to the User interests.


Privad

§  Introduce new party – Dealer:

w Proxies anonymously all communication between the user and the ad-network.

w might be government regulatory agency. w hides user’s identity from the ad-network, but

itself does not learn any profile information about the user since all messages between the user and ad-network are encrypted.


Privad

§  Advantages w  Ad-Networks can still target ads without violates user

privacy.

§  Disadvantages w  Complicated to add the new party. w  Ad-Network has to trust the dealer in order to fight click-

fraud which might unmotivated them to cooperate.


Adnostic (Toubina, Narayanan, Boneh, et al., 2009)

§  Two party solution: w  Client side: Implemented as a Firefox plugin. w  Server side: requires Ad-Network support

§  User’s preferences and interests are stored locally by the plugin, instead of at the Ad-network.

§  The targeted ad is selected by the plugin locally at the users computer, instead of at the Ad-Network servers.


Adnostic - Accounting

§  “charge per click” model remains unchanged. §  “charge per impression” is harder. §  It uses homomorphic encryption scheme.

w  given the public key and ciphertexts , anyone can calculate

w  given the public key and ciphertexts , and scalar c, can be calculated.


Adnostic - charge per impression protocol

¨ Client: Track user activity and maintains the data locally.

¨ Visits an Ad supported website. ¨ Server: Sends a list of n ads ids along with

public key ¨ The browser chooses an ad to display to the

user. Then creates that matches the selected ad, then send , Along with zero-knowledge proof that and each is 0 or 1.


Adnostic - charge per impression protocol

q Validates the proof. If the proof is valid then using homomorphic encryption calculates

when c is the price of viewing the ad. q The server save encrypted counter for each ad and

add to it the previous values. Only one counter’s real value change.

§  At the end of the billing period, say a month, each counter is decrypted (should be done by trusted authority) and the advertisers pays for the ad-network.


Adnostic

§  Advantages w  Ad-networks can still target ads without violates user

privacy. w  Ad-networks can still detect click fraud though it will be

difficult without gathering information on IP even for a short time.

§  Disadvantages w  Ad-networks become weaker. w  Ad-networks can still track user if they are willing to, and

the protocol is built on trust.


n  Measurements n  Pricing n  Experimentation n  Other form of Advertising:

q  TV Ads q  Ad Exchanges q  Social Ads

Future of Online Advertising


Conclusions

§  In my opinion, It is hard to believe that ad-networks will give up the power of tracking users without legislation.

§  Nevertheless, There are reasonable solutions that still support targeted advertising without violating users privacy.

Online Advertising€¦ · $ Online Ads: # Banner Ads, Sponsored Search Ads, Pay-per-Sale ads. $...

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