Web Science & Technologies University of Koblenz ▪ Landau, Germany Online Advertising Steffen Staab Topics Introduction to online advertisement Understanding the participants and their roles. Targeted advertising. Privacy Issues Solutions User based solutions Collaborative solutions Conclusions Introduction to Web Science Slide 2 of 71 http://west.uni-koblenz.de 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 Introduction to Web Science Slide 3 of 71 http://west.uni-koblenz.de 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. Introduction to Web Science Slide 4 of 71 http://west.uni-koblenz.de Parties Advertiser Got money, wants publicity e.g., Coca-Cola Publisher Got content, wants money Cnn.com Ad-network Got advertising infrastructure, wants money e.g., Google AdSense, Yahoo Consumer Wants free content Introduction to Web Science Slide 5 of 71 http://west.uni-koblenz.de Ad embedding Introduction to Web Science Slide 6 of 71 http://west.uni-koblenz.de Business Model CPM = Cost Per thousand impressions Impression: user just sees the ad. Rates vary from $0.25 to $100 CPC = Cost Per Click This is the cost charged to an advertiser every time their ad is "clicked" on Rates around 0.3$ per click Introduction to Web Science Slide 7 of 71 http://west.uni-koblenz.de Click fraud clicking on an ad for the purpose of generating a charge per click without having actual interest. Might be: The publisher Advertiser’s competitor The publisher’s competitor Ad-networks deal with it by trying to identify who clicks on the ads. Introduction to Web Science Slide 8 of 71 http://west.uni-koblenz.de Online Advertising and Ad Auctions at Google Vahab Mirrokni Google Research, New York Introduction to Web Science Slide 9 of 71 http://west.uni-koblenz.de Traditional Advertising At the beginning: Traditional Ads Posters, Magazines, Newspapers, Billboards. What is being Sold: Pay-per-Impression: Price depends on how many people your ad is shown to (whether or not they look at it) Pricing: Complicated Negotiations (with high monthly premiums...) Form a barrier to entry for small advertisers Introduction to Web Science Slide 10 of 71 http://west.uni-koblenz.de Advertising on the Web Online Ads: Banner Ads, Sponsored Search Ads, Pay-per-Sale ads. Targeting: Show to particular set of viewers. Measurement: Accurate Metrics: Clicks, Tracked Purchases. What is being Sold: Pay-per-Click, Pay-per-Action, Pay-per-Impression Pricing: Auctions Introduction to Web Science Slide 11 of 71 http://west.uni-koblenz.de History of Online Advertising 1994: Banner ads, pay-per-impression 1998: Sponsored search, pay-per-click 1st-price auction Banner ads for Zima and AT&T appear on hotwired.com. GoTo.com develops keywordbased advertising with pay-perclick sales. 1996: Affiliate marketing, pay-per-acquisition 2002: Sponsored search, pay-per-click 2nd-price auction Amazon/EPage/CDNow pay hosts for sales generated through ads on their sites. Google introduces AdWords, a second-price keyword auction with a number of innovations. Introduction to Web Science Slide 12 of 71 http://west.uni-koblenz.de Banner Ads Introduction to Web Science Slide 13 of 71 http://west.uni-koblenz.de Pay-Per-Impression Pay-per-1000 impressions (PPM): advertiser pays each time ad is displayed Exposes advertiser to risk of fluctuations in market Models existing standards from magazine, radio, television Main business model for banner ads to date Corresponds to inventory host sells Banner blindness: effectiveness drops with user experience Barrier to entry for small advertisers Contracts negotiated on a case-by-case basis with large minimums (typically, a few thousand dollars per month) Introduction to Web Science Slide 14 of 71 http://west.uni-koblenz.de Pay-PerClick Pay-per-click (PPC): advertiser pays only when user clicks on ad Common in search advertising Middle ground between PPM and PPA Does not require host to trust advertiser Provides incentives for host to improve ad displays Introduction to Web Science Slide 15 of 71 http://west.uni-koblenz.de Auction Mechanism Advertisements sold automatically through auctions: advertisers submit bids indicating value for clicks on particular keywords Low barrier-to-entry Increased transparency of mechanism Keyword bidding allowed increased targeting opportunities Introduction to Web Science Slide 16 of 71 http://west.uni-koblenz.de Auction Mechanism Initial GoTo model: first-price auction Advertisers displayed in order of decreasing bids Upon a click, advertiser is charged a price equal to his bid Used first by Overture/Yahoo! Google model: stylized second-price auction Advertisers ranked according to bid and click-throughrate (CTR), or probability user clicks on ad Upon a click, advertiser is charged minimum amount required to maintain position in ranking Introduction to Web Science Slide 17 of 71 http://west.uni-koblenz.de Bidding Patterns Bidding history in Yahoo! First-Price Auction: Graph from [Zhang 2006] Introduction to Web Science Slide 18 of 71 http://west.uni-koblenz.de Bidding Patterns Graph from [Zhang 2006] Introduction to Web Science Slide 19 of 71 http://west.uni-koblenz.de Bidding Process Targeting Populations Advert Creation Keyword Selection Bids and Budget 1 2 3 4 “You don’t get it, Daddy, because they’re not targeting you.” Introduction to Web Science Slide 20 of 71 http://west.uni-koblenz.de Introduction to Web Science Slide 21 of 71 http://west.uni-koblenz.de Introduction to Web Science Slide 22 of 71 http://west.uni-koblenz.de Introduction to Web Science Slide 23 of 71 http://west.uni-koblenz.de Introduction to Web Science Slide 24 of 71 http://west.uni-koblenz.de Bidding Process Targeting Populations Advert Creation Keyword Selection Bids and Budget 1 2 3 4 “Here it is – the plain unvarnished truth. Varnish it.” Introduction to Web Science Slide 25 of 71 http://west.uni-koblenz.de Display url Ad title Ad text Introduction to Web Science Slide 26 of 71 http://west.uni-koblenz.de Bidding Process Targeting Populations Advert Creation Keyword Selection Bids and Budget “Now, that’s product placement!” Introduction to Web Science Slide 27 of 71 http://west.uni-koblenz.de 1 2 3 4 Introduction to Web Science Slide 28 of 71 http://west.uni-koblenz.de Introduction to Web Science Slide 29 of 71 http://west.uni-koblenz.de Introduction to Web Science Slide 30 of 71 http://west.uni-koblenz.de Bidding Process Targeting Populations Advert Creation Keyword Selection Bids and Budget Introduction to Web Science Slide 31 of 71 http://west.uni-koblenz.de 1 2 3 4 Daily Budget Introduction to Web Science Slide 32 of 71 http://west.uni-koblenz.de Introduction to Web Science Slide 33 of 71 http://west.uni-koblenz.de Auction Mechanism A repeated mechanism! Upon each search, Interested advertisers are selected from database using keyword matching algorithm Budget allocation algorithm retains interested advertisers with sufficient budget Advertisers compete for ad slots in allocation mechanism Upon click, advertiser charged with pricing scheme CTR updated according to CTR learning algorithm for future auctions Introduction to Web Science Slide 34 of 71 http://west.uni-koblenz.de Click-Through Rates Click-through rate (CTR): a parameter estimating the probability that a user clicks on an ad A separate parameter for each ad/keyword pair 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 CTR learning algorithm uses a weighted averaging of past performance of ad to estimate CTR Introduction to Web Science Slide 35 of 71 http://west.uni-koblenz.de (Old) Yahoo! 2nd-Price Auction Keywor d Ad slot 1 Ad slot 2 Algorithmic search results Advertiser Bid Allocation Price per click! A B $10 $5 2 X $5 $0 C $50 1 $10 Introduction to Web Science Slide 36 of 71 http://west.uni-koblenz.de Google Single-Shot Auction Keywor d Ad slot 1 Ad slot 2 Algorithmic search results Bid CTR A B $10 $5 0.10 0.50 1.0 2.5 2 1 $5 $2 C $50 0.01 0.5 X $0 Introduction to Web Science Bid x CTR (expected bid per Allocation Price impression) per click! Advertiser Slide 37 of 71 http://west.uni-koblenz.de Keyword Matching Exact match: keyword phrase equals search phrase Phrase match: keyword phrase appears in search (“red roses” matches to “red roses for valentines”) Broad match: each word of keyword phrase appears in search (“red roses” matches to “red and white roses”) Issues: Tradeoff between relevance and competition How to handle spelling mistakes Introduction to Web Science Slide 38 of 71 http://west.uni-koblenz.de Budget Allocation Basic algorithm Spread monthly budget evenly over each day If budget leftover at end of day, allocate to next day When advertiser runs out of budget, eliminate from auctions Issues: Need to smooth allocation through-out day Allocation of budget across keywords Introduction to Web Science Slide 39 of 71 http://west.uni-koblenz.de Typical Parameters PPC of most popular searches in Google, 4/06 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 Introduction to Web Science Slide 40 of 71 http://west.uni-koblenz.de Typical Parameters Bids on some valuable keywords Keyword Top Bid 2nd Bid mesothelioma $100 $100 structured settlement $100 $52 vioxx attorney $38 $38 student loan consolidation $29 $9 CTRs are typically around 1% Introduction to Web Science Slide 41 of 71 http://west.uni-koblenz.de Typical Parameters Avoiding click fraud Bidding with budget constraints Externalities between advertisers User search models Introduction to Web Science Slide 42 of 71 http://west.uni-koblenz.de Measurement: Information Adwords FrontEnd: Bid Simulations Google Analytics Traffic Patterns, Site visitors. Search insights: Clicks and Cost for other bids. Search Patterns Interest-Based Advertising Indicate your interests so that you get more relevant ads Introduction to Web Science Slide 43 of 71 http://west.uni-koblenz.de AdWords FrontEnd Introduction to Web Science Slide 44 of 71 http://west.uni-koblenz.de Introduction to Web Science Slide 45 of 71 http://west.uni-koblenz.de Web Analytics Introduction to Web Science Slide 46 of 71 http://west.uni-koblenz.de Re-acting to Metrics Distinguish Causality and Correlation. Experimentation: Ad Rotation: 3 different creatives Website Optimizer E.g. 6000 search quality experiments, 500 of which were launched. Repeated experimentation: Continuous Improvement (Multi-armed bandit) Introduction to Web Science Slide 47 of 71 http://west.uni-koblenz.de 47 Other Online Advertising Aspects Google Ad Systems: Bid Management & Campaign Optimization for Advertisers Short-term vs. Long-term effect of ads. Planning: Ad Auctions & Ad Reservations. Stochastic/Dynamic Inventory Planning Sponsored Search: AdWord Auctions. Contextual Ads (AdSense) & Display Ads (DoubleClick) Ad Exchange Social Ads, YouTube, TV ads. Pricing: Auctions vs Contracts Ad Serving Online Stochastic Assignment Problems Introduction to Web Science Slide 48 of 71 http://west.uni-koblenz.de 48 Ad Serving Efficiency, Fairness, Smoothness. Sponsored Search: Repeated Auctions, Budget Constraints, Throttling, Dynamics(?) Display Ads: Online Stochastic Allocation Impressions arrive online, and should be assigned to Advertisers (with established contracts) Online Primal-Dual Algorithms. Offline Optimization for Online Stochastic Optimization: Power of Two Choices. Learning+Optimization: Exploration vs Exploitation?? Ad Exchange Ad Serving: Bandwidth Constraints. Social Ads: Ad Serving over Social Networks Introduction to Web Science Slide 49 of 71 http://west.uni-koblenz.de 49 TARGETED ONLINE ADVERTISING Itay Gonshorovitz Foundation of privacy Introduction to Web Science Slide 50 of 71 http://west.uni-koblenz.de Online behavioral advertising Online behavioral advertising refers to the practice of adnetworks tracking users across web sites in order to learn user interests and preferences. Benefits Advertisers targets a more focused audience which increases the effectively. Consumer is “bothered” by more relevant and interesting ads. Introduction to Web Science Slide 51 of 71 http://west.uni-koblenz.de 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 Search history – analyzing search keywords Browse history - analyzing content of visited pages Purchase history Social networks Geography Introduction to Web Science Slide 52 of 71 http://west.uni-koblenz.de How Ad-Networks track users Cookies 3rd Party cookies Flash cookies Web bug IP address User-agent Headers Browser + OS More than 24,000 signatures Introduction to Web Science Slide 53 of 71 http://west.uni-koblenz.de Levis.com case study Introduction to Web Science Slide 54 of 71 http://west.uni-koblenz.de Levis.com case study Introduction to Web Science Slide 55 of 71 http://west.uni-koblenz.de 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. Introduction to Web Science Slide 56 of 71 http://west.uni-koblenz.de So… What we have so far? User - Preserve his privacy Ad-Network & Publisher – Maintain targeting and preserve their effectiveness and income Still want to be able to fight click fraud Questions: Do the two goals necessarily conflict? Or can they be both achieved? Introduction to Web Science Slide 57 of 71 http://west.uni-koblenz.de Naive (paranoid) solution Surf only across anonymizing proxies. TOR Surf in private mode Advantages Effective from the user’s perspective. Disadvantages Are proxies really anonymizing? Very awkward Slower Damages targeted advertising Introduction to Web Science Slide 58 of 71 http://west.uni-koblenz.de 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: Query timing Click through behavior Introduction to Web Science Slide 59 of 71 http://west.uni-koblenz.de TrackMeNot Advantages Simple Disadvantages Still the real queries can be connected to real identity. Might have problems with offensive contents. Again, damages targeted advertising Introduction to Web Science Slide 60 of 71 http://west.uni-koblenz.de Privad (Guha, Reznichenko, Tang , et al., 2009) Require client software: saves locally database of ads (served by the ad-network) Learn user interests in order to match ads. Match add from the local database according to the User interests. Introduction to Web Science Slide 61 of 71 http://west.uni-koblenz.de Privad Introduce new party – Dealer: Proxies anonymously all communication between the user and the ad-network. might be government regulatory agency. 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. Introduction to Web Science Slide 62 of 71 http://west.uni-koblenz.de Introduction to Web Science Slide 63 of 71 http://west.uni-koblenz.de Privad Advantages Ad-Networks can still target ads without violates user privacy. Disadvantages Complicated to add the new party. Ad-Network has to trust the dealer in order to fight clickfraud which might unmotivated them to cooperate. Introduction to Web Science Slide 64 of 71 http://west.uni-koblenz.de Adnostic (Toubina, Narayanan, Boneh, et al., 2009) Two party solution: Client side: Implemented as a Firefox plugin. 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. Introduction to Web Science Slide 65 of 71 http://west.uni-koblenz.de Adnostic - Accounting “charge per click” model remains unchanged. “charge per impression” is harder. It uses homomorphic encryption scheme. given the public key and ciphertexts , anyone can calculate given the public key and ciphertexts , and scalar c, can be calculated. Introduction to Web Science Slide 66 of 71 http://west.uni-koblenz.de 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. Introduction to Web Science Slide 67 of 71 http://west.uni-koblenz.de Adnostic - charge per impression protocol Validates the proof. If the proof is valid then using homomorphic encryption calculates when c is the price of viewing the ad. 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 adnetwork. Introduction to Web Science Slide 68 of 71 http://west.uni-koblenz.de Adnostic Advantages Ad-networks can still target ads without violates user privacy. Ad-networks can still detect click fraud though it will be difficult without gathering information on IP even for a short time. Disadvantages Ad-networks become weaker. Ad-networks can still track user if they are willing to, and the protocol is built on trust. Introduction to Web Science Slide 69 of 71 http://west.uni-koblenz.de Future of Online Advertising Measurements Pricing Experimentation Other form of Advertising: TV Ads Ad Exchanges Social Ads Introduction to Web Science Slide 70 of 71 http://west.uni-koblenz.de 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. Introduction to Web Science Slide 71 of 71 http://west.uni-koblenz.de