Online Advertising and Ad Auctions at Google Vahab Mirrokni



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  • Vahab Mirrokni
  • Google Research, New York
  • Traditional Advertising
  • At the beginning: Traditional Ads
  • 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
  • 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
  • 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.
  • Banner Ads
  • Pay-Per-Impression
  • Pay-per-1000 impressions (PPM): advertiser pays each time ad is displayed
    • Models existing standards from magazine, radio, television
    • Main business model for banner ads to date
    • Corresponds to inventory host sells
  • Exposes advertiser to risk of fluctuations in market
  • Barrier to entry for small advertisers
    • Contracts negotiated on a case-by-case basis with large minimums (typically, a few thousand dollars per month)
  • Sponsored Search Ads
  • Pay-Per-Click
  • 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
  • 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
  • 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-through-rate (CTR), or probability user clicks on ad
    • Upon a click, advertiser is charged minimum amount required to maintain position in ranking
  • Bidding Process
  • 4
  • Keyword Selection
  • 3
  • 2
  • 1
  • “You don’t get it, Daddy, because they’re not targeting you.”
  • Bidding Process
  • 4
  • Targeting Populations
  • Advert Creation
  • Keyword Selection
  • Bids and Budget
  • “Here it is – the plain unvarnished truth. Varnish it.”
  • 3
  • 2
  • 1
  • Ad title
  • Ad text
  • Display url
  • Bidding Process
  • 4
  • Targeting Populations
  • Advert Creation
  • Keyword Selection
  • Bids and Budget
  • “Now, that’s product placement!”
  • 3
  • 2
  • 1
  • Bidding Process
  • 4
  • Targeting Populations
  • Advert Creation
  • Keyword Selection
  • Bids and Budget
  • 3
  • 2
  • 1
  • Daily Budget
  • 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
  • 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
  • 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:
  • 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
  • Typical Parameters
  • 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
  • PPC of most popular searches in Google, 4/06
  • Typical Parameters
  • Keyword
  • Top Bid
  • 2nd Bid
  • mesothelioma
  • $100
  • $100
  • $100
  • $52
  • vioxx attorney
  • $38
  • $38
  • student loan consolidation
  • $29
  • $9
  • Bids on some valuable keywords
  • CTRs are typically around 1%
  • Other Important issues in ad auctions
  • Avoiding click fraud
  • Bidding with budget constraints
  • Externalities between advertisers
  • User search models
  • Measurement: Information
  • Adwords FrontEnd: Bid Simulations
    • Clicks and Cost for other bids.
  • Google Analytics
    • Traffic Patterns, Site visitors.
  • Search insights:
    • Search Patterns
  • Interest-Based Advertising
    • Indicate your interests so that you get more relevant ads
  • AdWords FrontEnd
  • Web Analytics
  • Re-acting to Metrics
  • Distinguish Causality and Correlation.
  • Experimentation:
  • Repeated experimentation:
    • Continuous Improvement (Multi-armed bandit)
  • Other Online Advertising Aspects
  • Google Ad Systems:
    • Sponsored Search: AdWord Auctions.
    • Contextual Ads (AdSense) & Display Ads (DoubleClick)
    • Ad Exchange
    • Social Ads, YouTube, TV ads.
  • Bid Management & Campaign Optimization for Advertisers
    • Short-term vs. Long-term effect of ads.
  • Planning: Ad Auctions & Ad Reservations.
    • Stochastic/Dynamic Inventory Planning
    • Pricing: Auctions vs Contracts
  • Ad Serving
    • Online Stochastic Assignment Problems
  • 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
  • Future of Online Advertising
  • Measurements
  • Pricing
  • Experimentation
  • Other form of Advertising:
  • Homework
  • Students will write an essay to compare between traditional advertising and advertising on the web.
  • Length: 1000 to 1500 words (no bargain)
  • Deadline: 7th April. Late or no-submission will result to a zero.
  • Any kind of plagiarisms will result to a zero.


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