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Statistical Challenges in Online Advertising Deepak Agarwal Deepayan Chakrabarti (Yahoo! Research)

Explore the multi-billion-dollar online advertising industry, its growth, key drivers, and the emerging discipline of Computational Advertising. Dive into revenue models, auction mechanisms, and user interaction with ads. Understand the diverse statistical challenges and opportunities in the realm of online advertising.

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Statistical Challenges in Online Advertising Deepak Agarwal Deepayan Chakrabarti (Yahoo! Research)

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  1. Statistical Challenges in Online AdvertisingDeepak AgarwalDeepayan Chakrabarti(Yahoo! Research)

  2. Online Advertising • Multi-billion dollar industry, high growth • $9.7B in 2006 (17% increase), total $150B • Why this will continue? • Broadband cheap, ubiquitous • “Getting things done” easier on the internet • Advertisers shifting dollars • Why does it work? • Massive scale, automated, low marginal cost • Key: Monetize more and better, “learn from data” • New discipline “Computational Advertising”

  3. What is “Computational Advertising”? New scientific sub-discipline, at the intersection of • Large scale search and text analysis • Information retrieval • Statistical modeling • Machine learning • Optimization • Microeconomics

  4. Online advertising: 6000 ft Overview Pick ads Ads Advertisers Ad Network Content User Examples:Yahoo, Google, MSN, RightMedia, … Content Provider

  5. Outline • Background on online advertising • Sponsored Search, Content Match, Display, Unified marketplace • The Fundamental Problem • Statistical sub-problems: • Description • Existing methods • Challenges

  6. Different flavors Online Advertising Revenue Models Misc. Ad exchanges Advertising Setting CPM CPC CPA Sponsored Search Display Content Match

  7. Revenue Models CPM CPC CPA Cost Per iMpression Ad Network Pick ads Ads Advertisers Content $$ User $ Content Provider

  8. Revenue Models CPM CPC CPA Cost Per Click Ad Network click Pick ads Ads Advertisers Content $$ User $ Content Provider

  9. Revenue Models Advertiser landing page Cost Per Action CPM CPC CPA Ad Network click Pick ads Ads Advertisers Content $$ User $ Content Provider

  10. Revenue Models • Example: Suppose we show an ad N times on the same spot • Under CPM: Revenue = N * CPM • Under CPC: Revenue = N * CTR * CPC CPM CPC CPA Depends on auction mechanism Click-through Rate(probability of a click given an impression)

  11. Auction Mechanism • Revenue depends on type of auction • Generalized First-price: • CPC = bid on clicked ad • Generalized Second-price: • CPC = bid of ad below clicked ad (or the reserve price) • CPC could be modified by additional factors • [Optimal Auction Design in a Multi-Unit Environment: The Case of Sponsored Search Auctions] by Edelman+/2006 • [Internet Advertising and the Generalized Second Price Auction…] by Edelman+/2006

  12. Revenue Models • Example: Suppose we show an ad N times on the same spot • Under CPM: Revenue = N * CPM • Under CPC: Revenue = N * CTR * CPC • Under CPA: Revenue = N * CTR * Conv. Rate * CPA CPM CPC CPA Conversion Rate(probability of a user conversion on the advertiser’s landing page given a click)

  13. Revenue Models CPM website traffic CPC website traffic +ad relevance CPA website traffic +ad relevance +landing page quality Revenue dependence Relevance to advertisers Prices and Bids Ease of picking ads

  14. Background Online Advertising Revenue Models Misc. Ad exchanges Advertising Setting CPM CPC CPA Sponsored Search Display Content Match

  15. Advertising Setting Pick ads Ads Advertisers Ad Network Content • What do you show the user? • How does the user interact with the ad system? User Content Provider

  16. Advertising Setting Sponsored Search Display Content Match

  17. Advertising Setting Sponsored Search Display Content Match Pick ads

  18. Advertising Setting • Graphical display ads • Mostly for brand awareness • Revenue model is typically CPM Sponsored Search Display Content Match

  19. Advertising Setting Sponsored Search Display Content Match Content match ad

  20. Advertising Setting Sponsored Search Display Content Match Text ads Pick ads Match ads to the content

  21. Advertising Setting • The user intent is unclear • Revenue model is typically CPC • Query (webpage) is long and noisy Sponsored Search Display Content Match

  22. Advertising Setting Sponsored Search Display Content Match Search Query Sponsored Search Ads

  23. Advertising Setting Sponsored Search Display Content Match Pick ads Text ads Search Query Match ads to the query

  24. Advertising Setting • User “declares” his/her intention • Click rates generally higher than for Content Match • Revenue model is typically CPC (recently some CPA) • Query is short and less noisy than Content Match Sponsored Search Display Content Match

  25. Summary • Different revenue models • Depends on the goal of the advertiser campaign • Brand awareness • Display advertising • Pay per impression (CPM) • Attracting users to advertised product • Content Match, Sponsored Search • Pay per click (CPC), Pay per action (CPA)

  26. Background Online Advertising Revenue Models Misc. Ad exchanges Advertising Setting CPM CPC CPA Sponsored Search Display Content Match

  27. Unified Marketplace • Publishers, Ad-networks, advertisers participate together in a singe exchange • Publishers put impressions in the exchange; advertisers/ad-networks bid for it • CPM, CPC, CPA are all integrated into a single auction mechanism

  28. Overview: The Open Exchange Bids $0.75 via Network… Bids $0.50 Bids $0.60 Ad.com AdSense Bids $0.65—WINS! Has ad impression to sell -- AUCTIONS … which becomes $0.45 bid Transparency and value

  29. Unified scale: Expected CPM • Campaigns are CPC, CPA, CPM • They may all participate in an auction together • Converting to a common denomination is a challenge

  30. Outline • Background on online advertising • The Fundamental Problem • Statistical sub-problems: • Description • Existing methods • Challenges

  31. Outline • Background on online advertising • The Fundamental Problem • Display advertising • Sponsored Search and Content Match • Statistical sub-problems: • Description • Existing methods • Challenges

  32. Display Advertising

  33. Does it work? • Lewis and Reiley, 2009. Retail Advertising Works! Yahoo! Technical Report • Controlled experiment assigning customers to treatment and control groups for a large retailer • Advertising significantly improved purchases (online and offline) • Good news! Main goal is brand awareness • Revenue model typically CPM, advertiser takes all the risk

  34. Display adverting: Buyer and Seller • Advertiser (Buyer) • Buy ad space well in advance or in spot market • May buy from publisher who may guarantee or not • Typically, guaranteed contracts cost more, price higher • Publisher (seller) • Sell in advance (guaranteed) or sell in spot market

  35. Display Advertising • Main goal of advertisers: Brand Awareness • Revenue Model: Primarily Cost per impression (CPM) • Traditional Advertising Model: • Ads are targeted at particular demographics (user characteristics) • GM ads on Y! autos shown to “males above 55” • Mortgage ad shown to “everybody on Y! Front page” • Book a slot well in advance • “2M impressions in Jan next year” • These future impressions must be guaranteed by the ad network

  36. Display Advertising • Fundamental Problem: Guarantee impressions to advertisers • Predict Supply: • How many impressions will be available? • Demographics overlap • Predict Demand: • How much will advertisers want each demographic? Young US 2 1 4 3 2 2 1 Y! Mail Female

  37. Display Advertising Fundamental Problem: Guarantee impressions to advertisers si xij dj • Predict Supply • Predict Demand • Find the optimal allocation • subject to supply and demand constraints Young US 2 1 4 3 2 2 1 Y! Mail Female

  38. Display Advertising Fundamental Problem: Guarantee impressions to advertisers Predict Supply Predict Demand Find the optimal allocation, subject to constraints Optimal in terms of what objective function? Depends on the goal of the ad-network Advertisers don’t specify too many targets Dangerous for ad-networks to allow that (difficult to forecast) BUT they expect good inventory (campaign should result in good conversion rates)

  39. Allocation through Optimization si supply demand xij dj • Optimal in terms of what objective function? • E.g. Maximize value of remaining inventory • Cherry-picks valuable inventory, saves it for later • Fairness • “Spreads the wealth” subject to constraints

  40. Example Supply Pools Young US, Y, nFSupply = 2Price = 1 US Demand 2 1 4 3 US & Y(2) 2 2 US, Y, FSupply = 3Price = 5 1 Y! Mail Female Supply Pools How should we distribute impressions from the supply pools to satisfy this demand?

  41. Cherry-picking:Fulfill demands at least cost Example (Cherry-picking) Supply Pools US, Y, nFSupply = 2Price = 1 Demand (2) US & Y(2) US, Y, FSupply = 3Price = 5 How should we distribute impressions from the supply pools to satisfy this demand?

  42. Cherry-picking:Fulfill demands at least cost Fairness:Equitable distribution of available supply pools Example (Fairness) Supply Pools US, Y, nFSupply = 2Cost = 1 Demand (1) US & Y(2) (1) US, Y, FSupply = 3Cost = 5 How should we distribute impressions from the supply pools to satisfy this demand?

  43. Example of an objective function

  44. Display Advertising Fundamental Problem: Guarantee impressions to advertisers Predict Supply Predict Demand Find the optimal allocation, subject to constraints Pick the right objective function Further issues: Risk Management: Supply and demand forecasts should have both mean and variance Forecast aggregation: Forecasts may be needed over multiple resolutions, in time and in demographics Challenging time series problem Adapting system to external events

  45. Display Advertising • Fundamental Problem: Guarantee impressions to advertisers • Predict Supply • Predict Demand • Find the optimal allocation, subject to constraints • Pick the right objective function • Forecasting accuracy is critical! • Overshoot  under-delivery of impressions  unhappy advertisers • Undershoot  loss in revenue

  46. Outline • Background on online advertising • The Fundamental Problem • Display advertising • Sponsored Search and Content Match • Statistical sub-problems: • Description • Existing methods • Challenges

  47. Sponsored Search and Content Match • Given a query: • Select the top-k ads to be shown on the k slots to maximize total expected revenue • What is total expected revenue?

  48. Example (Content Match) Ad Position 1 Ad Position 2 Ad Position 3

  49. Example (Content Match)

  50. Reminder: Auction Mechanism • Revenue depends on type of auction • Generalized First-price: • CPC = bid on clicked ad • Generalized Second-price: • CPC = bid of ad below clicked ad (or the reserve price) • CPC could be modified by additional factors • Total expected revenue = revenue obtained in a given time window • [Optimal Auction Design in a Multi-Unit Environment: The Case of Sponsored Search Auctions] by Edelman+/2006 • [Internet Advertising and the Generalized Second Price Auction…] by Edelman+/2006

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