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Web Advertisement

Web Advertisement

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Web Advertisement

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  1. Web Advertisement Rong Jin

  2. Basic Forms of Advertising • Brand advertising • Creates a distinct favorable image • Direct-marketing • Advertising that involves a "direct response”: buy, subscribe, vote, donate, etc, now or soon

  3. Examples

  4. Web Advertising There are a lot of ad. on the web …

  5. Search Advertising

  6. Display Advertising

  7. Revenue of Internet Advertising

  8. Different Forms of Internet Ad. Internet advertising revenues by format in percents - 1998-2004.

  9. Search Advertising • Advertising that is related to the web search • Becomes the driving force sustaining monetization of Web services • 63% of the advertisers want to target users according to their interests • 49% want to target users on their geographical locations • Two types • Keyword-targeted advertising • Content-targeted advertising

  10. Keyword-targeted Advertising • Introduced by Overtune in 1998 • Ads are related to the user’s queries Paid list

  11. Keyword-targeted Advertising • Advertiser • Associate ad with keywords • Bid on the keywords • The higher the bid, the greater the chances that the ad will be shown the paid list • Only pay when users click on the ad • Presentation of ads • A title, a short description, and a URL • Creative = title + short description • Buzzing word: “free”, “click here”, “enter now to win …” • Conversion: the user jump to the landing page and starts transaction

  12. Content-targeted Advertising • Ads are related to the content of triggering pages • Automated selection of ads • Assign each page with keywords and key categories • The dominant contextual approach in Web marketing. Paid list

  13. Search Advertising Business • All large players (Google, Yahoo, MSN) now own: • Web search engine • Advertising platform + network • They are both a search engine and an ad agency • Google • Own engine for keyword ads (Adwords) • Bought Applied Semantics (context ads, Adsense) • Yahoo • Bought Inktomi, AltaVista, AlltheWeb, Overture • Search: Inktomi • Ads: Overture (keyword, context) • MSN • Used search from Inktomi + own corpus. Has now its own (Bing) • Used ads from Overture, has now own platform (“AdCenter” in 2006)

  14. Search Advertising Network • Failure of search advertising in the early days • Poor matching between ads and keywords/content • Irrelevant messages annoy users • Questionable practice • popping up ads in pages without the permission of their publishers, associating their images with improper companies • Solution: search advertising network • Form a network so that all the participants are benefited

  15. Search Advertising Network Search advertising network

  16. Search Advertising Network: Broker • Responsible for the maintenance of the network. • Decide advertisers and publishers that participate of the network • Decide the publishing policies • Responsible for the auction system • Interfaces, databases, controlled vocabularies • Match ads to keywords/content

  17. Search Advertising Network Search advertising network

  18. Search Advertising Network: Advertiser • Goal: • Popularize their brands and commercialize their products and services. • Their ads are referred to quality users • Compete among themselves for keywords by bidding in an auction system. • Pay to the broker according to the traffic • Tune the parameters dynamically according to the performance report

  19. Advantages of Web Ad. • Accountability • Measure the performance and provide feedback about marketing campaigns and strategies. • Dynamically adjust their strategies • Flexibility • Target users according to their past behavior, interests, demographic information, and local information

  20. Search Advertising Network Search advertising network

  21. Search Advertising Network: Publisher • Goal: • Monetize their pages through the loyalty of their audience • Simply displaying ads will annoy most users • Search-related ads are more targeted  positive user attitude • Provide brokers with the description of their pages • Manually assigned keywords or categories • Essential topics derived automatically • Diverse publishers • search engines and online directories • Small publishers

  22. Search Advertising Network: Users • Goal: getting relevant information from the publishers. • Describing their information needs by keywords or by surfing in Web pages • Average four hundred million queries per day in 2003; 40% were of commercial nature • Occasionally click on the ads exhibited, jump to the advertisers’ pages, and start commercial transactions.

  23. Example (I) • We advertised a tutorial of WWW 2006 • Bid on keywords • www2006 $0.20 per click • “www 2006” $0.07 per click • “www conference” $0.10 per click • Prabhakar Raghavan $0.10 per click • Andrei Broder $0.07 per click

  24. Example (II)

  25. Example (III): Report

  26. Distribution of Cost Per Click (CPC) Not a Zip’s law !

  27. Challenging Questions • For advertisers • What words to buy? • How much to pay? • Arbitrage among keywords/suppliers, try to cherry pick demographics • How to fight with spam • Dilemma between the overall cost and the number of clicks • For publisher (search engines) • How to price the words? • Let the market decide: bidding! • When to place ad? (a matching problem) • How to price “extended” or “broad” match ? • Long term costs (bad user experience) • Dilemma between relevance and short term profit

  28. Find the Right Ad • Relatively simple on bid phrases • Similar to Web Search, but • Ranking depends on both bids and relevance • Each ad entry = a “small page” • Different metadata (keywords, title, URL) • Enormous historical data (billions of searches/ad/clicks records) • What about queries on which there is no bid?

  29. Find the Right Ad • Advertiser can bid on “broad queries” and/or “concept queries” • Bid on any query that contains “sigir”or the “sigir”concept. • Pitfall: partial matching • Ad: A seller of car water pumps might bid on “pumps”. • Query: “breast pumps” or “black pumps”

  30. Auction and Pricing • Advertisers compete for each keyword • Advertiser pays the search engine when a searcher clicked on a displayed ad • Challenges • Which keywords to bid? • How much to bid for each keyword? • How to adjust the biding according to the performance?

  31. Search Advertising System • D: a collection of documents (i.e., ads) • q: a user query • M: qD {0, 1} • A matching function • Decide if an ad is relevant to the query q • R: qD [0, 1] • A ranking function • Decide the relative rank of an ad to query q

  32. ad2 ad3 ad1 ad1 ad3 ad4 ad4 ad5 ad6 Search Advertising System Ad Database Query Identify relevant ads by M Miele Bid different keywords Click Data Rank ads by R Advertisers Change bid

  33. Relevance Matching • The quality of relevance matching affects the user’s perception of the web content • Exact match and approximate match • Exact match: query = ad keywords • Approximate match: partial match • Similar to typical IR methods • Query expansion to include synonyms and related terms

  34. Why? Conversion Rate vs. Query Size • Intuition: the longer the query the higher the conversion rate

  35. Conversion Rate vs. Query Size • Intuition: the longer the query the higher the conversion rate It is because most brand names are single word

  36. Conversion Rate vs. Query Size • Too specific queries Why?

  37. Ranking • Satisfy interests of different parties • Users: relevant information • Advertisers: quality traffic at a minimum cost and with a minimum risk of negative user attitude • Brokers and publishers: maximize their revenues at the minimum risk of negative user attitude

  38. Click Through Rate vs Ranking A simple strategy: always put the most expensive ads on the top (what is wrong with this idea?)

  39. Paid Placement Strategy • Rank by willingness to pay (WTP ranking) • Rank by willingness to pay times click-through rate (WTPC ranking), i.e., the product between their bids and their expected click-through rate. • The reward for a click is larger if it is received in a lower rank. Perceived Relevance Expected Profits 

  40. Fraud Detection • The more publishers have users clicking in the ads shown in their pages, the more advertisers will pay to them. • Publisher can fake traffic to attract advertisers • CompUSA spent more than $10 millions in 2004 due to fake traffic. • Characteristics of fake traffic • Distribution of clicks over time • Distribution of clicks over users • A standard classification

  41. Measurements • Advertisers need to get detailed feedback about their performance • Cost per thousand impression: • Traditional pricing model • Payments were measured mainly based on the quantity of impressions of ads • Uncertainty on the benefit of advertisers • Performance based metrics • Direct estimate of advertiser’s return of investments

  42. Measurements • User clicks may not convert • Require reliable estimate of the return of their investments (ROI). • Keyword-targeted advertising performs better than content-targeted advertising • Users are less likely to generate a conversion while surfing.