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. Search Advertising System Miele ad 1 ad 2 ad 3 ad 4 ad 5 ad 6 Query Ad Database ad 1 ad 3 ad 4 Identify relevant ads by M Rank ads by R Advertisers Click Data Change bid Bid different keywords

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

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 Perceived Relevance Expected Profits   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.

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.