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# "Economics of Web Design" by Yury Lifshits

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### "Economics of Web Design" by Yury Lifshits

1. 1. Economics of Web-Design Yury Lifshits Caltech http://yury.name CS Club, Steklov Institute, 2008 1 / 35
2. 2. A camel is a horse designed by a committee Vogue’1958 2 / 35
3. 3. A camel is a horse designed by a committee Vogue’1958 Less is more 2 / 35
4. 4. A camel is a horse designed by a committee Vogue’1958 Less is more 1855, Robert Browning (English poet) 2 / 35
5. 5. Outline 1 Anti-AdWords Theorem 3 / 35
6. 6. Outline 1 Anti-AdWords Theorem 2 Dynamic Design 3 / 35
7. 7. Outline 1 Anti-AdWords Theorem 2 Dynamic Design 3 Hit Counter Project Proposal 3 / 35
8. 8. 1 Anti-AdWords Theorem 4 / 35
9. 9. Problem Statement Given 10 organic search results and 5 sponsored results place them on 15 slots on a webpage 5 / 35
10. 10. Math model (1/2) Every link i: Price pi Relevance ri 6 / 35
11. 11. Math model (1/2) Every link i: Price pi Relevance ri Attractiveness ai 6 / 35
12. 12. Math model (1/2) Every link i: Price pi Relevance ri Attractiveness ai Every slot j: Attention share sj 6 / 35
13. 13. Math Model (2/2) Matching M: Place every link i to slot j = M(i) 7 / 35
14. 14. Math Model (2/2) Matching M: Place every link i to slot j = M(i) User clicks on link i with probability proportional to sM(i) + ai 7 / 35
15. 15. Payoffs Expected relevance: 8 / 35
16. 16. Payoffs Expected relevance: 15 Rel(M) = (sM(i) + ai) · ri i=1 8 / 35
17. 17. Payoffs Expected relevance: 15 Rel(M) = (sM(i) + ai) · ri i=1 Expected revenue: 8 / 35
18. 18. Payoffs Expected relevance: 15 Rel(M) = (sM(i) + ai) · ri i=1 Expected revenue: 15 Rev(M) = (sM(i) + ai) · pi i=1 8 / 35
19. 19. Payoffs Expected relevance: 15 Rel(M) = (sM(i) + ai) · ri i=1 Expected revenue: 15 Rev(M) = (sM(i) + ai) · pi i=1 Attractiveness is not important 8 / 35
20. 20. Pareto-Optimal Matching M is Pareto-Optimal iff there is no M such that Rel(M) < Rel(M ), Rev(M) < Rev(M ) 9 / 35
21. 21. Pareto-Optimal Matching M is Pareto-Optimal iff there is no M such that Rel(M) < Rel(M ), Rev(M) < Rev(M ) Otherwise M is stupid 9 / 35
22. 22. Example (1/2) Attention Link Relevance Price 50% O1 2 - 40% A1 0 25\$ 10% A2 1 20\$ 1.1 12\$ 10 / 35
23. 23. Example (2/2) Attention Link Relevance Price 50% A2 1 20\$ 40% O1 2 - 10% A1 0 25\$ 1.3 12.5\$ 11 / 35
24. 24. Anti-AdWords Left — organic, right — advertising 12 / 35
25. 25. Anti-AdWords Left — organic, right — advertising Under presented model AdWords design can be stupid 12 / 35
26. 26. Anti-AdWords Left — organic, right — advertising Under presented model AdWords design can be stupid That is, another presentation can improve both relevance and revenue 12 / 35
27. 27. Notation Page attention structure P: 20% 18% 15% . . . 4% 2% 13 / 35
28. 28. Notation Page attention structure P: 20% 18% 15% . . . 4% 2% Fixed design D: Ad slots, organic slots 20% 18% 15% . . . 4% 2% 13 / 35
29. 29. Anti-AdWords Theorem ∀P ∀∗ D ∃ links L such that any matching M ∈ D is stupid for L 14 / 35
30. 30. Alternative to AdWords Single list for sponsored and organic results 15 / 35
31. 31. Alternative to AdWords Single list for sponsored and organic results Tradeoff ranking 15 / 35
32. 32. Further questions Do we need to keep 10 Organic guarantee? 16 / 35
33. 33. Further questions Do we need to keep 10 Organic guarantee? Adwords redundancy? 16 / 35
34. 34. Further questions Do we need to keep 10 Organic guarantee? Adwords redundancy? Quantify possible Adwords inprovements 16 / 35
35. 35. Further questions Do we need to keep 10 Organic guarantee? Adwords redundancy? Quantify possible Adwords inprovements Find all rankings on a trade-off curve 16 / 35
36. 36. 2 Dynamic Design 17 / 35
37. 37. Increase Key Numbers Owners utility User utility 18 / 35
38. 38. Key Numbers for Owner 19 / 35
39. 39. Key Numbers for Owner Ad revenue Number of users Volume of content Amount of feedback User time online Brand recognition 19 / 35
40. 40. Key Numbers for User 20 / 35
41. 41. Key Numbers for User # of matches (dating) # of job offers # of answers (QA) # interesting stories (news) # of compliments (social networks) 20 / 35
42. 42. Design = tool for increasing key numbers 21 / 35
43. 43. Main Challenge 22 / 35
44. 44. Main Challenge Quantify Design 22 / 35
45. 45. Design Decisions What to display With what priorities? 23 / 35
46. 46. Quantify Internal Priorities Attention budgets and auctions 24 / 35
47. 47. Dynamic Design Choices based on opportunity (user, action, time) knowledge 25 / 35
48. 48. Design Mechanisms 26 / 35
49. 49. Design Mechanisms Chosen by owner Fresh User voted Random Personalized Paid 26 / 35
50. 50. Minimalism? Design complexity = # of links Links with CTR < .001 are worse than ads One random big instead many static small 27 / 35
51. 51. Links to kill Amazon: “Add to wedding registry” Google: “Report yourself” Yahoo: “Horoscopes” 28 / 35
52. 52. Django MTV Tricks Template inheritance If/for loops in templates Exceptions 29 / 35
53. 53. 3 Hit Counter Project Proposal 30 / 35
54. 54. Hit Analysis Easy: incoming hits Hard: outgoing hits 31 / 35
55. 55. Outgoing Hits Ajax solution 32 / 35
56. 56. Outgoing Hits Ajax solution Can be blocked 32 / 35
57. 57. Outgoing Hits Ajax solution Can be blocked Inverted index 32 / 35
58. 58. Outgoing Hits Ajax solution Can be blocked Inverted index 32 / 35
59. 59. Outgoing Hits Ajax solution Can be blocked Inverted index Internal hits only Goto 32 / 35
60. 60. Outgoing Hits Ajax solution Can be blocked Inverted index Internal hits only Goto Bad for SEO 32 / 35
61. 61. Call for Feedback Volunteers for hit counter project? 33 / 35
62. 62. Links http://yury.name Homepage http://yury.name/newweb.html Tutorial “The New Web” http://yury.name/reputation.html Tutorial “Reputation Systems” 34 / 35
63. 63. Thanks for your attention! Questions? 35 / 35