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  1. Measuring the Effects of Advertising: The Digital Frontier* Randall A. Lewis, Google, Inc. Justin M. Rao, Microsoft Research David Reiley, Google, Inc. * Opinions expressed are our own, not our huge employers

  2. Introduction

  3. advertising is a $200+ billion per-year industry (~1.5-2.0% of GDP)

  4. supports “free” services that constitute a majority of American’s leisure time

  5. yet effects of advertising are poorly understood

  6. ~$100,000 per month

  7. ~$25,000,000 per year

  8. ~$3-5,000,000 per year

  9. ~$10-25 CPM

  10. ~$1-8 CPM

  11. I have no idea

  12. impact of these ads?

  13. digital measurement era two key advances

  14. 1) ad delivery and purchase data can be linked at individual level

  15. 2) ad delivery can be randomized essential exogenous variation to measure causal effects

  16. plan for the talk

  17. what methods should we use to measure adfx?

  18. where can things go wrong?

  19. what type of precision can we expect?

  20. we’ll go through a case study

  21. what metrics are used to measure adfx?

  22. what metrics are should be used to measure adfx?

  23. what metrics are should be used to measure adfx?

  24. specific biases that can arise in online settings

  25. specific biases that can arise in online settings

  26. what’s in reach? what’s out of reach?

  27. what’s in reach? what’s out of reach?

  28. how does computational advertising help?

  29. Estimating the Causal Effects of Advertising

  30. a few useful facts on advertising

  31. average American sees ~$1.35 worth of ads per day

  32. universe of advertisers needs to net $1.35 in marginal profits

  33. universe of advertisers needs to net $1.35 $5-6 in marginal profits incremental sales

  34. (to break-even)

  35. any given campaign will be a small fraction of daily ad exposure

  36. with this in mind…

  37. 25 display advertising field experiments run at Yahoo!

  38. experiment: exposure determined by flip of coin

  39. results here taken from Lewis and Rao (2012)

  40. data sharing gives us sales records paired with ad delivery

  41. Notation: : an individual =sales for person (online + offline) =1 if is treated with firm’s ad =0 if is treated with placebo ad : vector of covariates (we’ll ignore for now, all results go through by just adding “condition on ”)

  42. Regression: average sales difference between exposed (E) and unexposed (U) groups

  43. Experimental study: E: treatment group U: control group Observational study: E: endogenously exposed U: pseudo control

  44. =s.d. of sales at individual level =treatment-control (sales impact)

  45. let’s calibrate with medians from the 19 retail sales experiments

  46. retailers ranging from budget to high-end

  47. (weekly) = (weekly) cost= $0.14 per customer (20-100) ads @$1-5 CPM ROI goal=25%  increase sales by $0.35 (based on margins)

  48. goal is to increase baseline sales by 5%

  49. problem: standard deviation is 10x the mean