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Measuring the Effects of Advertising: The Digital Frontier* - PowerPoint PPT Presentation


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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. Introduction. advertising is a $200+ billion per-year industry

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Measuring the Effects of Advertising: The Digital Frontier*


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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