1. The Value of Information in Real-Time Display Advertising Björn Hoppe 28. April 2015

3. Research Question and Hypothesis

4. THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING 4 Research Question What is the impact of targeted display advertisements through information on the probability of an user clicking on an ad (click-through rate)? Research Question What is the impact of targeted display advertisements through information on the probability of an user clicking on an ad (click-through rate)?

5. Research Question and Hypothesis THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING 5 Research Question What is the impact of targeted display advertisements through information on the probability of an user clicking on an ad (click-through rate)? Research Question What is the impact of targeted display advertisements through information on the probability of an user clicking on an ad (click-through rate)? Hypothesis If advertisers use information for targeting display advertisements, the probability of an user clicking on an ad will be higher. Hypothesis If advertisers use information for targeting display advertisements, the probability of an user clicking on an ad will be higher.

6. Structure of the Real-Time Display Advertising Market

7. Structure of the market (simplified) 7 Advertiser/ Agency Advertiser/ Agency Publisher THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING

8. Structure of the market (simplified) 8 Advertiser/ Agency Advertiser/ Agency Ad Exchange Publisher THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING

9. Structure of the market (simplified) 9 Advertiser/ Agency Advertiser/ Agency Ad Exchange Publisher Data Provider THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING

10. The 4 phases of real-time display advertising 10 THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING

11. Description of the Dataset

12. Three different campaigns with three different targeting objectives The dataset is divided into two user groups – targeted and untargeted 1.059.808 users that have not indicated an interest or buying intention 905.827 users that have indicated an interest and/or buying intention

13. The dataset contains the information the ad was targeted to (if at all) Click (0/1) Information Health (0/1) Fashion(0/1) Luxury(0/1) Social(0/1) Interest Buying Intention Clothing, shoes, bags(0/1) Beauty, personal care(0/1) Healthcare products(0/1) Luxury(0/1)

14. Three different campaigns with three different targeting objectives Many observations are important due to the steep “click-funnel” 0,999 0,001 Think about 1.000.000 ads shown to users (impressions)… …only about 1.000 of them will be clicked (click-through rate)

15. Calculating CTR of the two user groups reveals a counterintuitive insight CTR WITH information 0,049% WITHOUT information 0,055% 15 Surprisingly, targeting users that have indicated an interest and/or a buying intention does not seem to be beneficial. Hence, having information might not be an advantage. THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING

16. Three different campaigns with three different targeting objectives “Big Data” does not always mean “Big Information”…

17. Binary Logistic Regression

18. Logistic regression analysis Step 1: Defining the model THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING 18 Defining model

19. Logistic regression analysis Step 1: Defining the model THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING 19 Defining model Independent variables LogitOddsProb. lin. operation exp. operation log. operation

20. Ads are either wide and short or high and strait THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING 20

21. Ads are either wide and short or high and strait THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING 21 As the size of the banner influences the click-through rate we have to control for the size in our regression.

22. Logistic regression analysis Step 2: Estimating the logistic regression function Defining model Est. log. regression function THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING 22

23. 23 THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING

24. Binary logistic regression of the dataset WITHOUT information THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING 24 For every pixel increase in ad height resp. ad width the logit of the probability of a click increases by 0,0019 resp. 0,0009.

25. The probability of an user clicking on an ad is increasing in terms of ad height as well as ad width THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING 25

26. Evaluating the interaction effect of ad height and ad width 26 THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING The probability of an user clicking on an ad is increasing in terms of ad height as well as ad width. No interaction can be observed

27. Logistic regression analysis Step 3: Interpreting the regression coefficients Interpr. reg. coefficients Interpreting the operation between the estimated logistic regression function and the probability of observing a click is not easy due to the indirect effect and the non-linear operation of the Logit. Only the direction can be interpreted easily. THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING 27 Defining model Est. log. regression function

28. Logistic regression analysis Step 3: Interpreting the regression coefficients Interpr. reg. coefficients THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING 28 Defining model Est. log. regression function Each estimated coefficient is the expected change in the log odds of clicking on an ad for a unit increase in the corresponding predictor variable holding the other predictor variables constant at certain value.

29. The probability of an user clicking on an ad is 0,05% THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING 29 For the most common ad size – the „Medium Rectangle“ (300*250) – it follows:

30. Verifying model Interpr. reg. coefficients Defining model Est. log. regression function

31. Logistic regression analysis Step 4: Verifying the model THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING 31 Interpr. reg. coefficients Defining model Est. log. regression function Verifying model

32. The purpose of this analysis is descriptive rather than predictive. (However, it is still worthwhile to examine model fit.)

33. Area under the ROC curve is small 33 THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING

34. Binary logistic regression of the dataset WITH information 34 THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING

35. Binary logistic regression of the dataset WITH information 35 THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING

36. Binary logistic regression of the dataset WITH information 36 THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING

37. Binary logistic regression of the dataset WITH information 37 THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING

38. Impact of predictors on the probability of clicking 38 THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING

39. Binary logistic regression of the dataset WITH information 39 THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING For the most common ad size and a conscious fashion shopper follow:

40. If advertisers have information about users, the probability of clicking on ads will be (slightly) higher.

41. Area under the ROC curve has not changed 41 THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING

42. Predicting clicks is easy (or not possible) 42 THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING predicted observedno click Click P(y=1)>0,5 no click905.3810 click4460 predicted observedno click Click P(y=1)>0,001 no click664.171241.210 click269177 SensitivitySpecificitySensitivitySpecificity

43. Limitations

44. Department of Marketing Institute for Interactive Marketing & Social Media Welthandelsplatz 1, 1020 Vienna, Austria Björn Hoppe T +43-1-313 36-4926 bjoern.hoppe@wu.ac.at www.wu.ac.at/imsm 44 THE VALUE OF INFORMATION IN REAL-TIME DISPLAY ADVERTISING Questions!