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Webinar: Survival Analysis for Marketing Attribution - July 17, 2013

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A central question in advertising is how to measure the effectiveness of different ad campaigns. In online advertising, including social media, it is possible to create thousands of different variations on an ad, and serve millions of impressions to targeted audiences each day. Rather too often, digital advertisers use the last click attribution model to evaluate the success of campaigns. In other words, when a user clicks on an ad impression, only the very last event is deemed as significant. This is convenient but doesn't help in making good marketing decisions.

Survival analysis is widely used in the modeling of living organisms and time to failure of components, but Chandler-Pepelnjak (2010) proposed to use survival analysis for marketing attribution analysis. Listen to our webinar to learn more about this theory and a big data case study, showing how DataSong used Revolution Analytics.

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Webinar: Survival Analysis for Marketing Attribution - July 17, 2013

  1. 1. Revolution Confidential Survival Analysis for Marketing Attribution Webinar July 2013 Andrie de Vries Business Services Director – Europe @RevoAndrie Revolution Analytics @RevolutionR
  2. 2. Revolution Confidential Who am I? CRAN package ggdendro StackOverflow Revolution Analytics Webinar, July 2013 2
  3. 3. Revolution Confidential From Toledo to Albacete Revolution Analytics Webinar, July 2013 3
  4. 4. Revolution Confidential Rent a car? Revolution Analytics Webinar, July 2013 4
  5. 5. Revolution Confidential … or take a bus? Revolution Analytics Webinar, July 2013 5
  6. 6. Revolution Confidential … but what’s happening here? Revolution Analytics Webinar, July 2013 6
  7. 7. Revolution Confidential Marketing attribution: the Question How to attribute conversion success to marketing spend? Where to spend the next marketing dollar? Revolution Analytics Webinar, July 2013 7
  8. 8. Revolution Confidential Agenda Digital marketing attribution Using Survival models At scale, on big data Revolution Analytics Webinar, July 2013 8
  9. 9. Revolution Confidential Agenda Digital marketing attribution Using Survival models At scale, on big data Revolution Analytics Webinar, July 2013 9
  10. 10. Revolution Confidential Two-click conversion journey Click1: Open landing page Click 2: Sign up to offer Revolution Analytics Webinar, July 2013 10
  11. 11. Revolution Confidential Typical conversion journey… Impressions • Banner ad • Page post ad • Sponsored tweet • Search ad Click • Landing page • Special offer • Application form Conversion • Sign up • Ask for more detail 11Revolution Analytics Webinar, July 2013 11
  12. 12. Revolution Confidential …but no two journeys are the same… Impressions • Banner ad • Page post ad • Sponsored tweet • Search ad Click • Landing page • Special offer • Application form Conversion • Sign up • Ask for more detail 12Revolution Analytics Webinar, July 2013 Person 2  Person 3  Person 1  12
  13. 13. Revolution Confidential Attribution models Last click only All events even Rule based Statistical modelling …so how to attribute the value? 13Revolution Analytics Webinar, July 2013 Person 2  Person 3  Person 1  13
  14. 14. Revolution Confidential Attribution with statistical modelling  Regression  In many cases, log data is available only for conversions  And when non-conversion data is available, these people may convert in near future  Revolution Analytics Webinar, July 2013 14
  15. 15. Revolution Confidential Attribution with statistical modelling  Regression  In many cases, log data is available only for conversions  And when non-conversion data is available, these people may convert in near future  Survival analysis  Use time to conversion as dependent variable  Can use each interaction (view or click) as an observation  Can include censored (incomplete) data  No need to flatten the data   Revolution Analytics Webinar, July 2013 15
  16. 16. Revolution Confidential Agenda Digital marketing attribution Using Survival models At scale, on big data Revolution Analytics Webinar, July 2013 16
  17. 17. Revolution Confidential Survival models  Kaplan-Meier survivor function  Cox proportional hazards model 𝑆 𝑘𝑚 = 𝑡𝑖<𝑡 𝑟 𝑡𝑖 − 𝑑(𝑡𝑖) 𝑟(𝑡𝑖) 𝐿(𝛽) = 𝐿𝑖(𝛽) 𝐿𝑖(𝛽) = 𝑟𝑖 𝑡∗ 𝑗 𝑌𝑗 𝑡∗ 𝑟𝑗 𝑡∗ 𝜆 𝑡; 𝑍𝑖 = 𝜆0(𝑡)𝑟𝑖(𝑡) Hazard function 𝑟𝑖 𝑡 = 𝑒 𝛽𝑍 𝑖(𝑡) Risk score Likelihood that individual i dies Partial likelihood > library(survival) > Surv(…) > library(survival) > coxph( Surv(…) ~ …) Revolution Analytics Webinar, July 2013 17
  18. 18. Revolution Confidential What is death? Revolution Analytics Webinar, July 2013 Medicine: actual death of patient Engineering: failure of component 18
  19. 19. Revolution Confidential What is death? Revolution Analytics Webinar, July 2013 For attribution: cookie conversion 19
  20. 20. Revolution Confidential Worked example Attribution of digital media for telecoms client Revolution Analytics Webinar, July 2013 20
  21. 21. Revolution Confidential Read the data > rdsFile <- "survival_data.rds" > xd <- readRDS(rdsFile) > class(xd) [1] "data.table" "data.frame" > nrow(xd) [1] 775782 > ncol(xd) [1] 31 Revolution Analytics Webinar, July 2013 21
  22. 22. Revolution Confidential What does the data look like? > xd[1:25, 1:6, with=FALSE] id Conversion.Time Event.Number Event.Time Event.Type Campaign 1: 10101:49721794 01/10/2012 00:05 1 01/10/2012 00:02 Click Free Sims 2: 10101:49721801 01/10/2012 00:05 1 29/09/2012 16:25 View BAU High Media 6: 10101:49721854 01/10/2012 00:07 3 17/09/2012 18:32 View BAU High Media 7: 10101:49721854 01/10/2012 00:07 4 17/09/2012 19:13 View BAU High Media 8: 10101:49721854 01/10/2012 00:07 5 17/09/2012 19:17 View BAU High Media 9: 10101:49721854 01/10/2012 00:07 6 17/09/2012 19:20 View BAU High Media 10: 10101:49721854 01/10/2012 00:07 7 17/09/2012 19:21 View BAU High Media 11: 10101:49721854 01/10/2012 00:07 8 17/09/2012 19:47 View BAU High Media 12: 10101:49721854 01/10/2012 00:07 9 17/09/2012 19:49 View BAU High Media 13: 10101:49721854 01/10/2012 00:07 10 17/09/2012 19:53 View BAU High Media 14: 10101:49721854 01/10/2012 00:07 11 17/09/2012 20:04 View BAU High Media 15: 10101:49721854 01/10/2012 00:07 12 18/09/2012 10:02 View BAU High Media 16: 10101:49721854 01/10/2012 00:07 13 18/09/2012 10:03 View BAU High Media 17: 10101:49721854 01/10/2012 00:07 14 18/09/2012 10:03 View BAU High Media 18: 10101:49721854 01/10/2012 00:07 15 18/09/2012 20:06 View BAU High Media 19: 10101:49721854 01/10/2012 00:07 16 18/09/2012 20:10 View BAU High Media 20: 10101:49721854 01/10/2012 00:07 17 19/09/2012 18:14 View BAU High Media 21: 10101:49721854 01/10/2012 00:07 18 19/09/2012 20:23 View BAU High Media 22: 10101:49721854 01/10/2012 00:07 19 20/09/2012 20:22 View BAU High Media 23: 10101:49721854 01/10/2012 00:07 20 22/09/2012 14:57 View BAU High Media 24: 10101:49721854 01/10/2012 00:07 21 22/09/2012 22:18 View BAU High Media 25: 10101:49721854 01/10/2012 00:07 22 23/09/2012 21:06 View BAU High Media Revolution Analytics Webinar, July 2013 22
  23. 23. Revolution Confidential Histogram of cookie lifetime Revolution Analytics Webinar, July 2013 Impressions and clicks in customer journey Cookie duration (days) Events(impressionsandclicks) 0 5 10 15 20 25 30 050000150000250000 23
  24. 24. Revolution Confidential Fitting the model Revolution Analytics Webinar, July 2013 > library(survival) > fitp <- coxph( Surv(times, event=Converted) ~ Type + Event.Type + Supplier + PrevClicks + AdFormat, data=xd) 24
  25. 25. Revolution Confidential What does the data say? AdFormat Event.Type PrevClicks Supplier Type 0 1 2 SuperSky-160x600 Leaderboard-728x90 MPU-300x250 View Click PrevClicks MexAd SpecificMedia ValueClick Ebay AOLNetwork Gumtree GoogleDisplayNetwork FacebookAPI PaymonthlySIM ContractPhone SIMonly Exponentiatedcoefficient Revolution Analytics Webinar, July 2013 25
  26. 26. Revolution Confidential AdFormat Event.Type PrevClicks Supplier Type 0 1 2 SuperSky-160x600 Leaderboard-728x90 MPU-300x250 View Click PrevClicks MexAd SpecificMedia ValueClick Ebay AOLNetwork Gumtree GoogleDisplayNetwork FacebookAPI PaymonthlySIM ContractPhone SIMonly ExponentiatedcoefficientWhat does the data say? • Advertise the right product! • Some suppliers are better at generating conversion • But note the data wasn’t an unbiased experiment! Revolution Analytics Webinar, July 2013 26
  27. 27. Revolution Confidential Estimated hazard function Revolution Analytics Webinar, July 2013 > x <- survfit(fitp) > xx <- with(x, data.frame(time, surv, upper, lower)) > ggplot(xx, aes(time, surv)) + geom_step() … 27
  28. 28. Revolution Confidential Agenda Digital marketing attribution Using Survival models At scale, on big data Revolution Analytics Webinar, July 2013 28
  29. 29. Revolution Confidential Where Revolution helps Import • Text formats • SAS • High-speed database • Hadoop Pre- process • DataStep • Clean • Refactor • Sort • Merge Analyse • Cube • Summarise • Parallelise (rxExec) Model • Regression • GLM • Tweedie • Clustering • Decision trees Score • Predict Deploy • Web services Confidential to Revolution Analytics 29 Revolution R Enterprise Parallel external memory algorithms (PEMAs)
  30. 30. Revolution Confidential Case study: Datasong  Profile:  Multi-channel marketing analytics  Software developer and service provider  Growing, innovative, cost-conscious  Technology: Revolution Analytics Webinar, July 2013 30
  31. 31. Revolution Confidential Modeling the Baseline Hazard Revolution Analytics Webinar, July 2013 Capture nonlinear trends in baseline, while overlaying marketing treatment variables as well as other customer attributes Revolution R package used: • RevoScaleR Revolution R functions used: • rxImport() • rxSummary() • rxCube() • rxLogit() • rxPredict() • rxRoc() 31
  32. 32. Revolution Confidential Transformations Revolution Analytics Webinar, July 2013 Catalog Email 32
  33. 33. Revolution Confidential Outcome  Massively scalable infrastructure  Attribution and optimization at individual customer level for clients such as Williams-Sonoma  Client saved $250K in one campaign  Rapid deployment of customer-specific models  Innovative techniques, e.g. GAM Survival models  Performance improvement  Experienced 4x performance improvement on 50 million records Revolution Analytics Webinar, July 2013 33
  34. 34. Revolution Confidential 34 www.revolutionanalytics.com Twitter: @RevolutionR The leading commercial provider of software and support for the popular open source R statistics language. Revolution Analytics Webinar, July 2013

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