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Revolution Confidential
Survival Analysis for
Marketing Attribution
Webinar
July 2013
Andrie de Vries
Business Services Director – Europe
@RevoAndrie
Revolution Analytics
@RevolutionR
Revolution Confidential
Who am I?
CRAN package ggdendro
StackOverflow
Revolution Analytics Webinar, July 2013 2
Revolution Confidential
From Toledo to Albacete
Revolution Analytics Webinar, July 2013 3
Revolution Confidential
Rent a car?
Revolution Analytics Webinar, July 2013 4
Revolution Confidential
… or take a bus?
Revolution Analytics Webinar, July 2013 5
Revolution Confidential
… but what’s happening here?
Revolution Analytics Webinar, July 2013 6
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
Revolution Confidential
Agenda
Digital marketing attribution
Using Survival models
At scale, on big data
Revolution Analytics Webinar, July 2013 8
Revolution Confidential
Agenda
Digital marketing attribution
Using Survival models
At scale, on big data
Revolution Analytics Webinar, July 2013 9
Revolution Confidential
Two-click conversion journey
Click1:
Open landing page
Click 2:
Sign up to offer
Revolution Analytics Webinar, July 2013 10
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
11
Revolution Analytics Webinar, July 2013 11
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
12
Revolution Analytics Webinar, July 2013
Person 2 
Person 3 
Person 1 
12
Revolution Confidential
Attribution models
Last click only
All events even
Rule based
Statistical modelling
…so how to attribute the value?
13
Revolution Analytics Webinar, July 2013
Person 2 
Person 3 
Person 1 
13
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
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
Revolution Confidential
Agenda
Digital marketing attribution
Using Survival models
At scale, on big data
Revolution Analytics Webinar, July 2013 16
Revolution Confidential
Survival models
 Kaplan-Meier survivor function
 Cox proportional hazards model
𝑆𝑘𝑚 =
𝑡𝑖<𝑡
𝑟 𝑡𝑖 − 𝑑(𝑡𝑖)
𝑟(𝑡𝑖)
𝐿(𝛽) = 𝐿𝑖(𝛽)
𝐿𝑖(𝛽) =
𝑟𝑖 𝑡∗
𝑗 𝑌
𝑗 𝑡∗ 𝑟𝑗 𝑡∗
𝜆 𝑡; 𝑍𝑖 = 𝜆0(𝑡)𝑟𝑖(𝑡) Hazard function
𝑟𝑖 𝑡 = 𝑒𝛽𝑍𝑖(𝑡) Risk score
Likelihood that
individual i dies
Partial likelihood
> library(survival)
> Surv(…)
> library(survival)
> coxph( Surv(…) ~ …)
Revolution Analytics Webinar, July 2013 17
Revolution Confidential
What is death?
Revolution Analytics Webinar, July 2013
Medicine: actual death of patient
Engineering: failure of component
18
Revolution Confidential
What is death?
Revolution Analytics Webinar, July 2013
For attribution: cookie conversion
19
Revolution Confidential
Worked example
Attribution of digital media for
telecoms client
Revolution Analytics Webinar, July 2013 20
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
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
Revolution Confidential
Histogram of cookie lifetime
Revolution Analytics Webinar, July 2013
Impressions and clicks in customer journey
Cookie duration (days)
Events
(impressions
and
clicks)
0 5 10 15 20 25 30
0
50000
150000
250000
23
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
Revolution Confidential
What does the data say?
AdFormat Event.Type PrevClicks Supplier Type
0
1
2
Super
Sky-160x600
Leaderboard-
728x90
MPU-300x250
View
Click
PrevClicks
MexAd
Specific
Media
ValueClick
Ebay
AOL
Network
Gumtree
Google
Display
Network
Facebook
API
Pay
monthly
SIM
Contract
Phone
SIM
only
Exponentiated
coefficient
Revolution Analytics Webinar, July 2013 25
Revolution Confidential
AdFormat Event.Type PrevClicks Supplier Type
0
1
2
Super
Sky-160x600
Leaderboard-
728x90
MPU-300x250
View
Click
PrevClicks
MexAd
Specific
Media
ValueClick
Ebay
AOL
Network
Gumtree
Google
Display
Network
Facebook
API
Pay
monthly
SIM
Contract
Phone
SIM
only
Exponentiated
coefficient
What 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
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
Revolution Confidential
Agenda
Digital marketing attribution
Using Survival models
At scale, on big data
Revolution Analytics Webinar, July 2013 28
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)
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
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
Revolution Confidential
Transformations
Revolution Analytics Webinar, July 2013
Catalog Email
32
Revolution Confidential
Outcome
 Massively scalable infrastructure
 Attribution and optimization at individual customer level for clients
such as Williams-Sonoma and Nordstrom
 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
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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Webinar: Survival Analysis for Marketing Attribution - July 17, 2013

  • 1. Revolution Confidential Survival Analysis for Marketing Attribution Webinar July 2013 Andrie de Vries Business Services Director – Europe @RevoAndrie Revolution Analytics @RevolutionR
  • 2. Revolution Confidential Who am I? CRAN package ggdendro StackOverflow Revolution Analytics Webinar, July 2013 2
  • 3. Revolution Confidential From Toledo to Albacete Revolution Analytics Webinar, July 2013 3
  • 4. Revolution Confidential Rent a car? Revolution Analytics Webinar, July 2013 4
  • 5. Revolution Confidential … or take a bus? Revolution Analytics Webinar, July 2013 5
  • 6. Revolution Confidential … but what’s happening here? Revolution Analytics Webinar, July 2013 6
  • 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. Revolution Confidential Agenda Digital marketing attribution Using Survival models At scale, on big data Revolution Analytics Webinar, July 2013 8
  • 9. Revolution Confidential Agenda Digital marketing attribution Using Survival models At scale, on big data Revolution Analytics Webinar, July 2013 9
  • 10. Revolution Confidential Two-click conversion journey Click1: Open landing page Click 2: Sign up to offer Revolution Analytics Webinar, July 2013 10
  • 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 11 Revolution Analytics Webinar, July 2013 11
  • 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 12 Revolution Analytics Webinar, July 2013 Person 2  Person 3  Person 1  12
  • 13. Revolution Confidential Attribution models Last click only All events even Rule based Statistical modelling …so how to attribute the value? 13 Revolution Analytics Webinar, July 2013 Person 2  Person 3  Person 1  13
  • 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. 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. Revolution Confidential Agenda Digital marketing attribution Using Survival models At scale, on big data Revolution Analytics Webinar, July 2013 16
  • 17. Revolution Confidential Survival models  Kaplan-Meier survivor function  Cox proportional hazards model 𝑆𝑘𝑚 = 𝑡𝑖<𝑡 𝑟 𝑡𝑖 − 𝑑(𝑡𝑖) 𝑟(𝑡𝑖) 𝐿(𝛽) = 𝐿𝑖(𝛽) 𝐿𝑖(𝛽) = 𝑟𝑖 𝑡∗ 𝑗 𝑌 𝑗 𝑡∗ 𝑟𝑗 𝑡∗ 𝜆 𝑡; 𝑍𝑖 = 𝜆0(𝑡)𝑟𝑖(𝑡) Hazard function 𝑟𝑖 𝑡 = 𝑒𝛽𝑍𝑖(𝑡) Risk score Likelihood that individual i dies Partial likelihood > library(survival) > Surv(…) > library(survival) > coxph( Surv(…) ~ …) Revolution Analytics Webinar, July 2013 17
  • 18. Revolution Confidential What is death? Revolution Analytics Webinar, July 2013 Medicine: actual death of patient Engineering: failure of component 18
  • 19. Revolution Confidential What is death? Revolution Analytics Webinar, July 2013 For attribution: cookie conversion 19
  • 20. Revolution Confidential Worked example Attribution of digital media for telecoms client Revolution Analytics Webinar, July 2013 20
  • 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. 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. Revolution Confidential Histogram of cookie lifetime Revolution Analytics Webinar, July 2013 Impressions and clicks in customer journey Cookie duration (days) Events (impressions and clicks) 0 5 10 15 20 25 30 0 50000 150000 250000 23
  • 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. Revolution Confidential What does the data say? AdFormat Event.Type PrevClicks Supplier Type 0 1 2 Super Sky-160x600 Leaderboard- 728x90 MPU-300x250 View Click PrevClicks MexAd Specific Media ValueClick Ebay AOL Network Gumtree Google Display Network Facebook API Pay monthly SIM Contract Phone SIM only Exponentiated coefficient Revolution Analytics Webinar, July 2013 25
  • 26. Revolution Confidential AdFormat Event.Type PrevClicks Supplier Type 0 1 2 Super Sky-160x600 Leaderboard- 728x90 MPU-300x250 View Click PrevClicks MexAd Specific Media ValueClick Ebay AOL Network Gumtree Google Display Network Facebook API Pay monthly SIM Contract Phone SIM only Exponentiated coefficient What 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. 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. Revolution Confidential Agenda Digital marketing attribution Using Survival models At scale, on big data Revolution Analytics Webinar, July 2013 28
  • 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. 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. 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
  • 33. Revolution Confidential Outcome  Massively scalable infrastructure  Attribution and optimization at individual customer level for clients such as Williams-Sonoma and Nordstrom  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. 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