SlideShare uma empresa Scribd logo

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

•
•
Revolution Analytics
Revolution Analytics

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.

TecnologiaNotícias e política
1 de 34
Baixar para ler offline
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

Recomendados

IBM Watson Overview
IBM Watson OverviewIBM Watson Overview
IBM Watson OverviewPenn State EdTech Network
 
Introduction To Data Science
Introduction To Data ScienceIntroduction To Data Science
Introduction To Data ScienceSpotle.ai
 
Machine Learning in Cyber Security
Machine Learning in Cyber SecurityMachine Learning in Cyber Security
Machine Learning in Cyber SecurityRishi Kant
 
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Simplilearn
 
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...Kishor Datta Gupta
 
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckAI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
 
Applying Statistical Modeling and Machine Learning to Perform Time-Series For...
Applying Statistical Modeling and Machine Learning to Perform Time-Series For...Applying Statistical Modeling and Machine Learning to Perform Time-Series For...
Applying Statistical Modeling and Machine Learning to Perform Time-Series For...PyData
 
A/B testing at Spotify
A/B testing at SpotifyA/B testing at Spotify
A/B testing at SpotifyAli Sarrafi
 

Recomendados

IBM Watson Overview
IBM Watson OverviewIBM Watson Overview
IBM Watson OverviewPenn State EdTech Network
 
Introduction To Data Science
Introduction To Data ScienceIntroduction To Data Science
Introduction To Data ScienceSpotle.ai
 
Machine Learning in Cyber Security
Machine Learning in Cyber SecurityMachine Learning in Cyber Security
Machine Learning in Cyber SecurityRishi Kant
 
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...
Supervised and Unsupervised Learning In Machine Learning | Machine Learning T...Simplilearn
 
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...
Deep Reinforcement Learning based Recommendation with Explicit User-ItemInter...Kishor Datta Gupta
 
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckAI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
 
Applying Statistical Modeling and Machine Learning to Perform Time-Series For...
Applying Statistical Modeling and Machine Learning to Perform Time-Series For...Applying Statistical Modeling and Machine Learning to Perform Time-Series For...
Applying Statistical Modeling and Machine Learning to Perform Time-Series For...PyData
 
A/B testing at Spotify
A/B testing at SpotifyA/B testing at Spotify
A/B testing at SpotifyAli Sarrafi
 
What is Machine Learning | Introduction to Machine Learning | Machine Learnin...
What is Machine Learning | Introduction to Machine Learning | Machine Learnin...What is Machine Learning | Introduction to Machine Learning | Machine Learnin...
What is Machine Learning | Introduction to Machine Learning | Machine Learnin...Simplilearn
 
Missing values in recommender models
Missing values in recommender modelsMissing values in recommender models
Missing values in recommender modelsParmeshwar Khurd
 
Application of Machine Learning in Cyber Security
Application of Machine Learning in Cyber SecurityApplication of Machine Learning in Cyber Security
Application of Machine Learning in Cyber SecurityDr. Umesh Rao.Hodeghatta
 
Machine learning
Machine learningMachine learning
Machine learningDr Geetha Mohan
 
Introduction to the ethics of machine learning
Introduction to the ethics of machine learningIntroduction to the ethics of machine learning
Introduction to the ethics of machine learningDaniel Wilson
 
Introduction to Data Science
Introduction to Data ScienceIntroduction to Data Science
Introduction to Data ScienceANOOP V S
 
Deep Learning - The Past, Present and Future of Artificial Intelligence
Deep Learning - The Past, Present and Future of Artificial IntelligenceDeep Learning - The Past, Present and Future of Artificial Intelligence
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
 
Data science
Data scienceData science
Data scienceMohamed Loey
 
Data Science Project Lifecycle and Skill Set
Data Science Project Lifecycle and Skill SetData Science Project Lifecycle and Skill Set
Data Science Project Lifecycle and Skill SetIDEAS - Int'l Data Engineering and Science Association
 
Bias in AI
Bias in AIBias in AI
Bias in AIZeydy Ortiz, Ph. D.
 
Unified Approach to Interpret Machine Learning Model: SHAP + LIME
Unified Approach to Interpret Machine Learning Model: SHAP + LIMEUnified Approach to Interpret Machine Learning Model: SHAP + LIME
Unified Approach to Interpret Machine Learning Model: SHAP + LIMEDatabricks
 
ppt on machine learning to deep learning (1).pptx
ppt on machine learning to deep learning (1).pptxppt on machine learning to deep learning (1).pptx
ppt on machine learning to deep learning (1).pptxAnweshaGarima
 
Raskar 2012, Idea Hexagon
Raskar 2012, Idea HexagonRaskar 2012, Idea Hexagon
Raskar 2012, Idea HexagonCamera Culture Group, MIT Media Lab
 
Machine Learning Ml Overview Algorithms Use Cases And Applications
Machine Learning Ml Overview Algorithms Use Cases And ApplicationsMachine Learning Ml Overview Algorithms Use Cases And Applications
Machine Learning Ml Overview Algorithms Use Cases And ApplicationsSlideTeam
 
Federated Learning: ML with Privacy on the Edge 11.15.18
Federated Learning: ML with Privacy on the Edge 11.15.18Federated Learning: ML with Privacy on the Edge 11.15.18
Federated Learning: ML with Privacy on the Edge 11.15.18Cloudera, Inc.
 
Natural Language Generation: New Automation and Personalization Opportunities
Natural Language Generation: New Automation and Personalization OpportunitiesNatural Language Generation: New Automation and Personalization Opportunities
Natural Language Generation: New Automation and Personalization OpportunitiesAutomated Insights
 
Data science presentation
Data science presentationData science presentation
Data science presentationMSDEVMTL
 
A Friendly Introduction to Machine Learning
A Friendly Introduction to Machine LearningA Friendly Introduction to Machine Learning
A Friendly Introduction to Machine LearningHaptik
 
Recent Trends in Personalization at Netflix
Recent Trends in Personalization at NetflixRecent Trends in Personalization at Netflix
Recent Trends in Personalization at NetflixJustin Basilico
 
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Simplilearn
 
How to Build an Attribution Solution in 1 Day
How to Build an Attribution Solution in 1 DayHow to Build an Attribution Solution in 1 Day
How to Build an Attribution Solution in 1 DayPhillip Law
 
How To Make Your Marketing Match Your Reality (#mozcon 2015)
How To Make Your Marketing Match Your Reality (#mozcon 2015)How To Make Your Marketing Match Your Reality (#mozcon 2015)
How To Make Your Marketing Match Your Reality (#mozcon 2015)Dana DiTomaso
 

Mais conteúdo relacionado

Mais procurados

What is Machine Learning | Introduction to Machine Learning | Machine Learnin...
What is Machine Learning | Introduction to Machine Learning | Machine Learnin...What is Machine Learning | Introduction to Machine Learning | Machine Learnin...
What is Machine Learning | Introduction to Machine Learning | Machine Learnin...Simplilearn
 
Missing values in recommender models
Missing values in recommender modelsMissing values in recommender models
Missing values in recommender modelsParmeshwar Khurd
 
Application of Machine Learning in Cyber Security
Application of Machine Learning in Cyber SecurityApplication of Machine Learning in Cyber Security
Application of Machine Learning in Cyber SecurityDr. Umesh Rao.Hodeghatta
 
Introduction to the ethics of machine learning
Introduction to the ethics of machine learningIntroduction to the ethics of machine learning
Introduction to the ethics of machine learningDaniel Wilson
 
Introduction to Data Science
Introduction to Data ScienceIntroduction to Data Science
Introduction to Data ScienceANOOP V S
 
Deep Learning - The Past, Present and Future of Artificial Intelligence
Deep Learning - The Past, Present and Future of Artificial IntelligenceDeep Learning - The Past, Present and Future of Artificial Intelligence
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
 
Unified Approach to Interpret Machine Learning Model: SHAP + LIME
Unified Approach to Interpret Machine Learning Model: SHAP + LIMEUnified Approach to Interpret Machine Learning Model: SHAP + LIME
Unified Approach to Interpret Machine Learning Model: SHAP + LIMEDatabricks
 
ppt on machine learning to deep learning (1).pptx
ppt on machine learning to deep learning (1).pptxppt on machine learning to deep learning (1).pptx
ppt on machine learning to deep learning (1).pptxAnweshaGarima
 
Machine Learning Ml Overview Algorithms Use Cases And Applications
Machine Learning Ml Overview Algorithms Use Cases And ApplicationsMachine Learning Ml Overview Algorithms Use Cases And Applications
Machine Learning Ml Overview Algorithms Use Cases And ApplicationsSlideTeam
 
Federated Learning: ML with Privacy on the Edge 11.15.18
Federated Learning: ML with Privacy on the Edge 11.15.18Federated Learning: ML with Privacy on the Edge 11.15.18
Federated Learning: ML with Privacy on the Edge 11.15.18Cloudera, Inc.
 
Natural Language Generation: New Automation and Personalization Opportunities
Natural Language Generation: New Automation and Personalization OpportunitiesNatural Language Generation: New Automation and Personalization Opportunities
Natural Language Generation: New Automation and Personalization OpportunitiesAutomated Insights
 
Data science presentation
Data science presentationData science presentation
Data science presentationMSDEVMTL
 
A Friendly Introduction to Machine Learning
A Friendly Introduction to Machine LearningA Friendly Introduction to Machine Learning
A Friendly Introduction to Machine LearningHaptik
 
Recent Trends in Personalization at Netflix
Recent Trends in Personalization at NetflixRecent Trends in Personalization at Netflix
Recent Trends in Personalization at NetflixJustin Basilico
 
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Simplilearn
 

Mais procurados (20)

What is Machine Learning | Introduction to Machine Learning | Machine Learnin...
What is Machine Learning | Introduction to Machine Learning | Machine Learnin...What is Machine Learning | Introduction to Machine Learning | Machine Learnin...
What is Machine Learning | Introduction to Machine Learning | Machine Learnin...
 
Missing values in recommender models
Missing values in recommender modelsMissing values in recommender models
Missing values in recommender models
 
Application of Machine Learning in Cyber Security
Application of Machine Learning in Cyber SecurityApplication of Machine Learning in Cyber Security
Application of Machine Learning in Cyber Security
 
Machine learning
Machine learningMachine learning
Machine learning
 
Introduction to the ethics of machine learning
Introduction to the ethics of machine learningIntroduction to the ethics of machine learning
Introduction to the ethics of machine learning
 
Introduction to Data Science
Introduction to Data ScienceIntroduction to Data Science
Introduction to Data Science
 
Deep Learning - The Past, Present and Future of Artificial Intelligence
Deep Learning - The Past, Present and Future of Artificial IntelligenceDeep Learning - The Past, Present and Future of Artificial Intelligence
Deep Learning - The Past, Present and Future of Artificial Intelligence
 
Data science
Data scienceData science
Data science
 
Data Science Project Lifecycle and Skill Set
Data Science Project Lifecycle and Skill SetData Science Project Lifecycle and Skill Set
Data Science Project Lifecycle and Skill Set
 
Bias in AI
Bias in AIBias in AI
Bias in AI
 
Unified Approach to Interpret Machine Learning Model: SHAP + LIME
Unified Approach to Interpret Machine Learning Model: SHAP + LIMEUnified Approach to Interpret Machine Learning Model: SHAP + LIME
Unified Approach to Interpret Machine Learning Model: SHAP + LIME
 
ppt on machine learning to deep learning (1).pptx
ppt on machine learning to deep learning (1).pptxppt on machine learning to deep learning (1).pptx
ppt on machine learning to deep learning (1).pptx
 
Raskar 2012, Idea Hexagon
Raskar 2012, Idea HexagonRaskar 2012, Idea Hexagon
Raskar 2012, Idea Hexagon
 
Machine Learning Ml Overview Algorithms Use Cases And Applications
Machine Learning Ml Overview Algorithms Use Cases And ApplicationsMachine Learning Ml Overview Algorithms Use Cases And Applications
Machine Learning Ml Overview Algorithms Use Cases And Applications
 
Federated Learning: ML with Privacy on the Edge 11.15.18
Federated Learning: ML with Privacy on the Edge 11.15.18Federated Learning: ML with Privacy on the Edge 11.15.18
Federated Learning: ML with Privacy on the Edge 11.15.18
 
Natural Language Generation: New Automation and Personalization Opportunities
Natural Language Generation: New Automation and Personalization OpportunitiesNatural Language Generation: New Automation and Personalization Opportunities
Natural Language Generation: New Automation and Personalization Opportunities
 
Data science presentation
Data science presentationData science presentation
Data science presentation
 
A Friendly Introduction to Machine Learning
A Friendly Introduction to Machine LearningA Friendly Introduction to Machine Learning
A Friendly Introduction to Machine Learning
 
Recent Trends in Personalization at Netflix
Recent Trends in Personalization at NetflixRecent Trends in Personalization at Netflix
Recent Trends in Personalization at Netflix
 
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
Support Vector Machine - How Support Vector Machine works | SVM in Machine Le...
 

Destaque

How to Build an Attribution Solution in 1 Day
How to Build an Attribution Solution in 1 DayHow to Build an Attribution Solution in 1 Day
How to Build an Attribution Solution in 1 DayPhillip Law
 
How To Make Your Marketing Match Your Reality (#mozcon 2015)
How To Make Your Marketing Match Your Reality (#mozcon 2015)How To Make Your Marketing Match Your Reality (#mozcon 2015)
How To Make Your Marketing Match Your Reality (#mozcon 2015)Dana DiTomaso
 
Multi touch attribution & attribution modeling - GAUC Sydney Melbourne - 2013...
Multi touch attribution & attribution modeling - GAUC Sydney Melbourne - 2013...Multi touch attribution & attribution modeling - GAUC Sydney Melbourne - 2013...
Multi touch attribution & attribution modeling - GAUC Sydney Melbourne - 2013...Vinoaj Vijeyakumaar
 
Red Door Interactive: Contribution-Attribution-Mix, Oh My! Creating Content f...
Red Door Interactive: Contribution-Attribution-Mix, Oh My! Creating Content f...Red Door Interactive: Contribution-Attribution-Mix, Oh My! Creating Content f...
Red Door Interactive: Contribution-Attribution-Mix, Oh My! Creating Content f...AMASanDiego
 
Digital Attribution Modeling Using Apache Spark-(Anny Chen and William Yan, A...
Digital Attribution Modeling Using Apache Spark-(Anny Chen and William Yan, A...Digital Attribution Modeling Using Apache Spark-(Anny Chen and William Yan, A...
Digital Attribution Modeling Using Apache Spark-(Anny Chen and William Yan, A...Spark Summit
 
Introduction to Google Analytics
Introduction to Google AnalyticsIntroduction to Google Analytics
Introduction to Google AnalyticsDana DiTomaso
 
Markov model for the online multichannel attribution problem
Markov model for the online multichannel attribution problemMarkov model for the online multichannel attribution problem
Markov model for the online multichannel attribution problemadavide1982
 
Attribution Modeling - Case Study
Attribution Modeling - Case StudyAttribution Modeling - Case Study
Attribution Modeling - Case StudyConcur
 
Attribution Modeling and Big Data, Google
Attribution Modeling and Big Data, GoogleAttribution Modeling and Big Data, Google
Attribution Modeling and Big Data, GoogleInnovation Enterprise
 
GAUC 2017 Workshop Attribution with Google Analytics: Peter Falcone (Google)
GAUC 2017 Workshop Attribution with Google Analytics: Peter Falcone (Google)GAUC 2017 Workshop Attribution with Google Analytics: Peter Falcone (Google)
GAUC 2017 Workshop Attribution with Google Analytics: Peter Falcone (Google)e-dialog GmbH
 
Advanced attribution model
Advanced attribution model Advanced attribution model
Advanced attribution model Aspa Lekka
 
Operational Attribution with Google Analytics
Operational Attribution with Google AnalyticsOperational Attribution with Google Analytics
Operational Attribution with Google AnalyticsJonathan Breton
 
Webinar: Improve Campaign Results with Multi-Channel Funnels and Acquisio Att...
Webinar: Improve Campaign Results with Multi-Channel Funnels and Acquisio Att...Webinar: Improve Campaign Results with Multi-Channel Funnels and Acquisio Att...
Webinar: Improve Campaign Results with Multi-Channel Funnels and Acquisio Att...Acquisio
 
Attribution Super Modeling with Google Analytics
Attribution Super Modeling with Google AnalyticsAttribution Super Modeling with Google Analytics
Attribution Super Modeling with Google Analyticselliottkoppel
 
Marketing Attribution 101: Understanding Attribution and Calculating Cost of ...
Marketing Attribution 101: Understanding Attribution and Calculating Cost of ...Marketing Attribution 101: Understanding Attribution and Calculating Cost of ...
Marketing Attribution 101: Understanding Attribution and Calculating Cost of ...Kissmetrics on SlideShare
 

Destaque (16)

How to Build an Attribution Solution in 1 Day
How to Build an Attribution Solution in 1 DayHow to Build an Attribution Solution in 1 Day
How to Build an Attribution Solution in 1 Day
 
How To Make Your Marketing Match Your Reality (#mozcon 2015)
How To Make Your Marketing Match Your Reality (#mozcon 2015)How To Make Your Marketing Match Your Reality (#mozcon 2015)
How To Make Your Marketing Match Your Reality (#mozcon 2015)
 
Multi touch attribution & attribution modeling - GAUC Sydney Melbourne - 2013...
Multi touch attribution & attribution modeling - GAUC Sydney Melbourne - 2013...Multi touch attribution & attribution modeling - GAUC Sydney Melbourne - 2013...
Multi touch attribution & attribution modeling - GAUC Sydney Melbourne - 2013...
 
Red Door Interactive: Contribution-Attribution-Mix, Oh My! Creating Content f...
Red Door Interactive: Contribution-Attribution-Mix, Oh My! Creating Content f...Red Door Interactive: Contribution-Attribution-Mix, Oh My! Creating Content f...
Red Door Interactive: Contribution-Attribution-Mix, Oh My! Creating Content f...
 
Digital Attribution Modeling Using Apache Spark-(Anny Chen and William Yan, A...
Digital Attribution Modeling Using Apache Spark-(Anny Chen and William Yan, A...Digital Attribution Modeling Using Apache Spark-(Anny Chen and William Yan, A...
Digital Attribution Modeling Using Apache Spark-(Anny Chen and William Yan, A...
 
Introduction to Google Analytics
Introduction to Google AnalyticsIntroduction to Google Analytics
Introduction to Google Analytics
 
Web Analytics Attribution
Web Analytics AttributionWeb Analytics Attribution
Web Analytics Attribution
 
Markov model for the online multichannel attribution problem
Markov model for the online multichannel attribution problemMarkov model for the online multichannel attribution problem
Markov model for the online multichannel attribution problem
 
Attribution Modeling - Case Study
Attribution Modeling - Case StudyAttribution Modeling - Case Study
Attribution Modeling - Case Study
 
Attribution Modeling and Big Data, Google
Attribution Modeling and Big Data, GoogleAttribution Modeling and Big Data, Google
Attribution Modeling and Big Data, Google
 
GAUC 2017 Workshop Attribution with Google Analytics: Peter Falcone (Google)
GAUC 2017 Workshop Attribution with Google Analytics: Peter Falcone (Google)GAUC 2017 Workshop Attribution with Google Analytics: Peter Falcone (Google)
GAUC 2017 Workshop Attribution with Google Analytics: Peter Falcone (Google)
 
Advanced attribution model
Advanced attribution model Advanced attribution model
Advanced attribution model
 
Operational Attribution with Google Analytics
Operational Attribution with Google AnalyticsOperational Attribution with Google Analytics
Operational Attribution with Google Analytics
 
Webinar: Improve Campaign Results with Multi-Channel Funnels and Acquisio Att...
Webinar: Improve Campaign Results with Multi-Channel Funnels and Acquisio Att...Webinar: Improve Campaign Results with Multi-Channel Funnels and Acquisio Att...
Webinar: Improve Campaign Results with Multi-Channel Funnels and Acquisio Att...
 
Attribution Super Modeling with Google Analytics
Attribution Super Modeling with Google AnalyticsAttribution Super Modeling with Google Analytics
Attribution Super Modeling with Google Analytics
 
Marketing Attribution 101: Understanding Attribution and Calculating Cost of ...
Marketing Attribution 101: Understanding Attribution and Calculating Cost of ...Marketing Attribution 101: Understanding Attribution and Calculating Cost of ...
Marketing Attribution 101: Understanding Attribution and Calculating Cost of ...
 

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

Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data DecisionsBetter Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data DecisionsProduct School
 
Mobile First - med Google
Mobile First - med GoogleMobile First - med Google
Mobile First - med GoogleQuisma Denmark
 
Big Data & Analytics (Conceptual and Practical Introduction)
Big Data & Analytics (Conceptual and Practical Introduction)Big Data & Analytics (Conceptual and Practical Introduction)
Big Data & Analytics (Conceptual and Practical Introduction)Yaman Hajja, Ph.D.
 
K1 embedding big data & analytics into the business to deliver sustainable value
K1 embedding big data & analytics into the business to deliver sustainable valueK1 embedding big data & analytics into the business to deliver sustainable value
K1 embedding big data & analytics into the business to deliver sustainable valueDr. Wilfred Lin (Ph.D.)
 
1505 Statistical Thinking course extract
1505 Statistical Thinking course extract1505 Statistical Thinking course extract
1505 Statistical Thinking course extractJefferson Lynch
 
a journey in making sense of big data (part 2: KPI)
a journey in making sense of big data (part 2: KPI)a journey in making sense of big data (part 2: KPI)
a journey in making sense of big data (part 2: KPI)Patrick Deglon
 
The Distraction Economy: why scalable UX is the next revolution
The Distraction Economy: why scalable UX is the next revolutionThe Distraction Economy: why scalable UX is the next revolution
The Distraction Economy: why scalable UX is the next revolutionKurtis Morrison
 
Data Analysis - Making Big Data Work
Data Analysis - Making Big Data WorkData Analysis - Making Big Data Work
Data Analysis - Making Big Data WorkDavid Chiu
 
20151008 REx Predictive presentation v 1 0 - distributed
20151008 REx Predictive presentation v 1 0 - distributed20151008 REx Predictive presentation v 1 0 - distributed
20151008 REx Predictive presentation v 1 0 - distributedJefferson Lynch
 
201505 Statistical Thinking course extract
201505 Statistical Thinking course extract201505 Statistical Thinking course extract
201505 Statistical Thinking course extractJefferson Lynch
 
6 Steps to Building the Ultimate Integrated Marketing Framework with Productb...
6 Steps to Building the Ultimate Integrated Marketing Framework with Productb...6 Steps to Building the Ultimate Integrated Marketing Framework with Productb...
6 Steps to Building the Ultimate Integrated Marketing Framework with Productb...saastr
 
A primer on optimization using solvers
A primer on optimization using solversA primer on optimization using solvers
A primer on optimization using solversAnwar Ali Mohamed
 
ISM Silicon Valley Webinar Spend Analysis Key to Purchasing Success
ISM Silicon Valley Webinar Spend Analysis Key to Purchasing SuccessISM Silicon Valley Webinar Spend Analysis Key to Purchasing Success
ISM Silicon Valley Webinar Spend Analysis Key to Purchasing SuccessBill Kohnen
 
Intro to Data Analytics with Oscar's Director of Product
Intro to Data Analytics with Oscar's Director of Product Intro to Data Analytics with Oscar's Director of Product
Intro to Data Analytics with Oscar's Director of ProductProduct School
 
How to Use Data for Product Decisions by YouTube Product Manager
How to Use Data for Product Decisions by YouTube Product ManagerHow to Use Data for Product Decisions by YouTube Product Manager
How to Use Data for Product Decisions by YouTube Product ManagerProduct School
 
GAUC 2017 Closing Keynote: David Sneddon (Google)
GAUC 2017 Closing Keynote: David Sneddon (Google)GAUC 2017 Closing Keynote: David Sneddon (Google)
GAUC 2017 Closing Keynote: David Sneddon (Google)e-dialog GmbH
 
Build a Foundation for Data Integrity with Analytics Auditing
Build a Foundation for Data Integrity with Analytics AuditingBuild a Foundation for Data Integrity with Analytics Auditing
Build a Foundation for Data Integrity with Analytics AuditingTinuiti
 
Big Data for the CMO
Big Data for the CMOBig Data for the CMO
Big Data for the CMOBruno Aziza
 

Semelhante a Webinar: Survival Analysis for Marketing Attribution - July 17, 2013 (20)

Better Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data DecisionsBetter Living Through Analytics - Strategies for Data Decisions
Better Living Through Analytics - Strategies for Data Decisions
 
Mobile First - med Google
Mobile First - med GoogleMobile First - med Google
Mobile First - med Google
 
Big Data & Analytics (Conceptual and Practical Introduction)
Big Data & Analytics (Conceptual and Practical Introduction)Big Data & Analytics (Conceptual and Practical Introduction)
Big Data & Analytics (Conceptual and Practical Introduction)
 
K1 embedding big data & analytics into the business to deliver sustainable value
K1 embedding big data & analytics into the business to deliver sustainable valueK1 embedding big data & analytics into the business to deliver sustainable value
K1 embedding big data & analytics into the business to deliver sustainable value
 
1505 Statistical Thinking course extract
1505 Statistical Thinking course extract1505 Statistical Thinking course extract
1505 Statistical Thinking course extract
 
a journey in making sense of big data (part 2: KPI)
a journey in making sense of big data (part 2: KPI)a journey in making sense of big data (part 2: KPI)
a journey in making sense of big data (part 2: KPI)
 
Analytics and Creativity
Analytics and CreativityAnalytics and Creativity
Analytics and Creativity
 
The Distraction Economy: why scalable UX is the next revolution
The Distraction Economy: why scalable UX is the next revolutionThe Distraction Economy: why scalable UX is the next revolution
The Distraction Economy: why scalable UX is the next revolution
 
Data Analysis - Making Big Data Work
Data Analysis - Making Big Data WorkData Analysis - Making Big Data Work
Data Analysis - Making Big Data Work
 
Steps
StepsSteps
Steps
 
20151008 REx Predictive presentation v 1 0 - distributed
20151008 REx Predictive presentation v 1 0 - distributed20151008 REx Predictive presentation v 1 0 - distributed
20151008 REx Predictive presentation v 1 0 - distributed
 
201505 Statistical Thinking course extract
201505 Statistical Thinking course extract201505 Statistical Thinking course extract
201505 Statistical Thinking course extract
 
6 Steps to Building the Ultimate Integrated Marketing Framework with Productb...
6 Steps to Building the Ultimate Integrated Marketing Framework with Productb...6 Steps to Building the Ultimate Integrated Marketing Framework with Productb...
6 Steps to Building the Ultimate Integrated Marketing Framework with Productb...
 
A primer on optimization using solvers
A primer on optimization using solversA primer on optimization using solvers
A primer on optimization using solvers
 
ISM Silicon Valley Webinar Spend Analysis Key to Purchasing Success
ISM Silicon Valley Webinar Spend Analysis Key to Purchasing SuccessISM Silicon Valley Webinar Spend Analysis Key to Purchasing Success
ISM Silicon Valley Webinar Spend Analysis Key to Purchasing Success
 
Intro to Data Analytics with Oscar's Director of Product
Intro to Data Analytics with Oscar's Director of Product Intro to Data Analytics with Oscar's Director of Product
Intro to Data Analytics with Oscar's Director of Product
 
How to Use Data for Product Decisions by YouTube Product Manager
How to Use Data for Product Decisions by YouTube Product ManagerHow to Use Data for Product Decisions by YouTube Product Manager
How to Use Data for Product Decisions by YouTube Product Manager
 
GAUC 2017 Closing Keynote: David Sneddon (Google)
GAUC 2017 Closing Keynote: David Sneddon (Google)GAUC 2017 Closing Keynote: David Sneddon (Google)
GAUC 2017 Closing Keynote: David Sneddon (Google)
 
Build a Foundation for Data Integrity with Analytics Auditing
Build a Foundation for Data Integrity with Analytics AuditingBuild a Foundation for Data Integrity with Analytics Auditing
Build a Foundation for Data Integrity with Analytics Auditing
 
Big Data for the CMO
Big Data for the CMOBig Data for the CMO
Big Data for the CMO
 

Mais de Revolution Analytics

Speeding up R with Parallel Programming in the Cloud
Speeding up R with Parallel Programming in the CloudSpeeding up R with Parallel Programming in the Cloud
Speeding up R with Parallel Programming in the CloudRevolution Analytics
 
Migrating Existing Open Source Machine Learning to Azure
Migrating Existing Open Source Machine Learning to AzureMigrating Existing Open Source Machine Learning to Azure
Migrating Existing Open Source Machine Learning to AzureRevolution Analytics
 
Speed up R with parallel programming in the Cloud
Speed up R with parallel programming in the CloudSpeed up R with parallel programming in the Cloud
Speed up R with parallel programming in the CloudRevolution Analytics
 
Predicting Loan Delinquency at One Million Transactions per Second
Predicting Loan Delinquency at One Million Transactions per SecondPredicting Loan Delinquency at One Million Transactions per Second
Predicting Loan Delinquency at One Million Transactions per SecondRevolution Analytics
 
The Value of Open Source Communities
The Value of Open Source CommunitiesThe Value of Open Source Communities
The Value of Open Source CommunitiesRevolution Analytics
 
Building a scalable data science platform with R
Building a scalable data science platform with RBuilding a scalable data science platform with R
Building a scalable data science platform with RRevolution Analytics
 
The Business Economics and Opportunity of Open Source Data Science
The Business Economics and Opportunity of Open Source Data ScienceThe Business Economics and Opportunity of Open Source Data Science
The Business Economics and Opportunity of Open Source Data ScienceRevolution Analytics
 
Taking R Analytics to SQL and the Cloud
Taking R Analytics to SQL and the CloudTaking R Analytics to SQL and the Cloud
Taking R Analytics to SQL and the CloudRevolution Analytics
 
The Network structure of R packages on CRAN & BioConductor
The Network structure of R packages on CRAN & BioConductorThe Network structure of R packages on CRAN & BioConductor
The Network structure of R packages on CRAN & BioConductorRevolution Analytics
 
The network structure of cran 2015 07-02 final
The network structure of cran 2015 07-02 finalThe network structure of cran 2015 07-02 final
The network structure of cran 2015 07-02 finalRevolution Analytics
 
Simple Reproducibility with the checkpoint package
Simple Reproducibility with the checkpoint packageSimple Reproducibility with the checkpoint package
Simple Reproducibility with the checkpoint packageRevolution Analytics
 

Mais de Revolution Analytics (20)

Speeding up R with Parallel Programming in the Cloud
Speeding up R with Parallel Programming in the CloudSpeeding up R with Parallel Programming in the Cloud
Speeding up R with Parallel Programming in the Cloud
 
Migrating Existing Open Source Machine Learning to Azure
Migrating Existing Open Source Machine Learning to AzureMigrating Existing Open Source Machine Learning to Azure
Migrating Existing Open Source Machine Learning to Azure
 
R in Minecraft
R in Minecraft R in Minecraft
R in Minecraft
 
The case for R for AI developers
The case for R for AI developersThe case for R for AI developers
The case for R for AI developers
 
Speed up R with parallel programming in the Cloud
Speed up R with parallel programming in the CloudSpeed up R with parallel programming in the Cloud
Speed up R with parallel programming in the Cloud
 
The R Ecosystem
The R EcosystemThe R Ecosystem
The R Ecosystem
 
R Then and Now
R Then and NowR Then and Now
R Then and Now
 
Predicting Loan Delinquency at One Million Transactions per Second
Predicting Loan Delinquency at One Million Transactions per SecondPredicting Loan Delinquency at One Million Transactions per Second
Predicting Loan Delinquency at One Million Transactions per Second
 
Reproducible Data Science with R
Reproducible Data Science with RReproducible Data Science with R
Reproducible Data Science with R
 
The Value of Open Source Communities
The Value of Open Source CommunitiesThe Value of Open Source Communities
The Value of Open Source Communities
 
The R Ecosystem
The R EcosystemThe R Ecosystem
The R Ecosystem
 
R at Microsoft (useR! 2016)
R at Microsoft (useR! 2016)R at Microsoft (useR! 2016)
R at Microsoft (useR! 2016)
 
Building a scalable data science platform with R
Building a scalable data science platform with RBuilding a scalable data science platform with R
Building a scalable data science platform with R
 
R at Microsoft
R at MicrosoftR at Microsoft
R at Microsoft
 
The Business Economics and Opportunity of Open Source Data Science
The Business Economics and Opportunity of Open Source Data ScienceThe Business Economics and Opportunity of Open Source Data Science
The Business Economics and Opportunity of Open Source Data Science
 
Taking R Analytics to SQL and the Cloud
Taking R Analytics to SQL and the CloudTaking R Analytics to SQL and the Cloud
Taking R Analytics to SQL and the Cloud
 
The Network structure of R packages on CRAN & BioConductor
The Network structure of R packages on CRAN & BioConductorThe Network structure of R packages on CRAN & BioConductor
The Network structure of R packages on CRAN & BioConductor
 
The network structure of cran 2015 07-02 final
The network structure of cran 2015 07-02 finalThe network structure of cran 2015 07-02 final
The network structure of cran 2015 07-02 final
 
Simple Reproducibility with the checkpoint package
Simple Reproducibility with the checkpoint packageSimple Reproducibility with the checkpoint package
Simple Reproducibility with the checkpoint package
 
R at Microsoft
R at MicrosoftR at Microsoft
R at Microsoft
 

Último

QCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architecturesQCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architecturesBernd Ruecker
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxLoriGlavin3
 
Testing tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesTesting tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesKari Kakkonen
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxLoriGlavin3
 
Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024TopCSSGallery
 
Emixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native developmentEmixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native developmentPim van der Noll
 
UiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPathCommunity
 
A Framework for Development in the AI Age
A Framework for Development in the AI AgeA Framework for Development in the AI Age
A Framework for Development in the AI AgeCprime
 
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Mark Goldstein
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.Curtis Poe
 
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS: 6 Ways to Automate Your Data IntegrationBridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integrationmarketing932765
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxLoriGlavin3
 
Decarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a realityDecarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a realityIES VE
 
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxThe Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxLoriGlavin3
 
Generative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdfGenerative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdfIngrid Airi González
 
Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...
Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...
Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...Nikki Chapple
 
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesHow to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesThousandEyes
 
Connecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfConnecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfNeo4j
 
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfSo einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfpanagenda
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 

Último (20)

QCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architecturesQCon London: Mastering long-running processes in modern architectures
QCon London: Mastering long-running processes in modern architectures
 
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptxThe Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
The Role of FIDO in a Cyber Secure Netherlands: FIDO Paris Seminar.pptx
 
Testing tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesTesting tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examples
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
 
Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024Top 10 Hubspot Development Companies in 2024
Top 10 Hubspot Development Companies in 2024
 
Emixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native developmentEmixa Mendix Meetup 11 April 2024 about Mendix Native development
Emixa Mendix Meetup 11 April 2024 about Mendix Native development
 
UiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to Hero
 
A Framework for Development in the AI Age
A Framework for Development in the AI AgeA Framework for Development in the AI Age
A Framework for Development in the AI Age
 
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
 
How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.How AI, OpenAI, and ChatGPT impact business and software.
How AI, OpenAI, and ChatGPT impact business and software.
 
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS: 6 Ways to Automate Your Data IntegrationBridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
Bridging Between CAD & GIS: 6 Ways to Automate Your Data Integration
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
 
Decarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a realityDecarbonising Buildings: Making a net-zero built environment a reality
Decarbonising Buildings: Making a net-zero built environment a reality
 
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptxThe Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
The Fit for Passkeys for Employee and Consumer Sign-ins: FIDO Paris Seminar.pptx
 
Generative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdfGenerative Artificial Intelligence: How generative AI works.pdf
Generative Artificial Intelligence: How generative AI works.pdf
 
Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...
Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...
Microsoft 365 Copilot: How to boost your productivity with AI – Part one: Ado...
 
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesHow to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
 
Connecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdfConnecting the Dots for Information Discovery.pdf
Connecting the Dots for Information Discovery.pdf
 
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdfSo einfach geht modernes Roaming fuer Notes und Nomad.pdf
So einfach geht modernes Roaming fuer Notes und Nomad.pdf
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 

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