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Revolution Confidential
Marketing Analytics with R:Lifting Campaign Success Rates
London June 7th , 2013
Neil Miller Managing Director InternationalAndrie de Vries Business Services Director Europe
Revolution Confidential
Introductions and welcome
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Andrie de VriesBusiness Services Director, Europe
Neil MillerManaging Director, International
Revolution Confidential
Strawpoll: experiences with R?
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Revolution Confidential
Agenda: challenges…R…Revolution…examples
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Revolution Confidential
Today’s Challenge: Accelerating Business Cadence
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Changing Business Environment• Fact Based Decisions Require More Data • Need to Understand Tradeoffs and Best Course of Action• Predictive Models Need to Continually Deliver Lift • Reduced Shelf Life for Predictive Models
Faster Time to Value• Reduce Analytic Cycle Time• Build & Deploy Models Faster• Eliminate Time Consuming Data Movements
Rapid Customer Facing Decisions• Score More Frequently• Need to Make Best Decision in Real Time
Revolution Confidential
Page Hits on www.revolutionanalytics.com by country in last 8 weeks
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Revolution Confidential
www.revolutionanalytics.com - page views
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Page Views - Top 10 Countries2013/ 04/ 01 – 2013/ 05/ 25 197454
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Revolution Confidential
Incredible graphics, visualization and flexiblestatistical analytics capabilities
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4500+ packages
Revolution Confidential
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has some constraints for enterprise use
Revolution Confidential
Can we be more innovative in marketing analytics…and precise in our targeting… using new and “old” data… in less time?
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Revolution Confidential
How fast can the marketing data scientist innovate to drive better precision in model output? ……and can you get it (scale of data / scale of model scoring) in to production? …at an acceptable price point?
Revolution ConfidentialDistributedR and ScaleR processing handles big data and / or big analytics.
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Revolution ConfidentialScaleR: High Performance ScalableParallel External Memory Algorithms
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Data import – Delimited, Fixed, SAS, SPSS, OBDC
Variable creation & transformation
Recode variables Factor variables Missing value handling Sort Merge Split Aggregate by category
(means, sums)
Data import – Delimited, Fixed, SAS, SPSS, OBDC
Variable creation & transformation
Recode variables Factor variables Missing value handling Sort Merge Split Aggregate by category
(means, sums)
Min / Max Mean Median (approx.) Quantiles (approx.) Standard Deviation Variance Correlation Covariance Sum of Squares (cross product
matrix for set variables) Pairwise Cross tabs Risk Ratio & Odds Ratio Cross-Tabulation of Data
(standard tables & long form) Marginal Summaries of Cross
Tabulations
Min / Max Mean Median (approx.) Quantiles (approx.) Standard Deviation Variance Correlation Covariance Sum of Squares (cross product
matrix for set variables) Pairwise Cross tabs Risk Ratio & Odds Ratio Cross-Tabulation of Data
(standard tables & long form) Marginal Summaries of Cross
Tabulations
Chi Square Test Kendall Rank Correlation Fisher’s Exact Test Student’s t-Test
Chi Square Test Kendall Rank Correlation Fisher’s Exact Test Student’s t-Test
Data Prep, Distillation & Descriptive Analytics Data Prep, Distillation & Descriptive Analytics
Subsample (observations & variables)
Random Sampling
Subsample (observations & variables)
Random Sampling
R Data Step Statistical Tests
Sampling
Descriptive Statistics
Revolution ConfidentialScaleR: High Performance ScalableParallel External Memory Algorithms
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Sum of Squares (cross product matrix for set variables)
Multiple Linear Regression Generalized Linear Models (GLM)
- All exponential family distributions: binomial, Gaussian, inverse Gaussian, Poisson, Tweedie. Standard link functions including: cauchit, identity, log, logit, probit. User defined distributions & link functions.
Covariance & Correlation Matrices
Logistic Regression Classification & Regression Trees Predictions/scoring for models Residuals for all models
Sum of Squares (cross product matrix for set variables)
Multiple Linear Regression Generalized Linear Models (GLM)
- All exponential family distributions: binomial, Gaussian, inverse Gaussian, Poisson, Tweedie. Standard link functions including: cauchit, identity, log, logit, probit. User defined distributions & link functions.
Covariance & Correlation Matrices
Logistic Regression Classification & Regression Trees Predictions/scoring for models Residuals for all models
Histogram Line Plot Scatter Plot Lorenz Curve ROC Curves (actual data and
predicted values)
Histogram Line Plot Scatter Plot Lorenz Curve ROC Curves (actual data and
predicted values)
K-Means K-Means
Statistical ModelingStatistical Modeling
Decision Trees Decision Trees
Predictive Models Cluster AnalysisData Visualization
Classification
Machine LearningMachine Learning
SimulationSimulation
Variable Selection Stepwise Regression
Monte Carlo Parallel Random Number
Generation
Monte Carlo Parallel Random Number
Generation
Revolution Confidential
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• User Churn: predict the likelihood of a user leaving a particular game
• User Community Impact: understand the impact players have on communities
• Promotional Pricing: understand user purchase behavior better.
• Game Content Optimization: understand user behavior to develop new games
Revolution example: multi-use predictive analytics
Revolution Confidential
Example of what we do: DataSong, marketing attribution and optimisation
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Company: Data Song Software, San Franciscowww.datasong.com
Industry: software / services for marketing attribution and campaign optimization
Challenge: economically develop a scalable, high-performing R-powered Big Data Analytics platform on which to provide services to clients
Solution: • Revolution R Enterprise for Big Data
Analytics and Hadoop for data management • Customized exploratory data analysis and
GAM survival models to drive NBA and targeting
• Saved one client $270,000 on one campaign• Generated 14% lift for another client
We saw about a 4x performance improvement on 50 million records. It works brilliantly.”- CEO, John Wallace, DataSong
Revolution Confidential
Example of what we do: [X+1], digital marketing analytics
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Company: [X+1] New York, www.xplusone.com
Industry: software and services for optimized digital marketing through multi-channel visitor experiences on personalized websites and real-time digital audience targeting
Challenge: needed real-time analytics, automated model updates, include new data types and manage quickly-growing data volumes
Solution: • Revolution R Enterprise, for Big Data
Analytics, and a distributed computing platform for data management
• Higher lift of real time multi-channel ad targeting analytics derived from use of more data and attributes
• Higher lift through higher precision audience targeting and tailored messaging 2X data, 2X attributes
no impact on performance
Revolution Confidential
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Revolution Analytics is the only company that provides bigger, faster, smarter R‐powered analytics
for new generation enterprises.
Revolution ConfidentialPEMAs Beat In-Memory Algorithms Parallel external memory algorithms
(PEMA’s) Exploit distributed and streaming data Deliver scalability and performance Split computations so not all data has to be in
memory at one time “automatically” parallelize and distribute
algorithms
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Revolution Confidential
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Revolution R EnterpriseHigh Performance, Multi-Platform Analytics Platform
Revolution R EnterpriseRevolution R EnterpriseDeployR
Web Services Software Development Kit
DevelopRIntegrated
Development Environment
ConnectRHigh Speed & Direct Connectors
Teradata, HDFS (both), Hbase, Netezza, SAS, SPSS, CSV, ODBC
ScaleRHigh Performance Big Data Analytics
DistributedRStreaming, In-Memory Distributed Computing Framework
IBM PureData, IBM Platform LSF, HPC Server, MS Azure Burst, Windows & redhat Servers
RevoRPerformance Enhanced Open Source R + Open Source R packages
Revolution Confidential
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www.revolutionanalytics.com Twitter: @RevolutionR
The leading commercial provider of software and support for the popular open source R statistics language.
Thank you