This document summarizes a presentation given by Revolution Analytics on using R for marketing analytics. It discusses challenges like needing to make decisions faster based on more data and predictive models. It provides examples of companies using Revolution's R software to improve results, such as increasing lift for a client by 14% and saving another $270k. The presentation promotes Revolution's R software for handling big data and analytics faster through techniques like parallel processing and distributed computing. It argues Revolution R is the leading commercial provider of high performance R software.

1. Revolution Confidential
Marketing Analytics
with R:
Lifting Campaign
Success Rates
London June 7th , 2013
Neil Miller Managing Director International
Andrie de Vries Business Services Director Europe

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Introductions and welcome
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Andrie de Vries
Business Services Director, Europe
Neil Miller
Managing Director, International

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Strawpoll: experiences with R?
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Agenda: challenges…R…Revolution…examples
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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

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Page Hits on www.revolutionanalytics.com by
country in last 8 weeks
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Incredible graphics, visualization and flexible
statistical analytics capabilities
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4500+ packages

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has some constraints for enterprise use

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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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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?

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DistributedR and ScaleR processing
handles big data and / or big analytics.
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13. Revolution ConfidentialScaleR: High Performance Scalable
Parallel 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 AnalyticsData Prep, Distillation & Descriptive Analytics
 Subsample (observations &
variables)
 Random Sampling
 Subsample (observations &
variables)
 Random Sampling
R Data Step Statistical Tests
Sampling
Descriptive Statistics

14. Revolution ConfidentialScaleR: High Performance Scalable
Parallel External Memory Algorithms
14
 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

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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

16. Revolution Confidential
Example of what we do:
DataSong, marketing attribution and optimisation
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Company: Data Song Software, San Francisco
www.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

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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

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Revolution Analytics is the only company that
provides bigger, faster, smarter R‐powered analytics
for new generation enterprises.

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PEMAs 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 R Enterprise
High Performance, Multi-Platform Analytics Platform
Revolution R EnterpriseRevolution R Enterprise
DeployR
Web Services Software Development Kit
DevelopR
Integrated
Development
Environment
ConnectR
High Speed & Direct Connectors
Teradata, HDFS (both), Hbase, Netezza, SAS, SPSS, CSV, ODBC
ScaleR
High Performance Big Data Analytics
DistributedR
Streaming, In-Memory Distributed Computing Framework
IBM PureData, IBM Platform LSF, HPC Server, MS Azure Burst, Windows &
redhat Servers
RevoR
Performance Enhanced Open Source R + Open Source R packages

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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