Marketing Analytics with R Lifting Campaign Success Rates

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


Introductions and welcome

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Andrie de VriesBusiness Services Director, Europe

Neil MillerManaging Director, International


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


Page Hits on www.revolutionanalytics.com by country in last 8 weeks

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www.revolutionanalytics.com - page views

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Page Views - Top 10 Countries2013/ 04/ 01 – 2013/ 05/ 25 197454

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Incredible graphics, visualization and flexiblestatistical analytics capabilities

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4500+ packages


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has some constraints for enterprise use


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?

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


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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


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


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.

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 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


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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

Technology

Marketing Analytics with R Lifting Campaign Success Rates