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Marketing Analytics with R Lifting Campaign Success Rates

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From making your campaigns more effective to greater accuracy in attribution, Revolution Analytics shows you how data analysis and predictive analytics with Revolution R Enterprise will help you make your marketing budget work harder and last longer.

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Marketing Analytics with R Lifting Campaign Success Rates

  1. 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
  2. 2. Revolution Confidential Introductions and welcome 2 Andrie de Vries Business Services Director, Europe Neil Miller Managing Director, International
  3. 3. Revolution Confidential Strawpoll: experiences with R? 3
  4. 4. Revolution Confidential Agenda: challenges…R…Revolution…examples 4
  5. 5. Revolution Confidential Today’s Challenge: Accelerating Business Cadence 5 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
  6. 6. Revolution Confidential Page Hits on www.revolutionanalytics.com by country in last 8 weeks 6NB: Countries with <500 page hits excluded
  7. 7. Revolution Confidential www.revolutionanalytics.com - page views 7 0 20000 40000 60000 80000 100000 120000 140000 160000 151302 36724 28321 27718 19888 12990 13615 11096 11748 10442 Page Views - Top 10 Countries 2013/ 04/ 01 – 2013/ 05/ 25 197454 163055 112172 19303 6544 4073 738 10624795 Page Views by Geo – 2013/ 04/ 01 – 2013/ 05/ 25 EUROPE NORTH AMERICA APJ SOUTH AMERICA AFRICA MIDDLE EAST NA CARIBBEAN CENTRAL AMERICA 15645 76227 EMEA Page Views by Organisation Type Academic Commercial
  8. 8. Revolution Confidential Incredible graphics, visualization and flexible statistical analytics capabilities 8 4500+ packages
  9. 9. Revolution Confidential 9 has some constraints for enterprise use
  10. 10. Revolution Confidential Can we be more innovative in marketing analytics…and precise in our targeting… using new and “old” data… in less time? 10
  11. 11. 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?
  12. 12. Revolution Confidential DistributedR and ScaleR processing handles big data and / or big analytics. 12
  13. 13. Revolution ConfidentialScaleR: High Performance Scalable Parallel External Memory Algorithms 13  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. 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
  15. 15. Revolution Confidential 15 • 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. 16. Revolution Confidential Example of what we do: DataSong, marketing attribution and optimisation 16 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
  17. 17. Revolution Confidential Example of what we do: [X+1], digital marketing analytics 17 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
  18. 18. Revolution Confidential 18 Revolution Analytics is the only company that  provides bigger, faster, smarter R‐powered analytics for new generation enterprises.
  19. 19. Revolution Confidential 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 19
  20. 20. Revolution Confidential 20 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
  21. 21. Revolution Confidential 21 www.revolutionanalytics.com  Twitter: @RevolutionR The leading commercial provider of software and support for the popular  open source R statistics language. Thank you

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