An Automatic Recommendation System using R Christopher Byrd - - PowerPoint PPT Presentation

an automatic recommendation system using r
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An Automatic Recommendation System using R Christopher Byrd - - PowerPoint PPT Presentation

An Automatic Recommendation System using R Christopher Byrd Analytics System Architect - christopher.byrd@ihg.com 1 An Automatic Multivariate Testing System using R Christopher Byrd Analytics Systems Architect - christopher.byrd@ihg.com


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An Automatic Recommendation System using R

Christopher Byrd – Analytics System Architect - christopher.byrd@ihg.com

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An Automatic Multivariate Testing System using R

Christopher Byrd – Analytics Systems Architect - christopher.byrd@ihg.com

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Business Need: Relevancy + Maximizing Customer Engagement

  • Q. How to Optimize Consumer Interactions, with an eMail Application?

 Recommendation System  Targeting System  Multivariate Testing

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What is Multivariate Testing?

  • “True multivariable testing will not only be able to test millions of content variations,

it will also be able to determine the impact each individual variable has on conversion, by itself, and in conjunction with other variables.” – Optimost

  • “Multivariate testing is a method of experimentation that allows you to test multiple

variables simultaneously…”

  • Google
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Thank You Email - 10 Personalization Areas (10 Factors)

Subject Line 2 Paragraphs Signature OfferSlot2 Intro Sentence – Direct Link Header line Main Image Brand Cross-Sell OfferSlot 1 Preview pane text sentence Thank you for your stay

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Got Content? What’s the Optimal Combination?

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Why use R?

  • “…R would enable us to experiment with the use of multivariate

testing, targeting, and recommendation systems all in one programming environment, to meet our business needs.”

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Agenda

  • How R Fits in the Enterprise?
  • Primary Packages used in Multivariate Testing
  • Results
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Enterprise Framework Flexible Architecture – Highly Customizable – Session Enabled

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Primary Packages Extensible Markup Language (XML)

xmlTreeParse(doc, useInternal = TRUE)

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Primary Packages AlgDesign – OptFederov and Gen.Factorial Routines

  • ptFederov(model, data, arg1, …)

Final email

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Results Multiple Email Versions “in Market”

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Results Champion Challenger Framework

9% 7%

Compile Behavioral Feedback to Determine a Winner per Segment

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Results Preliminary – Open and Click Thru Rates

▲ 20% increase in Opens ▲ 30% increase in Click Thru

“Thank You!”

Winning Combination!