This document summarizes a webinar about analyzing Google Analytics data with R. The webinar covered an introduction to R, why use R for Google Analytics data, getting started with R and the Google Analytics API, and provided three examples of real-life applications including predicting product revenue, assessing marketing campaign value over time, and creating visualizations with ggplot2. Speakers included Kushan Shah from Tatvic and Andy Granowitz from Google Analytics.

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Google Analytics Data Mining with R
(includes 3 Real Applications)
Jan 28th, 2015
FREE Webinar by

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Our Speakers
Kushan Shah
Maintainer of RGoogleAnalytics
Library & Web Analyst at Tatvic
@ kushan_s
Andy Granowitz
Developer Advocate, Google
Analytics (Google)
@ agrano

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Outline
An Introduction to R
Why analyze Google Analytics data with R
Getting started with R & Google Analytics
Questions & Answers
3 Real Life Applications & Use Cases

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An Introduction to R
• Open source statistical computing language, widely used by
organizations to solve business problems

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An Introduction to R
• Open source statistical computing language, widely used by
organizations to solve business problems
Data Analysis Statistical Tests
Data
Visualization
Predictive Models
Forecasting
R

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Why Use R
Easy to integrate with various data sources
Data Frame – Analogous to Excel Spreadsheet or MySQL
Table
6000 Pre developed packages for various applications
No software licensing costs
Enables reproducible analysis

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Outline of this Webinar
An Introduction to R
Why analyze Google Analytics data with R
Getting started with R & Google Analytics
Questions & Answers
3 Real Life Applications & Use Cases

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Why analyze Google Analytics data with R
● Google Analytics API allows data extraction for custom reports
• Reports with up to 7 Dimensions and 10 Metrics
• API is well suited for batch data extraction
• API has techniques for handling large queries (10K - 1M records and beyond)
● RGoogleAnalytics = R Wrapper over the Google Analytics API
• Provides functions to easily interact with the Google Analytics API
• Takes care of the low level plumbing
● Google Analytics Premium User and data exported to Big Query
• Use the bigrquery package by Prof. Hadley Wickham

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Outline of this Webinar
An Introduction to R
Why analyze Google Analytics data with R
Getting started with R & Google Analytics
Questions & Answers
3 Real Life Applications & Use Cases

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Getting Started with R and Google Analytics
● Install R - http://www.r-project.org/
● Install RStudio - GUI for R (Optional)
● Install RGoogleAnalytics
Check out the blogpost for a step by step walkthrough - bit.ly/18oJjqA

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Getting Started with R and Google Analytics
One Time Setup
● Create a Project in the Google Dev Console
● Activate the Google Analytics API for your project
● Get your Project’s Client ID and Client Secret
https://developers.google.com/analytics/devguides/reporting/core/v3/gdataAuthorization

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Outline of this Webinar
An Introduction to R
Why analyze Google Analytics data with R
Getting started with R & Google Analytics
Questions & Answers
3 Real Life Applications & Use Cases

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Examples
● Example 1: Forecast Product Revenue for an eCommerce
Store
● Example 2: Assess the long term value of your Marketing
Campaigns
● Example 3: Web Analytics Visualization with ggplot2

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Example 1: Predict Product Revenue with R
● Get Product Revenue as Time Series (historical data)
● Forecast Product Revenue for the next quarter
Check out the blogpost for a complete walkthrough - http://bit.ly/1y3dmtI

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Example 1: Predict Product Revenue with R
Time Series Components

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Example 2: Long Term Value of Your Marketing Campaigns
Check out the blogpost for a complete walkthrough - http://bit.ly/1zpjbYA

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Example 2: Long Term Value of Your Marketing Campaigns

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Query New Customers Acquired via a Given Campaign
query.list <- Init(start.date = "2014-11-01",
end.date = "2014-12-20",
dimensions = "ga:date",
metrics = "ga:transactions,ga:transactionRevenue",
segment = "users::sequence::
^ga:userType==New Visitor;
dateOfSession<>2014-11-01_2014-11-07;
ga:campaign==Campaign A;
->>perSession::ga:transactions>0",
sort = "ga:date",
table.id = tableId)
Example 2: Long Term Value of Your Marketing Campaigns

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Example 2: Long Term Value of Your Marketing Campaigns
Segments Explained
• The segment selects users:: in order to include not only the sessions that match the
conditions, but all sessions among users who match the conditions.
• The sequence:: prefix selects a set of users that completed a specified set of steps
• Step #1 - Visit from a given campaign in a given set of time
• Step #2 - Make a purchase
• The ^ prefix in front of ga:userType==New Visitor;dateOfSession<>2014-11-01_2014-
11-07;ga:campaign==Campaign A ensures that the Date of Session, Campaign, and
User Type conditions are true for the first hit of the first session in the given date
range.
• ->>perSession::ga:transactions > 0 specifies the second step of making a purchase at
some point.

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Example 2: Long Term Value of Your Marketing Campaigns
• head(campaign_a_df)
• cumulativeTransactions <- cumsum(campaign_a_df$transactions)

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Example 2: Long Term Value of Your Marketing Campaigns
Use the data to Generate a Cumulative Transactions Plot

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Example 3: Web Analytics visualization with ggplot2
Background
• gg – Grammar of Graphics (Wilkinson, 2005)
• R Implementation by Prof. Hadley Wickham
• Sophisticated graphs in a *few lines of R code
* Learning Curve

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Example 3: Web Analytics visualization with ggplot2
ggplot(data = ga.data) + geom_line(aes(x = date, y = itemRevenue)
Check out the blogpost for a complete walkthrough - http://bit.ly/15Iaaf7

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Example 3: Web Analytics visualization with ggplot2
Decomposing the syntax
• Create the ggplot object and populate it with data
• ggplot(ga.data)
• Add Layers(s)
• geom_line(aes(x=date, y=itemRevenue))
• Other geoms – bar, point, line, histogram
• Aesthetics describe how variables are mapped to visual
properties of geoms
• Additional Plotting Options -> Plot title, Axis Titles, Axis Text
Formatting, Legends

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Best Practices
• Become familiar with the Google Analytics API Naming
Conventions
– Dimension/Metric names are in camelCase
• Know the permissible Dimension Metric Combinations
– https://developers.google.com/analytics/devguides/reporting/core/dims
mets
• Use the Query Feed Explorer to test queries before running them
in R
– https://ga-dev-tools.appspot.com/explorer/
• Post issues at https://github.com/Tatvic/RGoogleAnalytics/issues
• Questions at Google Analytics Reporting API Forum

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Further Resources
• http://bit.ly/r-googleanalytics-resources
• Watch the full R Google Analytics Webinar -
http://bit.ly/1KrHXtH

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Outline of this Webinar
An Introduction to R
Why analyze Google Analytics data with R
Getting started with R & Google Analytics
Questions & Answers
3 Real Life Applications & Use Cases

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Next Webinar
Webinar: Everything You Need to Know about
GTM V2
When: Feb 18th 10:00 AM PDT Guest Speaker
Phil Pearce
(Web Analyst/ PPC/ SEO Expert)
You will learn:
• How to Upgrade to GTM V2
• Mistakes to Avoid while Upgrading
• A Quick Checklist for Upgrading to GTM V2
And much more…
Register Here - http://bit.ly/gtm-v2-webinar

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Kushan Shah
Twitter: @kushan_s
Thank You!
Andy Granowitz
Twitter: @agrano