1. A GACP and GTMCP company
How to perform predictive analysis on your web analytics tool data
January 23rd, 2014
A GACP and GTMCP company
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2. 3 Type of Analytics… Analytics Descriptive: What has happened?
Prescriptive: What should happen?
Predictive: Predicts the outcome or future
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3. Scope for Today Analytics Descriptive: What has happened?
Prescriptive: What should happen?
Predictive: Predicts the outcome or future
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4. In other words…
Predictive Analytics
“Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions.”
Source: Siegel, E. (2013) “Predictive Analytics. The power to predict who will click, buy, lie or die.”
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5. Why it matters
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6. Applications
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7. Generic mental model
Individual suggestions, preferences , clustering, & reaction of cohort to stimuli
Predict Failures, errors that costs big
Economic Value of Prediction
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8. Myth Busting
You don’t need to be a PhD statistician to build predictive models
A predictive model shouldn’t be a black box
Even if you know your data, modeling can help
Predictive models can be implemented quickly
Predictive models enhance human judgment, not replace it
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9. Outline Predictive Analytics Tool Data Model R Google Analytics
Logistic
Regression
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10. Outline Predictive Analytics Visualization Tool Data Model R
Google Analytics
Logistic
Regression
Visualization
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11. Introduction to R What Applications Why How to get started
Open source statistical computing language, widely used by organizations to solve business problems.
Applications
Data Analysis
Data Visualization
Statistical Tests
Predictive Model
Forecasting
Why
Easy to integrate
Data frame
Pre developed packages
How to get started
Download
and install
Choose and download a user-friendly GUI
RStudio
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12. R Packages Categories of Packages For this webinar Data Extraction
RGoogleAnalytics
Usage: To extract Google Analytics data into R
Contibutors: Michael Pearmain, Nick Mihailovski, Amar Gondaliya and Vignesh Prajapati
ggplot2
Usage: Build plots and charts
Contibutor: Hadley Wickham
Data Visualization
Time Series
Machine Learning
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13. Outline of this webinar
Predictive Analytics
Tool
Data
Model R
Google Analytics
Logistic
Regression
Visualization
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14. Outline of this webinar
Predictive Analytics
Tool
Data
Model R
Google Analytics
Logistic
Regression
Visualization
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15. Extracting your GA data into R Call API for list of profiles
Google Analytics data
Extracting your GA data into R
User performing data extraction
Google OAuth2
Authorization
Server
Google Analytics
API
Access Token Request
Access Token Response
Call API for list of profiles
Call API for query
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16. Increase in Revenue with recovered returns in long run
Business Problem
Product return
“Returns are on the rise-up 19% from For every US$1 spent on merchandize, 9¢ are returned.”
“Average return rate for ecommerce retailers varies from 3-12%.”
Source: Time Magazine, Sept. 04th, 2012
Product Return Impact (per day)
Increase in Revenue with recovered returns in long run
Average Return Rate
9 %
7 %
Average Order Value
$100
Orders Per Day
500
Total Income
$50,000
Loss due to returns
$4,500
$3,500
Revenue post loss
$45,500
$46,500
Increase in Revenue/day
-----
$1000
Month
x30
$30,000
Year
x365
$365,000
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17. Outline of this webinar
Predictive Analytics
Tool
Data
Model R
Google Analytics
Logistic
Regression
Visualization
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18. Data Introduction Transactional Data
Pre Purchase Data
Browsing Behavior up to shopping cart
In Purchase Data
Purchase Behavior from shopping cart to thank you page
Post Purchase Data
Delivery Period, Location, amount of time to deliver,
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19. Modeling Loading Input Data Introducing Model Variables Model Creation
Model Performance
Applying Model to Test Data
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20. Machine Learning Algorithm Predicted Outcome labels
Machine Learning Tech.
Supervised Learning
Generates a function that maps inputs (labeled data) to desired outputs (e.g. Spam Detection)
Training Data
Variables
Supervised Learning Model
Labels are right answers from historical data
eg. Spam Detector
Input Data: Contains s marked Spam/No Spam
Machine Learning Algorithm
Labels
Test Data
Predicted Outcome labels
Variables
Predictive Model
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21. Modeling Loading Input Data Introducing Model Variables Model Creation
Model Performance
Applying Model to Test Data
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22. Modeling Loading Input Data Introducing Model Variables Model Creation
Model Performance
Applying Model to Test Data
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23. Feature engineering
Going beyond algorithms and using domain knowledge to augment new variables to model
E.g.: Products purchased as gifts are less likely to be returned
Create a New Variable with binary values: 1 – Product purchased as gift, 0 – otherwise
Products purchased in holiday season are more likely to be returned
Based on Purchase date, create new variable with binary values: 1 – Product purchased in the month Nov-Dec, 0 - otherwise
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24. Predictor/Response Variables
Predictor Variable
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25. Modeling Loading Input Data Introducing Model Variables Model Creation
Model Performance
Applying Model to Test Data
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26. Generalized Linear Models
glm (formula, family, data)
Formula
Response ~ Predictor (This argument shows which all variables are independent (predictor) variables and which variable is/are dependent(response) variable/s
Family
Binomial (Since the output variable (which is product return is defined as binary value 0 or 1, we are using binomial family)
Data
Train data set – This data set consists values of all 18 variables (i.e. values of dependent variables and independent variables are given). This dataset is also called labeled data.
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27. Modeling Loading Input Data Introducing Model Variables Model Creation
Model Performance
Applying Model to Test Data
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28. Modeling Loading Input Data Introducing Model Variables Model Creation
Model Performance
Applying Model to Test Data
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29. Machine Learning Algorithm Predicted Outcome labels
Machine Learning Tech.
Supervised Learning
Generates a function that maps inputs (labeled data) to desired outputs (e.g. Spam Detection)
Training Data
Variables
Supervised Learning Model
Labels are right answers from historical data
e.g.: Spam Detector
Input Data: Contains s marked Spam/No Spam
Machine Learning Algorithm
Labels
Test Data
Predicted Outcome labels
Variables
Predictive Model
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30. Summary Call customer before shipping
Probability of product return ≤ 60%
> 60 %
≤ 60 %
Call customer before shipping
Send discount coupon to initiate customer for future purchase
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31. Myth Busting
You don’t need to be a PhD statistician to build predictive models
A predictive model shouldn’t be a black box
Even if you know your data, modeling can help
Predictive models can be implemented quickly
Predictive models enhance human judgment, not replace it
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32. Thank you!
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