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Data Science with Google Analytics
Is it possible and how ?
Alexandros Papageorgiou
MeasureCamp London. September 2017.
About me
Account strategist @ Google
Back to school - Data analytics
analyst @alex-papageo.com
Consumer behaviour analyst @ WhatClinic.com
Data...data...data
It’s in our menu every day!
Is it Data science ?
Can it be Data science ?
(what’s data science anyway ? )
“Google Analytics is basically for marketers”
Data Scientist in big internet company to me
The landscape back in 2015
A year or so later
Hype ?
Back to the question:
Is DS with GA possible ?
Answer: Yes it is. But…
1. Access the Data
Google API + R/Python Libraries.
R
GoogleAnalyticsR
RGA
RGoogleAnalytics
Python
Pandas ga
google2pandas
2. Initial variable selection
3. Un-sample the data
E.g. Day by day or hour by hour api calls.
4. Transform the data
Bring it to atomic/event level, e.g. every row corresponds to an individual
obervation
- User
- Session
- Page
User explorer ?
Query multiple dimensions
Result: Session level data
Custom dimensions
Supercharging websites with a real-time R API http://code.markedmondson.me/predictClickOpenCPU/supercharge.html#1
Improve Data Collection With Four Custom Dimensions https://www.simoahava.com/analytics/improve-data-collection-with-four-custom-dimensions/
E.g. Cust ID + Timestamp
5. Store the data (recommended)
6. Model & Communicate the data
https://github.com/papageorgiou/dublinR-talk-analytics http://www.alex-papageo.com/research.html
E.g. Decision tree & variable importance for conversion prediction
Some ideas
● Decision tree for conversion prediction (rpart)
● Clickstream analysis to predict next page (clickstream)
● Clustering for customer segmentation (base R)
● Association Rules for pages/products frequently visited together (arules)
Enrich data: Look for opportunities to join with internal data/ external api data
Wrapping up
DS with GA not straightforward but possible
Take advantage of GA API
Open source R/Python libraries
Get familiar with 2-3 algos and apply them on your data.
Thank you!

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Data science with Google Analytics @MeasureCamp

  • 1. Data Science with Google Analytics Is it possible and how ? Alexandros Papageorgiou MeasureCamp London. September 2017.
  • 2. About me Account strategist @ Google Back to school - Data analytics analyst @alex-papageo.com Consumer behaviour analyst @ WhatClinic.com
  • 3. Data...data...data It’s in our menu every day! Is it Data science ? Can it be Data science ? (what’s data science anyway ? )
  • 4. “Google Analytics is basically for marketers” Data Scientist in big internet company to me
  • 6. A year or so later
  • 8. Back to the question: Is DS with GA possible ? Answer: Yes it is. But…
  • 9. 1. Access the Data Google API + R/Python Libraries. R GoogleAnalyticsR RGA RGoogleAnalytics Python Pandas ga google2pandas
  • 10. 2. Initial variable selection
  • 11. 3. Un-sample the data E.g. Day by day or hour by hour api calls.
  • 12. 4. Transform the data Bring it to atomic/event level, e.g. every row corresponds to an individual obervation - User - Session - Page
  • 16. Custom dimensions Supercharging websites with a real-time R API http://code.markedmondson.me/predictClickOpenCPU/supercharge.html#1 Improve Data Collection With Four Custom Dimensions https://www.simoahava.com/analytics/improve-data-collection-with-four-custom-dimensions/ E.g. Cust ID + Timestamp
  • 17. 5. Store the data (recommended)
  • 18. 6. Model & Communicate the data https://github.com/papageorgiou/dublinR-talk-analytics http://www.alex-papageo.com/research.html E.g. Decision tree & variable importance for conversion prediction
  • 19. Some ideas ● Decision tree for conversion prediction (rpart) ● Clickstream analysis to predict next page (clickstream) ● Clustering for customer segmentation (base R) ● Association Rules for pages/products frequently visited together (arules) Enrich data: Look for opportunities to join with internal data/ external api data
  • 20. Wrapping up DS with GA not straightforward but possible Take advantage of GA API Open source R/Python libraries Get familiar with 2-3 algos and apply them on your data.