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Data science with Google Analytics @MeasureCamp

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Is data science with Google Analytics possible ? How ?
This more an overview and less a technical talk.
Measure camp London Sept 2017

Published in: Data & Analytics
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Data science with Google Analytics @MeasureCamp

  1. 1. Data Science with Google Analytics Is it possible and how ? Alexandros Papageorgiou MeasureCamp London. September 2017.
  2. 2. About me Account strategist @ Google Back to school - Data analytics analyst @alex-papageo.com Consumer behaviour analyst @ WhatClinic.com
  3. 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. 4. “Google Analytics is basically for marketers” Data Scientist in big internet company to me
  5. 5. The landscape back in 2015
  6. 6. A year or so later
  7. 7. Hype ?
  8. 8. Back to the question: Is DS with GA possible ? Answer: Yes it is. But…
  9. 9. 1. Access the Data Google API + R/Python Libraries. R GoogleAnalyticsR RGA RGoogleAnalytics Python Pandas ga google2pandas
  10. 10. 2. Initial variable selection
  11. 11. 3. Un-sample the data E.g. Day by day or hour by hour api calls.
  12. 12. 4. Transform the data Bring it to atomic/event level, e.g. every row corresponds to an individual obervation - User - Session - Page
  13. 13. User explorer ?
  14. 14. Query multiple dimensions
  15. 15. Result: Session level data
  16. 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. 17. 5. Store the data (recommended)
  18. 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. 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. 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.
  21. 21. Thank you!

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