1. Clickstream Analytics
Overview and practical applications
with Markov Chains
Data Science and Engineering Club
Dublin, May 2018
Alexandros Papageorgiou

2. Agenda
● Clickstream introduction
● Markov Chains overview
● 3 Practical applications

3. My journey so far
alex-papageo.com

4. Digital transformation
● Traditional companies undergoing digital transformation
● Increasing number of IRL startups now purely digital
● Clickstream becoming an ideal way to listen to the voices of customers

5. Warm-up: Wikipedia Clickstream and Network analysis

6. Why Clickstream
● Perform advanced types of analysis
● Go beyond standard segmentation analysis
● Get closer to the individual voices of customers

7. Alternatives ?

8. What’s the clickstream exactly ?

9. The Weblog

10. Accessing the Clickstream via Google Analytics
1. Implement Customer ID dimension
2. Implement timestamp dimension
Then for every pageview we can see the customer ID and the time stamp
How to guide: https://www.simoahava.com/analytics/improve-data-collection-with-
four-custom-dimensions/

11. A tidy clickstream example

12. Multiple models for clickstream analysis
● Network Analysis to visualise flow of
web traffic
● Clustering of customers
● Clustering of sessions
● Markov Chains for future click
prediction
● Frequent path analysis
● Hidden Markov Models to identify
user’s stage in the buying cycle.
● Association Rules to identify
bottlenecks to conversion
● Bot analysis for SEO optimisation

13. 3 useful applications
● Frequent Path analysis
● Future Click predicition w/ Markov Chains
● Transition Probablities w/ Markov Chains

14. Markov Chains
● It’s a 100+ year old theory.
● Studies the evolution of dynamic systems
● Used widely in science from physics to finance, information science
● Hidden Markov Models, Markov Chain Monte Carlo, higer order
Markov Chains

15. Markov Chains vocabulary
Media Exposure through the Funnel: A Model of Multi-Stage Attribution
repository.cmu.edu/cgi/viewcontent.cgi?article=1399&context=heinzworks

16. The clickstream R package.
Package Author: Michael Scholz
- Cluster your clickstream
- Model the clickstream clusters as a markov chain
- Visualise and calculate transition probabilities
- Predict next click given a submited click sequence.
- Convert the clickstream to an object that is ready for association rules

17. Useful References
Markov Chains intro – when to use them, how they work
https://towardsdatascience.com/introduction-to-markov-chains-50da3645a50d
Clickstream package article on the Journal of Statistical Software
www.jstatsoft.org/article/view/v074i04
Supercharging websites with a real-time R API
http://code.markedmondson.me/predictClickOpenCPU/supercharge
Notebook on Github
https://github.com/papageorgiou/clickstream-talk/blob/master/data-sci-eng-meetup.md

18. Thank you!
@alpapag
analyst@alex-papageo.com
linkedin.com/in/alexandrospapageorgiou