Covers key concepts of clickstream analysis and Markov Chains. Followed by 3 practical applications with the R language:
- Frequent path analysis
- Future click prediction
- Transition probabilities mapping
● It’s a 100+ year old theory.
● Hidden Markov Models, Markov Chain Monte Carlo, higher order
● Used widely in science from physics to finance information science
● Models the evolution of dynamic systems in time
Markov Chains key concepts
Media Exposure through the Funnel: A Model of Multi-Stage Attribution Abhishek, Vibhanshu & Fader, Peter &
Hosanagar, Kartik. (2012) repository.cmu.edu/cgi/viewcontent.cgi?article=1399&context=heinzworks
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 submitted click sequence.
- Convert the clickstream to an object that is ready for association rules
Clickstream analysis has been an academic research topic for quite some time
Development of practical applications is now becoming more accessible thanks to
open source tools and libraries
There are several other methods that play nicely with the clickstream such as
clustering, network analysis and association rules.
Clickstream package article on the Journal of Statistical Software
Supercharging websites with a real-time R API
Wikipedia clickstream rabbit holes
MeaureCamp session Clickstream notebook