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Clickstream analytics with Markov Chains

Data Science and Engineering Club, Dublin May 2018

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Clickstream analytics with Markov Chains

  1. 1. Clickstream Analytics Overview and practical applications with Markov Chains Data Science and Engineering Club Dublin, May 2018 Alexandros Papageorgiou
  2. 2. Agenda ● Clickstream introduction ● Markov Chains overview ● 3 Practical applications
  3. 3. My journey so far
  4. 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. 5. Warm-up: Wikipedia Clickstream and Network analysis
  6. 6. Why Clickstream ● Perform advanced types of analysis ● Go beyond standard segmentation analysis ● Get closer to the individual voices of customers
  7. 7. Alternatives ?
  8. 8. What’s the clickstream exactly ?
  9. 9. The Weblog
  10. 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: four-custom-dimensions/
  11. 11. A tidy clickstream example
  12. 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. 13. 3 useful applications ● Frequent Path analysis ● Future Click predicition w/ Markov Chains ● Transition Probablities w/ Markov Chains
  14. 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. 15. Markov Chains vocabulary Media Exposure through the Funnel: A Model of Multi-Stage Attribution
  16. 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. 17. Useful References Markov Chains intro – when to use them, how they work Clickstream package article on the Journal of Statistical Software Supercharging websites with a real-time R API Notebook on Github
  18. 18. Thank you! @alpapag