Clickstream Clustering

Through clickstream analysis, we can answer critical questions like

  1. Who buyers are?
  2. What they are trying to accomplish?
  3. What goals drive their behaviour?
  4. How they think?
  5. How they buy?
  6. What are the underlying patterns of their thinking and buying?
  7. Why they make buying decisions?
  8. What are the major behavioural categories?
  9. Which behavior is more prevalent?
  10. What’s the relationship between different types of behavior?
  11. How to Segment Buyer Persona?

Solution: Unsupervised Clickstream Clustering We will build an unsupervised system to capture dominating user behaviours from clickstream data (traces of users’ click events) and visualize the detected behaviours in an intuitive manner. Our model will identify “clusters” of similar users by partitioning a similarity graph (nodes are users; edges are weighted by clickstream similarity). The partitioning process leverages iterative feature pruning to capture the natural hierarchy within user clusters and produce intuitive features for visualizing and understanding captured user behaviours. Additionally, these clusters are transformed into a text that describes in natural language the main characteristics of the buyer. Both Cluster Analysis (Divisive Hierarchical Clustering) and RFM Analysis would be leveraged for segmenting the buyer persona.