Corpus ID: 39119115

Discovering Customer Journey Maps using a Mixture of Markov Models

  title={Discovering Customer Journey Maps using a Mixture of Markov Models},
  author={Matthieu Harbich and Ga{\"e}l Bernard and P. Berkes and B. Garbinato and P. Andritsos},
  • Matthieu Harbich, Gaël Bernard, +2 authors P. Andritsos
  • Published in SIMPDA 2017
  • Computer Science
  • Customer Journey Maps (CJMs) summarize the behavior of customers by displaying the most common sequences of steps they take when engaging with a company or product. In many practical applications, the challenge lies in automatically discovering these prototypical sequences from raw event logs for thousands of customers. We propose a novel, probabilistic approach based on a mixture of Markov models and show it can reliably extract CJMs with just one input parameter (and potentially none). 
    4 Citations

    Figures and Topics from this paper

    Contextual and Behavioral Customer Journey Discovery Using a Genetic Approach
    • 1
    • PDF
    CJM-ab: Abstracting Customer Journey Maps Using Process Mining
    • 6
    • PDF
    Analyzing User Behavior in Search Process Models


    CJM-ex: Goal-oriented Exploration of Customer Journey Maps using Event Logs and Data Analytics
    • 8
    • PDF
    A Process Mining Based Model for Customer Journey Mapping
    • 16
    • PDF
    Understanding Customer Experience Throughout the Customer Journey
    • 1,144
    • PDF
    Process Mining: Data Science in Action
    • 594
    Pattern Recognition and Machine Learning
    • R. Neal
    • Computer Science, Mathematics
    • Technometrics
    • 2007
    • 18,185
    Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
    • 45,922
    • PDF