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A Process Mining Approach to Analyze Customer Journeys to Improve Customer Experience

Akhavan, Fatemeh | 2022

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 55640 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Hassannayebi, Erfan
  7. Abstract:
  8. With the growth of the number of online service providers and the need to innovate in these services, in this study, the processes and the journeys taken by visitors of a website that provides employment services and employment insurance has been analyzed. In this research, process mining techniques and predictive process monitoring were implemented. With the use of a supervised and unsupervised learning algorithm, it attempted to identify the customer journeys' output and the existing patterns that lead to the complaint. In the first step, the website event log is extracted. Afterward, by using frequency-based encoding methods, the journeys traveled by users were clustered based on the similarity between the journeys. In order to find suitable values for the clustering parameters for creating appropriate clusters, silhouette index analysis has been used to adjust the parameters. For the next step, an analysis based on variance and covariance was performed by selecting the most effective properties of each cluster. Finally, the process outcome has been predicted using a decision tree algorithm with an accuracy of 0.98. Also, to improve user satisfaction, decision tree patterns have been extracted and analyzed
  9. Keywords:
  10. Digital Marketing ; Process Mining ; Data Mining ; Predictive Process Monitoring ; Customer Journey ; Online Businesses

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