Fast Implementation of Machine Learning Algorithms for Event Sequence Data in order to improve Customer Experience

Every day millions of customers move through the sales cycles of companies, this generates large sources of event data. This project aims to discover, understand and predict the journeys of their customers. On one hand, the project is interested in describing the data at a higher level. This means to apply machine learning techniques, namely clustering, and sequence embedding, in order to group similar behaviors together and allow the user to focus analysis on different aspects of the data, such as users of a specific age. On the other hand, this project focuses on dealing with real-time data, i.e. data that is coming into the system continuously. This platform could be applied to sales optimization, business process mining, churn analysis and so on.

Zhou Fang
Faculty Supervisor: 
Nathan Taback
Partner University: