MSE Research Project Database

Temporal data analysis for customer churn prediction


Project Leader: Ling Luo
Staff: Goce Ristanoski
Primary Contact: Ling Luo (ling.luo@unimelb.edu.au)
Keywords: artificial intelligence; data mining; machine learning
Disciplines: Computing and Information Systems
Domains:

The customer behaviour analysis is a critical component of business intelligence and marketing. Understanding the customer behaviour can support businesses to develop cost- and time-efficient marketing strategies, or launch tailored programs with social value for the public. One of the main interesting areas of applied data science in industry environment is customer churn prediction, which can segment the customer cohorts to identify those who are at risk of ending their engagement with the company. This project will use machine learning and data mining methods to better understand customer behaviour, discover different types of behaviour patterns, and provide solutions to predicting when a customer leaves, and how to prevent that.

In this project, the students can go through the lifecycle of a real research project, from understanding the research problem, designing innovative solutions, analysing results to presenting research outcomes. It may also involve the usage of big data platform such as AWS and Azure. The students can also practice a set of skills, such as programming, critical thinking and communication.

Expected skills: programming in R/Python; strong knowledge of statistics, machine learning and data mining; some knowledge of big data platform AWS/Azure.