Data-Driven Customer Insights and Segmentation

Postgraduate | 2026

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Area/Catalogue
COMP 6017
Course ID icon
Course ID
203336
Level of study
Level of study
Postgraduate
Unit value icon
Unit value
6
Course level icon
Course level
2
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Inbound study abroad and exchange
Inbound study abroad and exchange
The fee you pay will depend on the number and type of courses you study.
Yes
University-wide elective icon
University-wide elective course
Yes
Single course enrollment
Single course enrolment
Yes
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Note:
Course data is interim and subject to change

Course overview

This course is designed to deepen students' understanding of behavioural analytics and customer segmentation through advanced analytical models and real-world applications. Students will explore the complexities of consumer behaviour by employing statistical models, machine learning techniques, and predictive analytics. The course will integrate theories of behavioural segmentation and practical skills in clustering, lifetime value modelling, and predictive analysis. Students will work extensively with data, using sophisticated techniques to segment customers and generate actionable insights that can shape personalised marketing strategies, customer experience enhancements, and decision-making in complex business environments. Ethical considerations and data privacy laws will also be addressed.

Course learning outcomes

  • Apply fundamental time series analysis techniques to identify and model trends, seasonality, and noise within temporal data
  • Develop and evaluate ARIMA, SARIMA, and exponential smoothing models for forecasting time series data
  • Utilise machine learning models to forecast complex time series data and compare their performance to traditional methods
  • Interpret and assess the accuracy of time series models using appropriate evaluation metrics
  • Critically evaluate the ethical considerations in time series forecasting, ensuring responsible and unbiased applications of temporal models

Prerequisite(s)

N/A

Corequisite(s)

N/A

Antirequisite(s)

N/A