Data-Driven Customer Insights and Segmentation

Postgraduate | 2026

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Mode
Mode
Your studies will be on-campus, and may include some online delivery
On campus
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Area/Catalogue
COMP 6017
Course ID icon
Course ID
203336
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Campus
Mawson Lakes, Adelaide City Campus East
Level of study
Level of study
Postgraduate
Unit value icon
Unit value
6
Course owner
Course owner
School of Comp Sc & IT
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Course level
2
Work Integrated Learning course
Work Integrated Learning course
No
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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

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)

  • must have completed all of ARTI6003 Machine Learning Algorithms/COMP1002 Problem Solving and Programming/STAT5020 Statistical Foundations for Data Science and Artificial Intelligence

Corequisite(s)

N/A

Antirequisite(s)

N/A

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The Student Contribution amount displayed below is for students commencing a new program from 2021 onwards. If you are continuing in a program you commenced prior to 1 January 2021, or are commencing an Honours degree relating to an undergraduate degree you commenced prior to 1 January 2021, you may be charged a different Student Contribution amount from the amount displayed below. Please check the Student Contribution bands for continuing students here. If you are an international student, or a domestic student studying in a full fee paying place, and are continuing study that you commenced in 2025 or earlier, your fees will be available here before enrolments open for 2026.

Degree list
The following degrees include this course