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