Module description
What is the module about?
Companies today generate vast amounts of data about their customers, markets, and marketing activities. However, turning this data into actionable insights requires appropriate analytical techniques and the ability to interpret results in a managerial context. This module introduces students to key concepts and methods in marketing analytics and demonstrates how data can be used to support evidence-based marketing decisions.
Throughout the module, students will learn how to analyse, visualise, and interpret marketing data from a variety of sources, including survey data, transaction data, and unstructured text data such as social media content. The module provides practical, step-by-step guidance on applying a range of commonly used marketing analytics techniques using appropriate analytical software. Through various data exercises, students will gain hands-on experience working with data and will develop the skills needed to translate analytical outputs into meaningful managerial insights.
The module covers a range of analytical approaches used in modern marketing practice, including customer segmentation, predictive analytics, marketing experiments, customer value analysis, and text analytics. Students will also explore how emerging technologies such as artificial intelligence are transforming marketing decision-making.
By the end of the module, students will understand how marketing analytics can support a systematic, data-driven approach to marketing strategy and decision making. The module also emphasises the limitations of analytical techniques and highlights the importance of critical thinking and managerial judgement when interpreting results.
Who should do this module?
Those students should take this module who want to:
Provisional Lecture Outline
Lecture 1: Introduction to Marketing Analytics
Lecture 2: Data Technologies and Analytical Tools
Lecture 3: Dimensionality Reduction (Factor Analysis)
Lecture 4: Customer Segmentation and Targeting II (Cluster Analysis)
Lecture 5: Predictive Analytics
Lecture 6: Customer Lifetime Value Analysis
Lecture 7: Recency, Frequency, Monetary Value Analysis
Lecture 8: Text & Unstructured Data Analysis
Lecture 9: Marketing Experiments
Lecture 10: AI and Generative Analytics
Assessment details
70% Group Assessment
30% Tutorial Assessment
Groups are self-selected by students within tutorials
Teaching pattern
Weekly Lecture
Fortnightly Tutorial
Suggested reading list
Key text or background reading
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Sivarajah, U., Kamal, M. M., Irani, Z. and Weerakkody, V. (2017): Critical analysis of Big Data challenges and analytical methods,” Journal of Business Research, 70, 263–86.
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Mooi, E. and Sarstedt, M. (2010): A Concise Guide to Market Research, Berlin, Heidelberg: Springer
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Hair, J. F., Babin, B.J., Anderson, R.E ; Black, W.C. (2018): Multivariate Data Analysis, Eighth Edition, Relevant Chapters: 8 (Logistic Regression).
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Berger, J., Humphreys, A., Ludwig, S., Moe, W. W., Netzer, O. and Scweide, D. A. (2020): Uniting the Tribes: Using Text for Marketing Insight, Journal of Marketing, 84(1), 1-25
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Danatzis, I., Chandler, J. D., Akaka, M. A., & Ng, I. C. L. (2024). Designing Digital Platforms for Social Justice: Empowering End Users through the Dataswyft Platform. MIS Quarterly, 48(4), 1771–1802.
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Villarroel Ordenes, F., & Silipo, R. (2021). Machine learning for marketing on the KNIME Hub: The development of a live repository for marketing applications. Journal of Business Research, 137(September), 393–410.