Module description
What is the module about?
Companies today possess a wealth of data about their customers and how they respond to marketing campaigns, yet few firms possess the knowledge of how to effectively analyse this data to derive meaningful insights. In this module, you will learn how to use a range of marketing analytics techniques to analyse, visualise, and interpret data from various sources such as surveys, transaction data, and data from social media platforms. To effectively analyse these data, you will learn how to use typical software packages to develop practical solutions to specific marketing problems. In contrast to other marketing modules, particular emphasis will be placed in guiding you step-by-step on how to conduct specific data analyses using suitable software. You will also have the opportunity to hone your analytical skills through various data exercises. As such, this module will help you understand the importance of marketing analytics in facilitating a systematic, data-driven approach to marketing decision making. During the course of the module, you will appreciate how various marketing analytical techniques, tools, metrics, and data sources can be used to address fundamental challenges marketing managers face today in the age of Big Data. In doing so, we will also discuss the limitations and challenges of using specific marketing analytics techniques and will highlight the importance of managerial judgement in making sense of analysis results.
Who should do this module?
Those students should take this module who want to:
- Understand the importance of marketing analytics in addressing fundamental challenges marketing managers face when developing a marketing strategy.
- Critically evaluate core analytical techniques, metrics, and required data that can be used to provide insight into common marketing issues.
- Use modern computer software and advanced statistical techniques to conduct data analysis and visualize data to derive meaningful marketing insights.
- Understand the limitations and challenges of marketing analytics and the importance of managerial judgement.
Provisional Lecture Outline
Lecture 1: Introduction to Marketing Analytics
Lecture 2: Data Technologies and Analytical Tools
Lecture 3: Customer Segmentation and Targeting I (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 Mining
Lecture 9: Social Network Analysis
Lecture 10: Introduction to Artificial Intelligence
Assessment details
70% Group Assessment
15% Online Test
15% Online Test
Groups are self-selected by students within tutorials
Teaching pattern
Weekly Lecture
Fortnightly Tutorial
Suggested reading list
- 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.
- Mooi, E. and Sarstedt, M. (2010): A Concise Guide to Market Research, Berlin, Heidelberg: Springer
- Hair, J. F., Babin, B.J., Anderson, R.E ; Black, W.C. (2018): Multivariate Data Analysis, Eighth Edition, Relevant Chapters: 8 (Logistic Regression).
- 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
- Scott, J., Carrington, P. J. (2011): The SAGE Handbook of Social Network Analysis. Sage Publications,
- Palmatier, R.W. and Sridhar, S. (2017) Marketing Strategy: Based on First Principles and Data Analytics, Palgrave Macmillan.