Introductory description

This module equips students with the analytical skills needed to transform data into actionable business insights. In an era where organizations have vast amounts of customer data, the ability to extract meaningful insights is critical for strategic decision-making.

Through a combination of lectures, hands-on workshops, and real-world case studies, students will learn key marketing analytics techniques, including customer segmentation, regression analysis, and machine learning methods, using the R programming language. The module also emphasizes the effective communication of data-driven insights through graphical storytelling and structured written presentation.

By the end of the module, students will have a solid foundation in marketing analytics tools and their application in business contexts, enhancing their ability to support data-driven strategies.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Indicative syllabus:
Marketing and Strategy Analytics and Business Performance. What is Marketing and Strategy Analytics?
How can Marketing and Strategy Analytics help to improve business performance?
Marketing and Strategy Analytics Methods and how to apply them
Introduction to the Software R (or another appropriate software package)
Fundamentals of Customer Data Analysis. Describing data and studying relationships between different types of customer information
Advanced Marketing Applications and Using data for decision making
How to segment customers (decision trees, regression trees, clustering)
Sharing your insights with different audiences

Learning outcomes

By the end of the module, students should be able to:

• Demonstrate understanding of empirical methods and their role in data-driven decision-making.
• Implement a range of marketing and strategy analytics techniques using R (or another appropriate software package).
• Critically evaluate best practices in marketing analytics and data analysis.
• Critically assess the structure, quality, and usability of customer and business data for marketing and strategy analytics.
• Assess the ethical and social impact of marketing analytics in business practices.
• Critically evaluate the core principles of marketing and strategy analytics and their application in business decision-making.

Indicative reading list

The suggested key texts for this module will be:
Brownlee, J. (2016) Master Machine Learning Algorithms. http://MachineLearningMastery.com
Chapman, C. and Feit, E.M. (2015) R for Marketing Research and Analytics. Springer
Lantz, B. (2015) Machine Learning with R (Second edition). Birmingham: Packt Publishing
Miguel Forte, R. (2015) Mastering Predictive Analytics with R. Birmingham: Packt Publishing
In addition, books that provide an introduction to marketing and business analytics may be useful for students:
Grigsby, M. (2018) Marketing Analytics: A Practical Guide to Improving Consumer Insights Using Data Techniques. London: KoganPage.
Pauwels, K. (2014) It's Not the Size of the Data - It's How You Use It: Smarter Marketing with Analytics and Dash-boards. New York: American Management Association.
Pinder, J.P., 2016. Introduction to Business Analytics Using Simulation. Academic Press.
In addition, selected articles from leading Marketing Journals such as Journal of Marketing Research, Journal of Marketing, Marketing Science, and International Journal of Research in Marketing will be used to illustrate state of the art applications that go beyond the textbook content. For example:
Germann, F., Lilien, G.L. and Rangaswamy, A. (2013) ‘Performance Implications of Deploying Marketing Analytics’, International Journal of Research in Marketing, 30(2), pp. 114–128.
Lilien, G.L. (2011) ‘Bridging the Academic–Practitioner Divide in Marketing Decision Models’, Journal of Marketing, 75(4), pp. 196–210.
Wedel, M. and Kannan, P.K. (2016) ‘Marketing Analytics for Data-Rich Environments’, Journal of Marketing, 80(6), pp. 97–121.

Subject specific skills

Effectively communicate data insights using graphical, visual, and written storytelling techniques tailored for different audiences.
Extract and interpret meaningful insights from data by applying segmentation, clustering, and relationship analysis across customer datasets to derive insight.
Evaluate and apply appropriate marketing and strategy analytics techniques based on specific business contexts.
Assess the reliability and relevance of different data sources for marketing and strategy analytics, ensuring accuracy in decision-making.

Transferable skills

Demonstrate the ability to structure and communicate analytical insights clearly and persuasively in written and visual formats.
Demonstrate effective problem solving skills (both theoretically, and when it comes to the programming implementation of a selected method).