Academic year
2023/2024 Syllabus of previous years
Official course title
Course code
EM1412 (AF:382720 AR:208964)
On campus classes
ECTS credits
Degree level
Master's Degree Programme (DM270)
Educational sector code
1st Term
Course year
Go to Moodle page
The module's objective is to understand the challenges of digital marketing by focusing on the application of data analytics to contemporary marketing practice. Lessons will match marketing theory with marketing problems and machine learning or statistical models. Students are actively involved in hands-on coding exercises to develop solutions using data analytics techniques.
Learning outcomes include:
- the impact of digital technologies on marketing;
- the challenges of digital marketing;
- the choice between of prediction and causality;
- the choice between supervised and unsupervised learning algorithms;
- the application of data analytics to marketing intelligence: marketing campaigning, advanced profiling, anti-churn
Statistics and basic coding in R
The course will cover the following topics:
- digital technologies in marketing;
- CRM and consumer behaviour;
- marketing analytics;
- predictive modeling vs causal modeling;
- supervised vs unsupervised machine learning;
- customer segmentation, marketing campaigning, attrition.
1. Carlei, V. and Nuccio, M., 2014. Mapping industrial patterns in spatial agglomeration: A SOM approach to Italian industrial districts. Pattern Recognition Letters, 40, pp.1-10.
2. DeMartino, G.F., 2021. The specter of irreparable ignorance: counterfactuals and causality in economics. Review of Evolutionary Political Economy, pp.1-24.
3. Fabrizi A. & Banoub T. 2012 HP SPS Next Best Offer: how to re-think your marketing, HP Technical White Paper
4. Kitchin, R. "Big Data, new epistemologies and paradigm shifts." Big data & society 1, no. 1 (2014)
5. Majnik, M. and Bosnić, Z., 2013. ROC analysis of classifiers in machine learning: A survey. Intelligent data analysis, 17(3), pp.531-558.
6. Nuccio, M., and Guerzoni M. "Big data: Hell or heaven? Digital platforms and market power in the data-driven economy." Competition & Change 23, no. 3 (2019): 312-328.
7. Pearl, J., 2018. Theoretical impediments to machine learning with seven sparks from the causal revolution. arXiv preprint arXiv:1801.04016.
8. Verbraken, T., Wouter V., and Bart Baesens. "A novel profit maximizing metric for measuring classification performance of customer churn prediction models." IEEE transactions on knowledge and data engineering 25, no. 5 (2012): 961-973.
9. Wang, W., Feng, Y. and Dai, W., 2018. Topic analysis of online reviews for two competitive products using latent Dirichlet allocation. Electronic Commerce Research and Applications, 29, pp.142-156.
10. Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97-121.
For attending students, final evaluation will include:
- a group assignment (60%) to be submitted by the first exam date
- a class presentation (20%) based on two exercises on real data
- a written exam (20%) where one can choose one question out of three (theory)

Non-attending students will be evaluated on a written exam based on 3 questions and one exercise on RStudio.
Theoretical lessons; case studies; exercises in RStudio
Students are invited to participate in data challenge specifically conceived for the module by Open Data Playground. Here’s the link with all information:
Definitive programme.
Last update of the programme: 06/09/2023