DIGITAL MARKETING AND CUSTOMER ANALYTICS

The module addresses the evolution of marketing following digitalization and the challenges of digital marketing, with a focus on opportunities and risks of data analytics for marketing intelligence. From both a theoretical and empirical perspective the module presents different problems in contemporary marketing practice. Students are involved in hands-on exercises to develop solutions using data analytics techniques.

Learning outcomes include:
- the impact of digital technologies on marketing;
- the challenges of digital marketing;
- the use of predictive analytics over the costumer life cycle;
- data analytics for 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 and causal modeling;
- customer segmentation, marketing campagning, attrition.

Kannan, P.K., 2017. Digital marketing: A framework, review and research agenda. International Journal of Research in Marketing, 34(1), pp.22-45.
Erevelles, S., Fukawa, N. and Swayne, L., 2016. Big Data consumer analytics and the transformation of marketing. Journal of business research, 69(2), pp.897-904.

Textbooks (selected chapters)
Provost, F. and Fawcett, T., 2013. Data Science for Business: What you need to know about data mining and data-analytic thinking. O'Reilly Media, Inc.
James, G., Witten, D., Hastie, T. and Tibshirani, R., 2013. An introduction to statistical learning (Vol. 112, p. 18). New York: Springer. II Ed. (available online)

For attending students group project and individual written exam

scritto

Theoretical lessons; case studies; exercises in RStudio