DATA SCIENCE FOR MARKETING
Learning outcomes of the course unit
The course gives knowledge on statistical techniques for Marketing applications and for the analysis of consumer behavior. These techniques include:
multiple linear regression; logistic regression; classification trees; nonhierarchical
The aim of the course is threefold:
1. To provide both a theoretical and a practical understanding of the key methods of model building, classification and prediction.
2. To provide a Marketing-driven context for these methods.
3. Using real data and case studies, and a learning-by-doing approach, to illustrate
the application and the interpretation of these methods.
Computational aspects of the methods are addressed through the use of
MS Excel and IBM SPSS.
Knowledge of basic statistical methods, as given in undergraduate
programs in Economics and Management. Knowledge of the content of
the course "Statistical Methods for Management".
Course contents summary
The aim of this course is to explain the main statistical techniques that
are useful for Marketing applications to large data bases, with emphasis on
consumer behaviour. Specifically, the course covers:
a) multiple linear regression and its applications in Marketing;
b) logistic regression for the prediction of consumer behaviour;
c) classification trees for the prediction of consumer behaviour and for consumer segmentation;
d) cluster analysis for consumer segmentation.
The course covers both the statistical theory behind these techniques
and their applications. Emphasis is also placed on
computational aspects, through the use of MS Excel and IBM SPSS, and
by means of practical work.
M. Riani, F. Laurini, G. Morelli: Strumenti statistici e informatici per
applicazioni aziendali. Pitagora Editrice, Bologna, 2013, from Chapter 4 onwards.
A. Cerioli, F. Laurini: Il modello di regressione logistica per le decisioni
aziendali, Uni.Nova, Parma, 2013 (all but the Appendix).
S. Zani e A. Cerioli: Analisi dei dati e data mining per le decisioni
aziendali, Giuffrè, Milano, Chapter IX (Sections 1 – 2 – 11 – 12), Chapter XI.
Lectures; practical work (assisted and individual); possible seminars of
Teaching materials (course slides and data for replicating analyses) are provided through Elly.
Further materials (data for individual practical work and research readings) are also provided through Elly, as well as details on the course timetable.
Course slides are also available at the Printing Office since mid-September 2018.
Assessment methods and criteria
Written exam of 60 minutes, at which the student can bring the textbooks and a calculator.
Knowledge and understanding are assessed by
methodological questions, marked 3 grade points each. The ability of
applying knowledge and understanding are assessed by questions on
the interpretation of results, marked 3 grade points each. Learning skills
are assessed by questions on the conclusions to be drawn from an
analysis, marked 3 grade points each.
Details on examination procedures are provided to the class and made available through Elly before the start of the course.