STATISTICAL METHODS FOR MARKETING DECISIONS LABORATORY

The course is one of the activities chosen by the student in the Master’s degree program in Marketing and Communication that allows students to acquire knowledge and understanding of some of the main statistical concepts and their use in marketing activities. The aim of the course is to provide students with some advanced techniques of multivariate analysis for visualizing, mining, and interpreting information available through questionaries and business databases with the aim of planning strategies to support and improve the decision process by means of the open source statistical software R.

At the end of the course, students will be expected to have acquired the skills to develop a critical, personal and rigorous analysis of marketing phenomena through tools and statistical methods suitable for the analysis. They must also be able to present in a communicative way the results obtained and the strategies proposed deriving from the models developed. In particular, students should:
1. Knowledge and understanding
- know the terminology and basic concepts of statistics in marketing .
- understand the strengths and limitations of the statistical approaches used to analyze real phenomena.
- know the basic multivariate statistical models such as the principal components and the cluster analysis for marketing phenomena.
2. Ability to apply knowledge and understanding
- understand the main aspects of the conducted statistical analyses;
- know how to determine the best statistical models for the multivariate analysis
- know how to use the open source statistical software R.
3. Making judgements
- be able to critically assess under which circumstances the analyses are reliable
- be able to assess the goodness of the estimated models evaluating different models for market segmentation.
4. Communication
- know how to present, discuss and prove the information achieved by the analyses
- know how to argue marketing problem in an effective way.

The student must know the fundamentals of Statistics and the most common multivariate analysis techniques (Multiple linear regression, basics of Principal Component Analysis and Cluster Analysis). For the fundamentals of Statistics, see for example the book: Anderson, D. R., Sweeney, D., Williams, T. (2014) Statistica per le analisi economico-aziendali, Apogeo, or chapters 1-12 of: Illowsky, D., Dean, S. (2022) Introductory Statistics, OpenStax (freely available at link https://openstax.org/details/books/introductory-statistics?Book%20details ). For the multivariate analysis techniques, see for example chapters 3, 4.3, 12.2 e 12.4 of the book: James et al. (2021) An Introduction to Statistical Learning, Springer (freely available at link https://www.statlearning.com ).

1. Organization of data collected by questionnaire or other datasets
2. Visualizing information and preliminary statistical analyses
3. Reducing the dimensionality of the data matrix
4. Statistical multivariate analysis for marketing research
To support the theoretical knowledges acquired during the course, each topic will be developed by using the R statistical software. In particular, R will be briefly introduced, and the approaches and models used in the analyses will be developed used particular packages provided in R.

Notes, slides, data and other material necessary to follow lectures and to attain the intended learning outcomes are downloadable from the e-learning platform moodle.unive.it.

Referral texts:
1. C.N. Chapman, E. McDonnell Feit (2015) R for Marketing Research and Analytics. Springer International Publishing.
2. G. James, D. Witten, T. Hastie (2020) Introduzione all'apprendimento statistico. Con applicazioni in r. Piccin-Nuova Libraria.

Additional readings
Other reading material suggested by the teacher during the course

The exam consists of a preparation and a presentation about the statistical analysis of the collected data.
In particular, the exam aims to verify that the student has acquired the concepts presented during the lessons, is familiar with the software and has learned how to integrate this knowledge and skills to solve marketing problems.
The assessment of the work will take into account the following elements:
• Correct use of statistical methods
• Correct interpretation of the results
• Clear language
• Originality of the analysis and variety of techniques employed for the study problem
• Ability to summarize essential and most relevant information for the problem.
Each element will adequately contribute to the formation of the grade, regardless of whether the student is an attendee or non-attendee, respecting the following gradation:
A. Scores in the 18-22 range will be assigned in the presence of:
•⁠ ⁠sufficient knowledge and applied understanding with reference to the course program;
•⁠ ⁠limited ability to apply knowledge by formulating independent judgments;
•⁠ ⁠sufficient communication skills, especially in relation to the use of the specific language of the statistical methods used;
B. Scores in the 23-26 range will be assigned in the presence of:
•⁠ ⁠fair knowledge and applied understanding with reference to the course program;
•⁠ ⁠fair ability to apply knowledge by formulating independent judgments;
•⁠ ⁠fair communication skills, especially in relation to the use of the specific language of the statistical methods used;
C. Scores in the 27-30 range will be assigned in the presence of:
•⁠ ⁠good or excellent knowledge and applied understanding with reference to the course program;
•⁠ ⁠good or excellent ability to apply knowledge by formulating independent judgments;
•⁠ ⁠fully appropriate communication skills, especially in relation to the use of the specific language of the statistical methods used;
D. Honors will be awarded in the presence of excellent knowledge and applied understanding with reference to the program, judgment ability, and communication skills.

The course consists of fifteen lessons to introduce various methods for organizing, visualizing, and analyzing the collected data through the statistical software R (www.r-project.org).

Italian

1. È richiesto che gli studenti si registrino sulla pagina del corso della piattaforma e-learning di ateneo moodle.unive.it

2. Accessibilità, Disabilità e Inclusione
Accomodamenti e Servizi di Supporto per studenti con disabilità o con disturbi specifici dell’apprendimento
Ca’ Foscari applica la Legge Italiana (Legge 17/1999; Legge 170/2010) per i servizi di supporto e di accomodamento disponibili agli studenti con disabilità o con disturbi specifici dell’apprendimento. Se hai una disabilità motoria, visiva, dell’udito o altre disabilità (Legge 17/1999) o un disturbo specifico dell’apprendimento (Legge 170/2010) e richiedi supporto (assistenza in aula, ausili tecnologici per lo svolgimento di esami o esami individualizzati, materiale in formato accessibile, recupero appunti, tutorato specialistico a supporto dello studio, interpreti o altro) contatta l’ufficio Disabilità e DSA disabilita@unive.it.

written and oral