STATISTICAL METHODS FOR MARKETING
Instructional goals
Providing multivariate statistical tools for marketing research
Intended learning outcomes
Knowledge and understanding: Matrix representations of multidimensional data. Graphical representations of multidimensional data.
Multivariate statistical techniques for marketing: Cluster Analysis and Segmentation; Principal Component Analysis; Association Rules and Market Basket Analysis; Linear regression model and analysis of consumer behavior; Tree methodologies in marketing research
Applying knowledge and understanding: research designs, ability to select appropriate data processing techniques for the business and marketing environment
Making judgments: ability to collect, process and critically interpret quantitative and qualitative data relating to business and marketing phenomena
Communication skills: effective communication skills of data analysis - achieved through written tests and communication of research results on empirical data.
Learning skills: ability to learn autonomously data analysis techniques in professional activities or subsequent studies
Course Contents
1. Statistics and marketing research.
2. Information support, statistical sampling and marketing surveys.
3. Marketing research and data analysis.
4. Data matrix, data clearing, data pre-processing, covariance matrix, correlation matrix, proximity matrices.
5. Graphics for multidimensional data.
6. Statistical methods for marketing research: Cluster Analysis, Principal Component Analysis, Association rules and Market Basket Analysis, Linear regression model, Tree–based methodologies.
7. Marketing applications with R.
8. Case studies in traditional marketing, geomarketing, web marketing, social media marketing and neuromarketing.
Reference Books
- Appunti delle lezioni (P. D'Urso).
- Analisi dei dati multidimensionali con R (appunti) (R. Massari).
- Analisi dei dati e data mining per le decisioni aziendali (S. Zani, A. Cerioli), Giuffrè Editore, 2007.
Teaching Methods
Classroom teaching, exercises with PC, discussion of case studies, Seminars.
Assessment Method
WRITTEN EXAM, INTERMEDIATE WRITTEN ASSESSMENT (weight 50%), ASSIGNMENT with SLIDES
Thesis assignment criteria
The students must pass the exam with at least 28/30.
Does the syllabus cover sustainability topics?
Yes.
Week 1 Contenuto sessioni on line e on campus
Statistical methods and marketing research: introduction.
Statistics and marketing research: market segmentation, positioning and statistical methods for data analysis.
Introduction to R.
Week 2 Contenuto sessioni on line e on campus
Information support for marketing research.
Statistical surveys.
Sampling theory.
Week 3 Contenuto sessioni on line e on campus
Data analysis and data mining: introduction, data matrix and data cleaning.
Data pre-processing.
Sampling theory.
Week 4 Contenuto sessioni on line e on campus
Proximity matrices: distance matrices, similarità/dissimilarity matrices.
Distance matrices: examples.
Graphics for multidimensional data and application in R.
Week 5 Contenuto sessioni on line e on campus
Segmentation and hierarchical cluster analysis.
Hierarchical cluster analysis: methods and properties.
Market Basket Analysis: theory.
Week 6 Contenuto sessioni on line e on campus
Hierarchical cluster analysis: dendrogram, optimal partition, examples.
Single Linkage method: examples and applications.
Market Basket Analysis: applications in R.
Week 7 Contenuto sessioni on line e on campus
Complete Linkage method: examples and applications.
Constrained clustering methods with applications to geomarketing.
Cluster analysis: marketing applications.
Week 8 Contenuto sessioni on line e on campus
Hierarchical cluster analysis: exercises.
Segmentation and non hierarchical clustering methods: theory.
Linear regression model: theory.
Week 9 Contenuto sessioni on line e on campus
Non hierarchical clustering methods and segmentation: discussion of case studies.
Non hierarchical clustering methods and segmentation: discussion of case studies in web marketing, social media marketing and neuromarketing.
Linear regression model: theory.
Week 10 Contenuto sessioni on line e on campus
Covariance matrix and correlation matrix. Positioning and Principal Component Analysis.
Principal Component Analysis: theory.
Linear regression model: application in R and discussion of case studies.
Week 11 Contenuto sessioni on line e on campus
Tandem Analysis.
Tree-based methods: theory and web marketing applications.
Linear regression model: application in R.
Week 12 Contenuto sessioni on line e on campus
Tandem Analysis: web marketing applications.
Social Media Marketing, Web monitoring and data analysis.
Principal Component Analysis in R.