MARKET DATA ANALYSIS
Instructional goals
Providing multivariate statistical tools for marketing research.
Intended learning outcomes
Knowledge and understanding:
Statistical methods and Market Research.
Statistical learning: supervised and unsupervised. Parametric and non-parametric methods.
Sampling.
Matrix representations of multidimensional data. Graphical representations of multidimensional data.
Unsupervised learning. Cluster Analysis and Market Segmentation. Principal Component Analysis and Positioning.
Supervised learning. Multivariate Linear Regression. Logistic Regression. Tree-based methods. Regression and Classification Trees.
Application of AI and Generative AI to market research.
Applied knowledge and understanding:
Research design and the ability to select appropriate data processing techniques for business and marketing contexts.
Making judgements:
Ability to collect, process, and critically interpret quantitative and qualitative data related to business and marketing phenomena.
Communication skills:
Ability to effectively communicate data analysis results — developed through the presentation of empirical research findings.
Learning to learn:
Ability to independently learn data analysis techniques for professional activities or further academic study.
Course Contents
Statistical Learning and Market Research
Information support systems, statistical sampling, and data collection techniques in market research.
Databases: basic concepts.
Matrix representations of multidimensional data. Data matrix, data cleaning, data pre-processing, covariance and correlation matrices, proximity matrices.
Graphical representations of multidimensional data.
Unsupervised learning.
Multivariate statistical techniques for marketing.
Cluster Analysis (hierarchical, non-hierarchical) and Segmentation
Principal Component Analysis and Market Positioning
Supervised learning.
Multiple Linear Regression models and extensions Logistic Regression.
Decision Trees (Classification and Regression Trees).
Applications of Artificial Intelligence and Generative AI to market research.
Applications to real-world data using R statistical software.
Case studies.
Reference Books
- Notes of the teacher.
- Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. Introduction to Statistical Learning with Applications to R:
https://www.statlearning.com/
Teaching Methods
Lectures, Computer-based Exercises, Case Studies, Seminars.
Assessment Method
WRITTEN EXAM.
Students attending at least 70% of the lessons.
INTERMEDIATE TEST IN PRESENCE (weight 30%): exercises inclusive of multiple choice questions and reading R-code output.
IN-PRESENCE TEST (weight 70%): exercises inclusive of multiple-choice questions and reading R-code output (60%); discussion Project Work (10%).
The Project Work consists of applying the techniques learned to a market research project using real data.
Non-compliant students or students exempted from the attendance requirement. IN-PERSON TEST (100%). Exercises including multiple choice questions (70%) and testing of ability to use R for data analysis (30%).
The exam will be based on an appropriate teaching load to compensate for non-participation in classroom activities.
Thesis assignment criteria
To be agreed with the teacher.
Week 1
Statistical methods and marketing research: introduction.
Statistics and marketing research: market segmentation, positioning and statistical methods for data analysis.
Introduction to R statistical software for marketing research.
Week 2
Data Matrices and Transformations. Eigenvalues. Eigenvectors. Distance and Similarity Matrices.
Introduction to R statistical software for marketing research.
Week 3
ampling Theory in Marketing Research.
Exercise session.
R Lab with applications to market research.
Week 4
Unsupervised Learning.
Hierarchical Clustering: methods and properties.
Exercise session.
R Lab with applications to market research.
Week 5
Unsupervised Learning.
Non-Hierarchical Clustering: methods and properties.
Exercise session.
R Lab with applications to market research.
Week 6
Unsupervised Learning.
Principal Component Analysis
Exercise session.
R Lab with applications to market research.
Week 7
Unsupervised Learning.
Principal Component Analysis.
Exercise session.
R Lab with applications to market research.
Week 8
Supervised Learning.
Multiple Linear Regression and extensions.
Exercise session.
R Lab with applications to market research.
Week 9
Supervised Learning.
Multiple Linear Regression and extensions.
Exercise session.
R Lab with applications to market research.
Week 10
Regression with a binary dependent. variable: Logistic Regression.
Exercise session.
R Lab with applications to market research.
Week 11
Supervised Learning.
Topic: Decision Trees — Regression and Classification Trees.
Exercise session.
R Lab with applications to market research.
Week 12
Applications of AI and Generative AI to Market Research.
Exercise session.
R Lab with applications to market research.