69968 - Market Research And Analysis

Learning outcomes

By the end of this course students are able to: - apply advanced statistical techniques (e.g., factor and cluster analysis) to explore and segment market data; - predictive Modeling Application: Use predictive models (e.g., decision trees, discriminant analysis) for forecasting and business decisions; - data Mining Tool Proficiency: Gain hands-on experience with SAS for analyzing commercial, administrative, and survey data; - survey Design and Methodology: Design and conduct surveys, including questionnaire drafting and bias minimization; - teamwork and Communication in Research: Collaborate in teams to produce and present a professional research report.

Course contents

This course provides an overview of key methodologies and data types used in Market
Research and Data Analysis. It introduces Data Mining and explores different types of data
(commercial, administrative, and survey data), along with advanced statistical methods
such as Factor Analysis, Cluster Analysis, and Predictive Models. Predictive techniques
include discriminant analysis, regression, and correlation.
A central part of the course involves applied teamwork using SAS software. Students design
and conduct a psychographic CRM research project based on an original questionnaire.
Project steps include topic definition, questionnaire design, treatment of response scale
bias, statistical modeling using SAS, and report writing.


Structure of the course:
1. intro sas software
2. intro sas software
3. pca – principal component analysis
4. pca – principal component analysis
5. clustering
6. size effect in opinion data
7. t-test for clustering
8. chi-test for clustering
9. discriminant analysis (no parametric)
10. discriminant analysis (no parametric)
11. confusion matrix in discriminant analysis
12. bca - binary correspondence analysis
13. bca - binary correspondence analysis
14. mca – multiple correspondence analysis
15. mca – multiple correspondence analysis
16. application in a complete case study
17. interpretation of lookalike models
18. interpretation of lookalike models
19. interpretation of lookalike models

Readings/Bibliography

Required Readings:
- Stephane Tuffery, Data Mining and Statistics for decision making, 2011, ed. Wiley
(Chapters: 1, 3, 7, 9, 11.6, 11.7)
- SAS ProgrammingThe One-Day Course - Neil Spencer Chapman and Hall/CRC 2003Print
ISBN: 978-1-58488-409-5eBook ISBN: 978-0-203-49928-3
- Lebart, L., Morineau, A., Warwick, K. M. – Multivariate Descriptive Statistical Analysis:
Correspondence Analysis and Related Techniques for Large Matrices – 1984 – New York:
Wiley.
- Benzécri, J.-P. – Multivariate Descriptive Statistical Analysis: Correspondence Analysis and
Related Techniques for Large Matrices – 1984 – Chichester: Academic Press.

Teaching methods

The main instructional approaches used in the course are:
- Lectures in informatics laboratory using software SAS in applied case studies

Assessment methods

Class attendance is mandatory for at least 70% of the hours.
Students carry out an applied group project, developed according to the standards taught
during the course. Groups (maximum 3–4 students, formed independently) work on a
psychographic CRM project, which includes: topic selection, design of an online
questionnaire, management of bias effects from opinion scales, data analysis with SAS, and
drafting of a final report.
The final assessment is an individual oral exam, during which each student discusses the
group project and answers technical questions on the methodological choices adopted.
Therefore, the final evaluation will be based on the final oral exam (for which participation
at the group project is a prerequisite).
Course evaluation will be as detailed in the following:
Component Weight (%)
Final Exam on group project 100
- Exam and group project structure:


The exam is an individual oral discussion of a group work. The rating scale ranges from 1 to
30 and is a continuous indicator. The oral exam consists of 3/4 questions.
Verification takes place through an individual oral exam in which the teacher asks technical
questions about the methodological choices made in group work. The group works are
carried out by groups of students (composed of a maximum of 3 or 4 subjects) who are
formed independently and who carry out a real psychographic CRM project.
The group project involves applying the data analysis strategies addressed in the course to
opinion data, collected by the group through an electronic questionnaire: definition of the
topic, editing of the questionnaire, treatment of the distortion effects generated by the
opinion scale, statistical modeling using the SAS software, drafting of the report to be
delivered to the teacher. Further guidelines for the technical implementation will be
provided during the course.
The evaluation of the exam answers will be based on: 1) the technical and statistical
correctness of the answers, 2) the methodological adequacy of the solution with respect to
the specific business problem under discussion.
- Exam policy:
The starting hypothesis is that the student knows the entire exam program perfectly and
that, depending on the quality of his answers, starting from 30 (maximum grade) this grade
can be reduced. The laude is awarded at the discretion of the teacher if the understanding of
the technical topics of the course is particularly thorough in the methodological details.
It is possible to reject the grade an unlimited number of times.
Grading scale:
< 18: failed
18-23: sufficient
24-27: good
28-30: very good
30 e lode: outstanding
Students with disability or specific learning disabilities (DSA) are required to make their
condition known to find the best possibile accommodation to their needs.

Teaching tools

- Learning platform: Virtuale (virtuale.unibo.it) contains the slides and the team-work
assignments
- Communication tools: Email; Forum on Virtuale


- Other digital tools: SAS software in student virtual machine

Office hours

See the website of Furio Camillo