03558 - Market Analysis (A-L)

Learning outcomes

The course provides the skills to understand and apply statistical techniques aimed at analyzing market data, segmenting customers and evaluating the effectiveness of marketing strategies. The objective is to develop both a critical awareness in the use of quantitative tools and operational skills through the use of SAS software. Upon completion of the course, the student will be able to:

• Understand the main sampling designs and determine sample sizes.
• Apply multivariate analysis techniques for market segmentation and product positioning.
• Evaluate the effectiveness of advertising campaigns and marketing efforts through response models.
• Use SAS software to implement the analytical techniques learned.

Course contents

 

MODULE 1 - THEORETICAL AND METHODOLOGICAL FOUNDATIONS (30 hours)

1. Introduction to market analysis.

• Role of statistics in market research. Definitions, purposes and applications of market research
• Data types and information sources.
• Data collection techniques

2. Sampling for market research.

• Simple random sampling, Stratified sampling, Clustered and multistage sampling
• Determining sample size
• Sampling and non-sampling errors

3. The measurement of market phenomena

• Consumption and demand for goods eservices
• Consumer satisfaction
• Advertising audience

4. Methods for Market Segmentation:

• Theoretical principles of segmentation
• Principal component analysis (PCA)
• Cluster analysis: hierarchical and nonhierarchical methods
• Segment profiling

5. Product and brand positioning analysis

• Key concepts
• Positioning methodologies
• Case studies

6. Advertising effectiveness evaluation:

• The phases of the advertising campaign
• Media surveys
• Perception and memorization of the message
• The measurement of advertising effectiveness: response models

 

Module 2 - SAS Lab (20 hours for each of the two shifts A-L / M-Z)


1. Introduction to SAS

• SAS architecture and working environment
• Loading and managing data
• Basic syntax and fundamental procedures

 

2. Implementation of sampling methods


3. Segmentation in SAS

• PROC PRINCOMP for principal component analysis
• PROC CLUSTER and PROC FASTCLUS for cluster analysis
• Output interpretation and results validation
• Cluster profiling
  
  

Readings/Bibliography

The lectures are based on material taken mainly from the following volumes:

(Required) Bassi, F., Ingrassia, S. Statistica per le analisi di mercato: metodi e strumenti, 2022, Pearson, p. 406 (Chapters 1-3,5,7-8,10)

(Recommended) De Luca, A., Marketing models, Statistics for market analysis, p. 496 Year of publication: 2016, Franco Angeli (Chapters 1-2,5,7-8)

(Recommended) SERGIO BRASINI, MARZIA FREO, FRANCO TASSINARI, GIORGIO TASSINARI , Marketing and advertising: Tools and models for statistical analysis, p. 312, (Chapter 4), Il mulino, 2010.

Supplementary materials:

 Teaching handouts by the lecturer available at http://virtuale.unibo.it/

Real datasets for practical exercises

Online SAS manuals and tutorials

Selected scientific articles

Teaching methods

The Market Analysis course combines theoretical lectures and applied activities, with the aim of providing students with the necessary knowledge and skills to analyse and interpret real data in business and market contexts.

• Lectures include the presentation of theoretical and applied content relating to the main statistical analysis tools and their possible applications in the context of market analysis

• Computer lab activities are carried out using the SAS statistical software. During these sessions, applications on real datasets from business and market environments are introduced, replicated and commented on.

• Analysis of real datasets relating to marketing campaigns, customer satisfaction or consumer behaviour.

• Students are invited to solve empirical case studies. Case studies to be carried out at home (optional) will serve to reinforce the concepts of the course and to familiarise with data analysis and interpretation. Case studies will not be assessed. Solutions (or simply feedback from the lecturer) will be provided for self-assessment.

• In view of the type of activity and teaching methods adopted, the attendance of this training activity requires the prior participation of all students in Modules 1 and 2 of training on safety in the workplace, [https://elearning-sicurezza.unibo.it/] in e-learning mode.
  

Assessment methods

Course Assessment

The course assessment consists of two separate components, designed to evaluate both theoretical understanding and the practical application of the methodologies learned:

• Written Exam (Theoretical Module) – Duration: 2 hours
– 2 open-ended theoretical questions aimed at assessing knowledge of the studied methodologies and the ability to critically interpret concepts;
– 1 applied exercise requiring calculations and analysis of results based on data or simulated scenarios.

Note: The theoretical exam is closed-book — consultation of any materials is not allowed.

• Practical Exam (SAS Laboratory) – Duration: 90 minutes

This exam component aims to verify the achievement of the following objectives:

• Ability to apply the tools presented in the theoretical module to solve real-world problems using SAS software;

• Ability to interpret the obtained results in order to effectively support decision-making processes.

The exam will include 3 questions, each reflecting a different type of task:

• Detailed commentary on a SAS output already produced and provided by the instructor;

• Detailed commentary on a SAS program, related to one of the topics covered in class and provided by the instructor;

• Active use of SAS to perform an analysis on a given dataset.

Note: For the practical exam, the instructor will provide the necessary SAS code for consultation.

Final Grade Calculation
The final grade is calculated as the arithmetic mean of the scores obtained in the two components (theoretical written exam and practical lab exam).
It is not required to pass each component individually (i.e., score at least 18/30 in both); however, the overall arithmetic mean must be at least 18/30 for the exam to be considered passed.

Example:
– If a student scores 16/30 on the theoretical part and 20/30 on the practical part, the average is 18/30 → exam passed.
– If a student scores 14/30 and 17/30, the average is 15.5/30 → exam not passed, even though neither score is far below the passing mark.

Final Grade Scale (based on the average score):

• <18: Failed

• 18–23 (Sufficient): basic preparation, limited to a subset of course content;

• 24–27 (Good): adequate preparation, with some gaps in the course content;

• 28–30 (Very Good): thorough understanding of all course content;

• 30 cum laude (Excellent): outstanding knowledge of the course content.

Teaching tools

The UNIBO e-learning platform (VIRTUALE) will be used to share teaching materials and assign students periodic homework. The teaching materials include:

• Lecture slides summarizing the theoretical topics covered in class

• Open data and lecture slides to support the practical sessions

• Miscellaneous materials: exercises, homework solutions, sample exams, and follow-up resources

• SAS On Demand for Academics software (https://www.sas.com/en_us/software/on-demand-for-academics.html )

Office hours

See the website of Ida D'Attoma