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QUANTUM METHODS AND MARKETING FORECASTING LAB

International Teaching QUANTUM METHODS AND MARKETING FORECASTING LAB

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0323200018
DEPARTMENT OF POLITICAL AND COMMUNICATION SCIENCES
EQF7
DIGITAL MARKETING
2025/2026

OBBLIGATORIO
YEAR OF COURSE 2
YEAR OF DIDACTIC SYSTEM 2024
AUTUMN SEMESTER
CFUHOURSACTIVITY
945LAB
LUIGI RARITA' T Curriculum
Objectives
THE COURSE AIMS TO PROVIDE STUDENTS OPERATIONAL RESEARCH AND DATA ANALYSIS TECHNIQUES TO APPLY WITHIN THE CONTEXT OF DIGITAL MARKETING. STUDENTS WILL ACQUIRE PRACTICAL SKILLS IN USING MODELS AND ALGORITHMS OF OPERATIONAL RESEARCH AND DATA ANALYSIS TO ANALYSE DATA, MAKE PREDICTIONS, TAKE DECISIONS, IMPLEMENT STRATEGIES OF PREDICTIVE MARKETING, CARRY OUT INVESTIGATIONS ABOUT THE BEHAVIOUR OF CUSTOMERS, IDENTIFY EMERGING TRENDS AND VARIATIONS IN THE DEMAND OF CONSUMERS.
THE COURSE WILL FOCUS ON THE PRACTICAL APPLICATION OF SUCH METHODS THROUGH THE USE OF SOFTWARE TOOLS AND REAL-WORLD CASE STUDIES.

KNOWLEDGE AND UNDERSTANDING
THE STUDENT WILL ACQUIRE KNOWLEDGE OF THE MAIN MODELS OF OPERATIONAL RESEARCH AND DATA ANALYSIS TECHNIQUES FOR MODELLING, SYNTHESIS, PREDICTION AND CLASSIFICATION IN THE MARKETING CONTEXT.

APPLYING KNOWLEDGE AND UNDERSTANDING
THE STUDENT WILL BE ABLE TO:
-APPLY ADVANCED STATISTICAL METHODS TO SYNTHESIZE INFORMATION, MAKE PREDICTIONS AND CLASSIFICATIONS BY MEDIUM AND LARGE DATA SETS;
-GROUP ELEMENTS WITH SIMILAR CHARACTERISTICS BY BUILDING AND INTERPRETING CLUSTER ANALYSIS MODELS;
-APPLY OPERATIONS RESEARCH TECHNIQUES FOR MODELING AND OPTIMIZATION OF REAL PROBLEMS;
-IMPLEMENT PREDICTIVE MARKETING STRATEGIES;
-CARRY OUT INVESTIGATIONS ABOUT THE BEHAVIOUR OF CONSUMERS;
-PERFORM ANALYSES USING STATISTICAL SOFTWARE.

MAKING JUDGEMENTS
THE STUDENT WILL BE ABLE TO:
-IDENTIFY THE MOST APPROPRIATE METHODS TO EFFICIENTLY SOLVE PROBLEMS IN A WORK CONTEXT;
-EXPRESS AUTONOMOUS EVALUATIONS ABOUT THE VALIDITY AND FEASIBILITY OF DIFFERENT TECHNIQUES AND UNDERSTAND THEIR IMPACT ON THE RESULTS OF THE ANALYSES.

COMMUNICATION SKILLS
THE STUDENT WILL BE ABLE TO COMMUNICATE THE RESULTS OF THE INTERPRETATION OF DATA AND OF THE CONDUCTED ANALYSES BOTH TO PROFESSIONALS IN THE SECTOR AND TO NON-EXPERTS IN THE SUBJECT.

LEARNING SKILLS
THE STUDENT WILL BE ABLE TO APPROACH PROBLEMS CRITICALLY AND APPLY THE KNOWLEDGE AND SKILLS ACQUIRED IN USING TECHNOLOGICAL TOOLS IN CONTEXTS DIFFERENT FROM THOSE PRESENTED DURING THE COURSE.
Prerequisites
FOR AN EASIER UNDERSTANDING OF THE COURSE CONTENTS, BASIC MATHEMATICAL KNOWLEDGE AND SKILLS ABOUT MATHEMATICAL ANALYSIS, LINEAR ALGEBRA, PROBABILITY THEORY AND STATISTICS ARE REQUIRED.
MANDATORY PREPARATORY TEACHINGS
NONE
Contents
DATA ANALYSIS TECHNIQUES
REGRESSION
(LECTURE/ PRACTICE/LABORATORY HOURS 2/3/4)
PREDICTIVE MODELS FOR THE CONSUMER BEHAVIOUR: SIMPLE AND MULTIPLE LINEAR REGRESSION; SIMPLE AND MULTIPLE LOGISTIC REGRESSION. PRACTICAL APPLICATIONS IN MARKETING FORECASTING PROBLEMS.
CLUSTER ANALYSIS
(LECTURE/ PRACTICE/LABORATORY HOURS 2/3/4)
DISTANCE MATRIX. STEPWISE CLUSTER ANALYSIS. ALGORITHMS AND SOFTWARE TOOLS FOR CLUSTER ANALYSIS. PARTITIONAL ALGORITHMS. AGGLOMERATIVE HIERARCHICAL ALGORITHMS. USE OF CLUSTER ANALYSIS TECHNIQUES TO IDENTIFY HOMOGENEOUS GROUPS OF CONSUMERS.
FORECASTING TECHNIQUES
(LECTURE/ PRACTICE/LABORATORY HOURS 2/3/4
FORECASTING METHODS: ELEMENTS OF CHOICE OF THE FORECASTING METHOD; SALES FORECASTING METHODS; MAIN SELF-PROJECTIVE MODELS.
TIME SERIES ANALYSIS: THE DECOMPOSITION PHASES OF A TIME SERIES.
MAIN OPERATIONAL RESEARCH MODELS
DESCRIPTIVE MODELS
(LECTURE/ PRACTICE/LABORATORY HOURS 2/3/4)
MARKOVIAN PROCESSES: METHODOLOGY AND APPLICATION TO THE MARKET ANALYSES.
QUEUEING SYSTEMS: DISTRIBUTION OF ARRIVALS AND SERVICE TIMES; ESTIMATION OF PERFORMANCES.
DECISION-MAKING MODELS
(LECTURE/ PRACTICE/LABORATORY HOURS 2/3/4)
MATHEMATICAL PROGRAMMING: LINEAR AND NON-LINEAR PROGRAMMING; DYNAMIC PROGRAMMING; GRAPH THEORY; APPLICATIONS.


Teaching Methods
THE COURSE WILL BE STRUCTURED THROUGH THEORETICAL LESSONS, EXERCISE SESSIONS, PRACTICAL LABORATORY SESSIONS WITH THE USE OF SPECIALIZED SOFTWARE, AND CASE STUDIES.
STUDENTS WILL BE REQUIRED TO WORK IN GROUPS TO APPLY THE LEARNED CONCEPTS TO REAL-WORLD MARKETING SITUATIONS.
ATTENDANCE OF CLASSROOM LESSONS AND EXERCISES, ALTHOUGH NOT MANDATORY, IS HIGHLY RECOMMENDED TO FULLY ACHIEVE THE LEARNING OBJECTIVES.
Verification of learning
THE EXAM, WHICH FORESEES A MARK OUT OF THIRTY, CONSISTS OF THE DEVELOPMENT AND PRESENTATION OF A PROJECT TO BE DEVELOPED AND SOLVED WITH THE MATHEMATICAL AND STATISTICAL TOOLS PRESENTED IN THE COURSE.
STUDENTS WILL BE ASSESSED IN TERMS OF:
-CORRECTNESS OF THE TECHNICAL LANGUAGE;
-FORMALIZATION OF ADEQUATE CONCLUSIONS FOR THE ANALYSIS OF REAL CASE STUDIES;
-QUALITY OF THE CONTENTS DESCRIBED DURING THE ORAL TEST.
LAUDE WILL BE AWARDED TO STUDENTS WHO SHOW AN EXCELLENT KNOWLEDGE OF THE COURSE CONTENTS, EXCELLENT PRESENTATION SKILLS AND THE ABILITY TO APPLY THE KNOWLEDGE ACQUIRED TO SOLVE PROBLEMS THAT HAVE NOT BEEN FACED DURING THE COURSE.
Texts
THE COURSE TEACHING NOTES WILL BE AVAILABLE IN THE DEDICATED TEACHING SECTION WITHIN THE UNIVERSITY’S E-LEARNING PLATFORM (HTTP://ELEARNING.UNISA.IT), ACCESSIBLE TO COURSE STUDENTS VIA THEIR UNIQUE UNIVERSITY CREDENTIALS.
More Information
THE COURSE IS TAUGHT IN ENGLISH.
  BETA VERSION Data source ESSE3