ID:
MASMIT19
Duration (hours):
54
CFU:
9
SSD:
STATISTICA
Located in:
PESCARA
Url:
DIGITAL MARKETING/CORSO GENERICO Year: 1
Year:
2025
Course Catalogue:
Overview
Date/time interval
Secondo Quadrimestre (07/01/2026 - 17/04/2026)
Syllabus
Course Objectives
The course aims to present the main statistical techniques for analyzing business information involving multiple variables simultaneously, with particular focus on quantitative data. The techniques covered include linear regression, logistic regression, hierarchical and non-hierarchical cluster analysis, and principal component analysis. The data analyzed may come from internal company sources, such as sales data of goods or services, or be collected through sample surveys (market research) or extracted from the Web. The goal of multidimensional analysis is to provide a rigorous and rational knowledge base to support effective strategic marketing decisions, with particular reference to digital marketing and the dynamics of online channels. The course combines methodological insights, essential for understanding the techniques and correctly interpreting the results, with a practical approach based on learning by doing. Active participation in lectures and practical exercises using the R statistical environment will enable students to independently analyze relevant data to address challenges related to both traditional and digital marketing. By the end of the course, students will be able to apply the learned statistical methods to perform descriptive and predictive analyses, identify target customer segments, and analyze customer behavior to detect and prevent churn, developing targeted and personalized marketing strategies also in digital contexts.
Course Prerequisites
Although no prerequisite is required, it is recommended to have a basic understanding of fundamental statistical concepts.
Teaching Methods
The course consists of 54 hours of instruction, delivered through face-to-face teaching, including theoretical lessons and practical laboratories using R. For further information, you can contact Prof.ssa Annalina Sarra at: annalina.sarra@unich.it
Assessment Methods
The assessment of students’ learning outcomes consists of both a theoretical and a practical part: The theoretical part may be an oral or written exam, depending on the number of enrolled students, aimed at evaluating the knowledge of the statistical techniques covered during the course. The practical part involves the presentation of a group project carried out using the R software/language. This activity is intended to assess the application of statistical techniques to marketing case studies, the interpretation of results, and familiarity with the main objects handled in R.
Texts
Levine DM, Krehbiel TC, Berenson ML (2018) Statistics, Apogeo, Milan. for the part relating to Confidence Intervals, Hypothesis Testing, Regression Model Sergio Zani, Andrea Cerioli Data Analysis and Data Mining for Business Decisions. Giuffrè Editore 2007 Chapters I Data matrices and univariate analyses VI - Principal component analysis VIII - Distances and Similarity Indices IX- Analysis of groups Further books: -Chris Chapman and Elea McDonnell Feit, 2015. R for Marketing Research and Analytics. Springer. -Tonio Di Battista, 2014. Metodi statistici per la valutazione. Franco Angeli -The supplementary teaching material for the exercises with R will be published by the teacher on fad.unich
Contents
The course aims to provide students with both theoretical knowledge and practical skills necessary for the in-depth analysis of digital marketing data through the use of multivariate statistical techniques. Particular attention will be given to the use of R software for exploring, modeling, and interpreting data from online sources. In line with the professional profile defined by the degree program, the course focuses on developing the following competencies: 1. Knowledge and Understanding Collect, organize, and manage complex, high-dimensional datasets generated by digital marketing activities. Appropriately apply multivariate statistical techniques (e.g., Principal Component Analysis, Cluster Analysis, Multiple Regression, Logistic Regression) to identify patterns and market segmentations. Use the R programming language to implement core analytical procedures in real-world digital contexts. 2. Independent Judgment Critically select the most appropriate multivariate techniques based on marketing objectives and dataset structure. Independently interpret the results of statistical analyses, assessing their robustness and relevance for strategic decision-making. Formulate data-driven recommendations, even in complex or uncertain contexts. 3. Communication and Applied Skills Communicate the results of multivariate analyses using appropriate statistical language tailored to the target audience (e.g., managers, clients, technical teams). Translate analytical results into operational insights to inform marketing campaigns, content personalization, user profiling, and optimization of the customer journey. Use data visualizations strategically to support presentations and reports.
Course Language
Italian
More information
ERASMUS students are invited to contact the instructor to arrange their study program. Additionally, ERASMUS students have the option to take the exam in English.
Degrees
Degrees
DIGITAL MARKETING
Master’s Degree
2 years
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People
People
Docenti di ruolo di IIa fascia
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