MARKETING STATISTICS AND METRICS

Single discipline educational activity

Course Sheet

Course code:

MASMIT19

Academic Year of enrolment:

2023

Year of supply:

2023/2024

Type of Course:

Characterising

Degree:

Course of 2nd Cycle Degree in DIGITAL MARKETING

Disciplinary Sector:

Statistics

Language:

Italian

Location:

PESCARA

Credits:

Programme Year:

Professor and Collaborators:

SARRA Annalina

Cycle:

First four months

Hours of classroom activity:

Individual study hours:

171

Prerequisites:

Although no prerequisites are required, it is recommended to know the
essential concepts of basic statistics.

Objectives

The course aims to illustrate the statistical techniques for the processing
of information of business interest, considering several variables at the
same time, and in particular those of a quantitative type. These
techniques include: linear regression model, logistic regression,
hierarchical and non-hierarchical cluster analysis, principal component
analysis, data can be from internal sources, such as those relating to sales of goods or services produced, or they can be obtained through
sample surveys (market research) or obtained from the Web. The goal of
multidimensional data analysis is to provide rational cognitive support for
decisions regarding the marketing strategies to be pursued.
The skills taught in the course include both methodological aspects,
essential for understanding the techniques and interpreting the results,
and the use of the learning by doing approach. Participation in classroom
activities and carrying out exercises, through the use of the statistical
environment R, will increase the student's ability to independently
process relevant data for the solution of marketing and digital marketing
problems. At the end of the course, students will have to become familiar
with the statistical methods indicated above, to perform descriptive and
predictive analyzes, identify customer segments to contact, analyze
customer behavioral data to identify and prevent customers churn with
ad hoc marketing strategies.

Contents

The course aims to provide participants with the theoretical and technical
knowledge necessary for an in-depth understanding of business
phenomena through the analysis of available market data.
With respect to the professional profile that the course of study aims to
train, teaching is aimed at developing the following skills:
1)Knowledge and understanding:
- to collect and organize market data;
- to analyze big data with the statistical software R;
- to capture information to support marketing strategies via advanced
statistical methods.
2)Autonomy of judgment:
- to independently choose the most suitable type of analysis based on the
context and the type of data available;
- to autonomously interpret the results obtained form an advanced
statistical analysis without having to resort to external experts.
3)Communication / application skills:
- to use the appropriate statistical terminology with respect to the type of
analysis conducted,
- to apply the acquired knowledge for the diagnosis and understanding of
business phenomena;
- to cleverly communicate the results of a statistical analysis based on
the objectives to be pursued and the recipient of the report.

Extended Syllabus

• Data matrices and summary statistics for multivariate data.
• Introduction to the statistical environment R.
• First analyses of multidimensional data.
• Basics of linear algebra: vectors, matrices, relationships, operations,
rank, determinant.
• Simple and multiple regression models. Least squared estimation.
Criteria of model fitting. Residual analysis. Inference on the parameters of
regression models.
• Logistic regression
• Cluster analysis: hierarchical and non-hierarchical classification
methods.
• Reduction of data dimensionality via Principal Component Analysis.
• Exercises in R for the prediction of Customer Life Value, Churn
Prevention in online Marketing, the analysis of the principal components
for CRM data, the segmentation in digital Marketing.

Recommended Bibliography

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

Methods of Provision

Conventional

Teaching Methods

The course includes 54 hours of lessons, which will be taught through
lectures organized in theoretical lessons and R laboratories.

For information contact Prof. Sarra Annalina: annalina.sarra@unich.it

Evaluation methods

Verification of learning:

The assessment will be based on two parts: theoretical and practical
- The theoretical component is assessed either via oral or written (on the
basis on the number of students) format. It aims to evaluate the
comprehension of the statistical techniques presented during the course.
- The practical component is assessed through group-work presentations,
in which the participants show the correct application and usage of the
tools presented during the course and the interpretation of results for
marketing case studies in R.

Contacts/More Information

ERASMUS students are invited to contact the teacher for their program.
ERASMUS students can discuss the exam in English.

Università degli Studi
"G. d'Annunzio"

Chieti - Pescara

Aree

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