AIMS AND CONTENT

LEARNING OUTCOMES

The objective of the course is to provide students with skills related to content and data management in social media, addressing communication, digital marketing, data collection, integration and statistical analysis (social media analytics).

TEACHING METHODS

blended

SYLLABUS/CONTENT

Elements for setting up quantitative and qualitative research
(sampling, questionnaire, digital ethnography, smattering of
descriptive statistics)

Basic metrics:
Reach
Impressions
Engagement
Followers/Fans:
Click-Through Rate (CTR)
Examples and classroom work

Strategic KPIs:
Awareness Relevant metrics: reach, impressions, followers,
Mentions.
Engagement Relevant metrics: engagement rate,
comments, shares.
Conversion. Relevant metrics: CTR, conversion rate, leads
Generated.

Data cleansing and preparation: removing duplicate, incorrect, or
irrelevant, ensuring a clean and analysis-ready dataset,
saving valuable time and resources.
Key Information Extraction: Extract specific information from
Raw data, such as hashtags, mentions, keywords, sentiment, data
facilitating subsequent analysis.

Sentiment classification: using Natural Language algorithms
Processing (NLP) to analyze the language used by users,
classify comments and interactions as positive, negative, or
Neutral.
Emotion analysis: beyond simple classification, identifying
specific emotions such as joy, anger, sadness, surprise, allowing
to understand the deepest reactions of the public.
Sentiment monitoring over time: tracking the evolution of the
sentiment over time, identify any changes in perceptions
of users, allowing the company to intervene promptly.
 

Trend analysis: monitor social media conversations,
identifying emerging hashtags, topics and themes, anticipating
trends and ride the wave of the moment.
Strategic insights: Beyond just data, providing strategic insights
on public behaviour, preferences, needs and
expectations, allowing communication to be adapted in a targeted manner.
Predictive analytics: Use historical data to predict trends
helping to plan marketing strategies and optimize
campaigns in advance.
Examples and classroom work

 Audience Segmentation
Demographic segmentation: demographics such as age, gender, location
to create specific audiences.
Behavioral segmentation: analyze user interactions,
interests, preferences and shopping habits to create
behavior-based segments.
Psychographic segmentation: using language analysis and others
Techniques

RECOMMENDED READING/BIBLIOGRAPHY

Any bibliography will be indicated during the lessons

TEACHERS AND EXAM BOARD

Exam Board

LAURA SCUDIERI (President)

SEBASTIANO BENASSO

LUCA SABATINI (President Substitute)

LESSONS

Class schedule

The timetable for this course is available here: EasyAcademy