SYLLABUS

Course overview

Course description

The course aims to provide students with an operational understanding of brand management and digital reputation management. It starts from the fundamentals of brand identity, positioning, brand value and brand management, and then examines the role of digital touchpoints, content, social media, search reputation, reviews, communities, creators and AI-based recommendation systems in shaping online reputation.

The course integrates the critical use of generative AI tools to support professional activities such as analysis, ideation, planning and monitoring, always with human verification and attention to responsible use.

Learning objectives and expected outcomes

Knowledge and understanding

• Understand the main concepts of brand identity, brand image, brand equity, brand management and digital reputation.
• Recognise the role of owned, paid and earned channels in reputation building.
• Understand the basic logic of recommendation systems and AI platforms in brand visibility.

Applying knowledge and understanding

• Conduct a brand audit and a digital reputation audit.
• Design an online content strategy consistent with brand positioning.
• Use AI tools to support naming, verbal identity, visual identity briefing, editorial planning and competitor analysis.

Making judgements

• Assess the consistency between a brand's declared identity and its digital perception.
• Interpret data, content and reputation signals.
• Distinguish professional and verified AI use from generic, unchecked or ethically problematic AI use.

Communication skills

• Present a digital reputation strategy in English using professional terminology.
• Justify choices concerning branding, channels, content, metrics and the use of AI.

Learning skills

• Develop conceptual and operational tools to keep up to date with digital platforms, AI tools and brand reputation management practices.

Course programme

The 40-hour course is organised into ten weekly 4-hour lessons. The topics are presented in progressive order. Each lesson includes theoretical frameworks, cases and an applied component.

AI across the course: each topic includes an applied section on how AI tools can support professional work, including brand analysis, naming, verbal identity, visual identity briefing, editorial planning, content strategy, social listening, crisis response and visibility in recommendation systems.

Teaching methodology

The course combines lectures, theoretical models, case studies, class discussions, guided exercises, group work and project presentations. Each weekly lesson is divided into a theoretical section and an applied section with guided exercises or AI-assisted workshops. Students progressively apply brand management and digital reputation concepts to real or simulated cases, using generative AI critically and with human verification.

Laboratory activities

Approximately 12 hours of the course are dedicated to practical activities and workshops. Activities include:

• brand audit;
• competitor and reputation analysis;
• AI-assisted naming and verbal identity exercises;
• visual identity briefing;
• content strategy and editorial calendar development;
• search reputation audit;
• social listening and review analysis.

Study materials and reference texts

Attending and non-attending students must study all slides, articles, handouts, case studies and additional materials made available during the course on the course website or learning platform. No additional textbook is mandatory.

There are no differences between attending and non-attending students in terms of required course content.

Recommended reference texts

• Aaker, D. A., Building Strong Brands, Pocket Books, 2010.
• Burgess, C. and Burgess, M., The New Marketing: How to Win in the Digital Age, Sage, 2020.
• Heinze, A., Fletcher, G., Cruz, A., & Fenton, A. (2024), Digital and Social Media Marketing: A Results-Driven Approach (3rd ed.), Routledge.
• Krowinska, A., Backhaus, J., Becker, L., & Bosser, A. (2023), Digital Content Marketing: Creating Value in Practice, Routledge.
• Additional articles and professional reports on digital reputation, social media, AI search, recommendation systems and generative AI will be indicated during the course.

Prerequisites

Proficiency in English is required. Basic knowledge of marketing and communication is useful. No specific prerequisite courses are required.

Assessment and grading

Written test

The written test is compulsory for all students, both attending and non-attending. It lasts 30 minutes and includes multiple-choice questions, short-answer questions and open-ended questions on the main course topics. The test assesses knowledge and understanding of concepts, mastery of specialist terminology, ability to apply models, and critical judgement in the use of AI tools.

Final grade

The final grade is expressed out of 30 and takes into account the written test, the project work and, for non-attending students, the oral exam. The exam is passed with a minimum grade of 18/30.

Rating descriptors

Final dissertation

Students wishing to propose a final dissertation in the field of this course must demonstrate a specific interest in brand management, digital reputation, content strategy, social media, AI and recommendation systems. The proposal must be submitted in writing and include the topic, objectives, research question or professional problem, proposed methodology and initial bibliography/webography. Thesis allocation will follow the regulations of the degree programme.