digital marketing, social media, la salle campus barcelona

Online Master of Science in Digital Marketing and Social Media Management

Lead the future of Marketing with AI-powered strategies and cutting-edge digital tools

Social Commerce

Description: 

The main objective of this course is to equip students with advanced knowledge and practical skills to design and implement effective performance marketing strategies in social media, with a specific focus on social ads and social commerce.

Students will learn how to develop innovative strategies that leverage social media platforms as business and sales channels, integrating advanced techniques and tools for the management and optimization of paid social campaigns. They will gain both strategic and practical experience with the leading social media advertising platforms, enabling them to optimize campaign performance and drive measurable conversions.

The course also fosters critical thinking regarding emerging trends in social commerce and the evolution of digital consumer behavior, preparing students to innovate and make data-driven decisions in this rapidly evolving ecosystem.

Type Subject
Primer - Obligatoria
Semester
Second
Course
1
Credits
5.00
Previous Knowledge: 

No specific prior knowledge is necessary

Objectives: 

  • Design and implement effective performance strategies on social networks, including social ads and social commerce.
  • Manage and optimize advertising campaigns using advanced techniques and tools to generate conversions.
  • Analyze and apply emerging trends in social commerce to innovate in a constantly evolving digital environment.

Contents: 

  • Social Commerce.
  • Social Ads Campaigns. Meta Business Accounts.
  • Social Media Budget. How much should you spend in Social Ads.
  • Creating Meta Ads campaign.
  • Segmentation in Social Ads. Show the Right Ad to the Right People.
  • Creating TikTok Ads Campaign.
  • Digital Marketing Channels and Social Media Strategies for Customer Success.

Methodology: 

ON-CAMPUS

 The teaching methodology used in the face-to-face modality is divided into four steps for each of the sessions that make up the subjects of the program:

  • Students must prepare before the session, the tasks recommended by the professor: reading an article, watching a video, solving a practical case or exercise, etc. In this way the student prepares the subject of the session and acquires a previous knowledge that will allow him/her to take better advantage of the session together with the professor and the rest of the classmates.
  • The professor dedicates the first part of the session to work on the concepts related to the topic of the day, encouraging debate and discussion among the students, taking advantage of the fact that they have prepared the previous tasks and start from a previous knowledge.
  • The professor dedicates the second part of the session to the analysis, debate and resolution of the case study or exercise proposed. Therefore, the concepts covered in the first part of the session are taken to a practical environment to strengthen the students’ learning. It is essential for students to work on the case study or exercise during the second part of the session, in order to contribute to the group.
  • It is recommended that students prepare at home the homework assignments requested by the professor after the session, which are intended to consolidate was covered and worked on in class.

ONLINE

The teaching methodology used in the online modality is based on a proprietary approach developed by La Salle URL called SDBL (Self Directed Based Learning). SDBL is an active methodology grounded in situational learning and self-directed learning. Through situational learning, students address real business problems and scenarios via challenges, enabling them to consolidate newly acquired knowledge. Through self-directed learning, students determine how to progress in their training based on their prior experience.

Each week, the LMS (Learning Management System) platform releases the content for a new topic. The weekly structure is as follows:

Synchronous kick-off session:

  • The professor provides an overview of the content and tasks that students will encounter throughout the week. The objective of this session is to help students identify, at a personal level, which aspects of the upcoming content may present greater difficulty for them individually.
  • The professor also addresses any questions related to the previous week’s topic.

Between synchronous sessions:

  • Students review the session content and complete the assigned tasks related to the week’s topic in order to consolidate knowledge and identify potential doubts or areas requiring clarification.

Synchronous check point session:

  • The professor addresses any questions students may have regarding the current week’s content.
  • The professor may introduce additional content or practical cases of interest.
  • The professor fosters debate and discussion among students about the week’s content to support comprehension and enhance learning outcomes.
  • The professor provides oral feedback on the deliverables submitted in the previous week’s feedback pool, highlighting common difficulties and errors. This session is intended exclusively for feedback purposes and does not involve grading. Feedback pools are designed to allow students to broadly validate their task resolutions with the mentor. They offer students the opportunity to test their responses and receive guidance before submitting the final deliverable. Based on the mentor’s feedback, students can refine and complete their work before submitting the final assessed version, benefiting from prior academic support.

Remainder of the week:

The objective is to complete the development of the week’s tasks based on the clarifications provided during the check point session, in order to successfully complete exercises, assignments, and/or deliverables. It should be noted that most of the time during this final part of the week should be devoted to completing tasks and deliverables rather than assimilating content, as content comprehension should have been largely achieved between the kick-off and check point sessions.

The LMS platform releases content progressively (week by week) to ensure that the entire group follows the same academic pathway. In other words, the sequential release of topics ensures that all students in the program work on the same subject matter simultaneously.

Evaluation: 

ON-CAMPUS

Highly significant evaluation activities:

  • Final exam [30%]
  • Revisable Documents (RD) [70%]

ONLINE

The assessment for this course consists of the following milestones and weightings:

  • Deliverable: Fundamentals of Artificial Intelligence in Marketing 20%
  • Deliverable: Intelligent Tools and Automation 20%
  • Deliverable: Regulation and Digital Transformation with AI 20%
  • Final Course Deliverable and Interview 40%

Evaluation Criteria: 

ON CAMPUS

  • Final exam: Test answers.
  • Revisable Documents. The FMT tutors responsible for evaluating the weekly deliverables will be based on:
  1. The quality of the content of the paper according to the requirements defined in each deliverable.
  2. The adjustment to the objectives of the course in progress.
  3. The coherence with the strategy marked in the FMT.
  4. The adaptability to the reality of the case/company chosen.
  5. The format of the document, the quality of the writing and style of the deliverable.
  6. Compliance with the terms of delivery. If they are delivered out of time they will be graded as "not delivered" and the grade will be "0".

ONLINE

The student will be able to:

  • Understand the fundamentals of Artificial Intelligence and its application in marketing.
  • Differentiate between machine learning, deep learning, LLMs, and generative AI, and apply them in real-world contexts.
  • Analyze the impact and trends of AI in businesses and digital environments.
  • Apply advanced prompting techniques and use AI agents strategically.
  • Use generative AI tools to create textual, visual, and audiovisual content.
  • Integrate AI into CRM, email marketing, automation, and chatbots.
  • Design AI-powered digital marketing campaigns and strategies.
  • Evaluate ethical, privacy, and regulatory aspects of AI use.
  • Incorporate AI into digital marketing transformation processes.
  • Develop comprehensive AI implementation projects in the marketing field.

Basic Bibliography: 

Boronat, D., & Pallarés, E. (2000). Vender más en Internet: La persuabilidad o el arte de convertir usuarios en clientes. Gestión 2000.

Golden, M. (2021). Marca personal: Cómo venderse a sí mismo en línea usando el mercadeo de medios sociales y el potencial oculto de los influencers, Instagram, publicidad en Facebook, YouTube, Twitter, blogs y más. Independently published.

Glenister, G. (2021). Influencer marketing strategy: How to create successful influencer marketing. Kogan Page.

Ries, E. (2011). El método Lean Startup. Deusto.

Sanchez Macott, J., & Rodriguez, I. (2020). Redes sociales para el profesional en redes de mercadeo: Descubra los 9 pasos como prospectar, vender, y construir su negocio virtualmente sin rechazos para que avance de rango en 90 días. Independently Published.

Smith, M. (2019). Marketing en Facebook. Guy Saloniki.

Tuten, T. L. (2017). Social media marketing (4th ed.). Fourth.

Turban, E., Strauss, J., & Lai, L. (2016). Social commerce: Marketing, technology and management (Springer Texts in Business and Economics). Springer.

E-Commerce Business through Social Media Marketing. (2020). GoldInk Books.

Additional Material: 

No additional materials are required

List of Professors
Erasmo Enrique Lopez Garcia