Marketing Intelligence

This Curricular Unit allows for the use of various active and student-centred Teaching Methodologies, in order to develop analytical and
critical thinking, decision-making, problem-solving skills and practical knowledge of the topics. The following strategies will therefore be
used:
– Problem-Based Learning: starting from real problems in marketing and international business, ask students for solutions using data
analysis, Artificial Intelligence and CRM data. This strategy is also useful in relation to the use of reporting and productivity tools in
marketing, to solve problems by encouraging students to create reports on digital marketing indicators.
– Case Studies: choose and discuss case studies of companies that have developed artificial intelligence solutions applied to marketing and
sales strategies, promoting critical analysis, group debate and teamwork.
– Collaborative Projects: create practical group projects, with a hands-on approach, that allow students to create marketing strategies using
Data Mining, Web Mining and Text Analytics tools.
– Competency-based learning: promoting the acquisition of practical skills through simulation and analytical CRM platforms, exploiting data
to personalise campaigns.
– Flipped Classroom: Providing theoretical materials before classes, allowing classroom time to be used for debates, analysis and practical
activities related to digital marketing and Business Intelligence.
– Project-based learning (PjBL): proposing a project that can include all the aspects of the syllabus, as well as learning to work with welldefined
goals, creating a digital marketing strategy that includes data and where they use the tools and techniques discussed in the subject.

The Marketing Intelligence subject aims to:
LO1 (Learning Objective) – Enable students to use decision support systems in marketing with Big Data
LO2 – Understand the ethical application of Artificial Intelligence (AI) in marketing and sales strategies
LO3 – Use the analytical component in Customer Relationship Management (CRM) systems to personalise campaigns
LO4 — Apply Data Mining, Web Mining and Text Analytics to identify patterns
LO5 – Develop reports and analyse digital marketing indicators
LO6 – Analyse Business Intelligence (BI) trends in marketing
On completing this CU, students should be able to:
C1 (competence) – to make decisions based on data
C2 – a critical and ethical understanding of AI in Marketing
C3 – customer relationship management via CRM
C4 – extracting patterns via Data Mining
C5 – reporting and analysis skills
C6 – analysing emerging trends in BI to improve marketing strategy results

Marketing Decision Support Systems: data-driven Marketing and Big Data
2. Artificial Intelligence in Marketing decision-making:
2.1 Fundamentals of Artificial Intelligence
2.2 Ethics and Regulations in Artificial Intelligence
2.3 Marketing and Sales Strategies Using Artificial Intelligence
2.4 Artificial Intelligence in Social Media
2.5 Negotiation Techniques with Artificial Intelligence
2.6 Artificial Intelligence and future trends in Marketing and Sales
3. Analytical CRM
4. Data Mining, Web Mining and Text Analytics applied to Marketing
5. Reporting Systems for Marketing
6. Emerging Trends and Impacts of Business Intelligence in Marketing
7. Indicators in Digital Marketing
7.1 Monitoring and analysing results
7.2 Reporting results: from objectives to data
7.3 Productivity tools

NAO