Module BUSI4Q415: Digital Marketing, Marketing Analytics and AI

Department: Management and Marketing

BUSI4Q415: Digital Marketing, Marketing Analytics and AI

Prerequisites

• None

Corequisites

• None

Excluded Combination of Modules

• None

Aims

• This module prepares students to draw upon the latest research findings in the digital marketing field and to examine the use of AI and analytics for data-driven marketing decision-making. It integrates technical analytics foundations with strategic AI applications across diverse marketing contexts. Students develop capabilities to extract insights from marketing data, apply predictive analytics techniques, critically evaluate AI technologies, and make evidence-based marketing decisions. The module addresses technical applications, strategic implementation, and ethical considerations in AI-powered marketing.

Content

• Foundations:
• Introduction to digital marketing, AI and analytics in marketing
• Marketing analytics foundations: metrics, KPIs, and measurement frameworks
• Data-driven marketing strategy
• Analytics Techniques:
• Web analytics and digital campaign measurement
• Customer segmentation and targeting analytics
• Social media and sentiment analysis
• Predictive analytics techniques for marketing
• AI Applications:
• Generative AI for marketing communications and content
• AI applications in e-commerce, branding, international marketing, and sales
• Personalisation engines and recommendation systems
• Pricing optimisation using predictive analytics
• Advanced Topics:
• Churn prediction and customer retention strategies
• Customer lifetime value modelling
• Emerging AI technologies in marketing
• Ethics and Governance
• Privacy, data protection, and regulatory compliance
• Algorithmic bias and fairness in AI systems
• Ethical implications of AI in marketing practice
• Future trends in AI and analytics for marketing.

Learning Outcomes

• By the end of the module students will have a critical understanding of:
• Marketing analytics frameworks, predictive modelling techniques, and measurement approaches for evaluating marketing performance
• The strategic application of AI technologies including machine learning, natural language processing, and generative AI across diverse marketing contexts
• Technical foundations of predictive analytics including segmentation, churn prediction, personalisation, and pricing optimisation
• Ethical implications, privacy regulations, and responsible practices in AI-powered marketing

• By the end of the module students will be able to:
• Select and apply appropriate predictive analytics techniques to address specific marketing challenges.
• Interpret marketing data, analytics outputs, and model results to generate actionable business insights
• Critically evaluate AI technology opportunities and limitations across different marketing applications
• Design data-driven marketing strategies integrating predictive analytics and AI technologies
• Assess ethical implications, potential biases, and privacy concerns in AI-driven marketing systems
• Communicate data-driven insights and recommendations effectively to technical and non-technical stakeholders.

• Effective written communication skills.
• Planning, organising and time management skills.
• Problem solving and analytical skills.
• The ability to use initiative.
• Advanced skills in the interpretation of data and analytical outputs.
• Advanced computer literacy skills.
• Critical thinking and evaluation of technology applications.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• The module will be taught through a combination of lectures, seminars, group work and discussion, supported by guided reading and hands-on analytics exercises.
• The summative assessment of the module is an individual data-driven marketing analysis report requiring students to analyse marketing data using predictive analytics techniques, propose AI-powered solutions, develop evidence-based recommendations, and critically evaluate ethical implications and implementation challenges.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

Seminar exercises provide hands-on analytics practice with immediate feedback. Seminar discussions address reading comprehension, case study analysis and ethical debates. Students receive verbal formative feedback in seminars. All students have the opportunity to seek one-to-one guidance from the Module Leader if required.

■ Students who do not attend monitored activities shown under Teaching Methods and Learning Hours, or who fail to complete the summative or formative assessment(s) specified above, may be subject to the Academic Progress procedures defined in the University's General Regulation V, and may be required to leave the University.