Course objectives

This course aims to provide students with a comprehensive understanding of how data analytics and artificial intelligence (AI) can be applied to drive marketing success. Students will gain practical skills in analysing and interpreting marketing data, utilizing machine learning and AI techniques to optimize campaigns, and making data-driven decisions to improve marketing strategy. The course also emphasizes ethical considerations, the creation of impactful data visualizations, and the ability to effectively communicate insights to both technical and non-technical stakeholders. Through hands-on experience and real-world applications, students will be prepared to leverage advanced analytics tools in the digital era.

Learning outcomes

By successfully concluding this course the students should be able to:

• Apply Web Analytics: Use Google Analytics to measure website traffic, conversions, and user behaviour; design and evaluate KPIs.
• Evaluate Social Media Performance: Use SEMrush, Social Blade, and A/B testing to optimize digital campaigns.
• Demonstrate Data Collection Skills: Employ web scraping tools to gather marketing data and critically assess quality and ethics.
• Apply NLP Methods: Conduct sentiment analysis and topic modelling to analyse consumer-generated content.
• Integrate Analytics: Design integrated, data-driven marketing strategies that optimize customer engagement and conversions.
• Address Ethical Issues: Recognize and evaluate issues of privacy, bias, and regulation (GDPR, CCPA).
• Communicate Effectively: Present reports and deliver professional presentations tailored to technical and managerial audiences.

Course content

• Introduction to AI & Data Analytics in Marketing: Data-driven marketing & AI’s role; Structured vs. unstructured data; Industry case insights.
• Ethical Issues & AI in Marketing Analytics.
• Data crawling and pre-processing. Example: Crawling the web.
• Data summarisation and visualisation. Topic modelling of posts, reviews and comments
• Building classification and prediction models. Example: Sentiment analysis of reviews, comments and posts.
• User profiling via AI techniques. Example: Construction of user profiles based on posts and interactions.
• Web Analytics: Customer journey mapping, Linking analytics insights into strategy.