Data, Analytics and AI for Marketing Strategy

This course provides an advanced understanding of how data, analytics, and artificial intelligence can be leveraged to design and optimize marketing strategies. It focuses on the transformation of marketing decision-making in data-rich environments, moving from intuition-driven approaches to evidence-based and algorithm-supported strategies.

Students will learn how to structure marketing decision problems, integrate and evaluate data sources, and apply analytical models to key strategic areas such as customer segmentation, customer lifetime value (CLV), acquisition and retention, marketing mix allocation, pricing, and revenue optimization. The course also introduces modern machine learning approaches and AI-driven decision systems, including reinforcement learning and generative AI applications in marketing.

The course combines conceptual foundations with quantitative tools and practical applications. Through case studies, tutorials, and hands-on exercises, students will develop the ability to translate analytical insights into actionable marketing decisions. Special emphasis is placed on causal inference, economic decision criteria, and the strategic implications of AI-enabled marketing.

Upon successful completion of this course, students:

• are able to structure and formalize marketing decision problems in data-rich environments, integrating strategic objectives, constraints, and performance metrics.
• understand the role of data, analytics, and AI in marketing decision-making, including the distinction between predictive and causal approaches.
• are able to apply key analytical frameworks and models (e.g., segmentation, CLV, marketing mix models, pricing and demand models) to derive optimal marketing strategies.
• are able to evaluate marketing actions based on economic criteria such as profit, ROI, and customer lifetime value.
• have acquired the ability to design data-driven customer and market strategies, including targeting, acquisition, retention, cross-selling, and pricing decisions.
• understand the opportunities and limitations of machine learning and AI in marketing, including issues of model evaluation, interpretability, and governance.
• are able to translate analytical results into actionable managerial recommendations and communicate them effectively.
• have developed hands-on experience with analytical tools and methods used in modern marketing analytics and AI-supported decision-making.