Applying Data Analytics in Marketing

About this course

This course introduces students to marketing analytics as a data-driven approach to solving real-world marketing problems. It covers four key areas: causal analysis (identifying cause-and-effect in marketing interventions), predictive modeling and AI (forecasting customer behaviors using machine learning), social media analysis (extracting insights from online consumer interactions through text and network analysis), and consumer demand and preference analysis (estimating preferences, demand, and customer lifetime value). Students will gain hands-on experience using Python to analyze diverse data sources, apply advanced analytics techniques, and generate actionable insights to support strategic marketing decisions.

What you will learn

• Design experiments and apply quasi-experimental methods to identify and measure the impact of marketing interventions.
• Use machine learning and AI techniques to forecast customer behaviors and marketing outcomes.
• Analyze consumer sentiment and user-generated content through computational text and network analysis.
• Estimate customer preferences and demand patterns, and calculate customer lifetime value to inform marketing strategy.