Applying Data Analytics in Marketing

Overview

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.

Syllabus

Module 1: Course Introduction and Causal Analysis

In the first module, we will discuss analytics in marketing and delve into causal analysis, a crucial tool for analytics. We will begin with a comprehensive overview of why analytics is crucial for marketers, including the various types of data, the process of applying analytics in marketing, and the different types of analytics. We will then delve deeper into causal analysis.

Module 2: Artificial Intelligence, Prediction, and Machine Learning

In this module, we explore how Artificial Intelligence (AI) and Machine Learning (ML) are transforming marketing practices—from predicting customer behavior to enabling hyper-personalization at scale. We’ll examine the fundamentals of prediction, how to build machine learning models, and how advances in tools like Large Language Models (LLMs) are unlocking new capabilities in areas such as segmentation, market research, and customer retention. You’ll also learn about the tradeoffs and ethics of AI deployment, including bias, transparency, and privacy considerations.

Module 3: User, Firm, and AI-Generated Content Analysis

In this module, we explore how to make sense of the vast amounts of unstructured content that users and companies generate online—from product reviews and social media posts to Q&A threads and firm-generated promotions. You’ll learn how to pre-process text, extract insights using tools like sentiment analysis and topic modeling, and perform social network analysis to understand influence and engagement. We also examine how different types of content—user-generated, firm-generated, and AI-generated—shape brand perceptions and drive consumer behavior, while also discussing ethical challenges such as misinformation, bias, and fake reviews.

Module 4: Customer Preferences and Lifetime Value Analysis

This module introduces data-driven tools for understanding consumer preferences and forecasting demand. You'll learn how to segment customers, assess their long-term value, and apply choice modeling techniques like conjoint analysis to evaluate which product features matter most. We also cover customer lifetime value (CLV), how to calculate it, and how it guides investment in acquisition and retention. The module highlights the growing importance of incrementality in churn prediction and campaign evaluation.