Curriculum

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Descriptions

MLDS 490: Marketing Models

Description

This course explores the models and algorithms commonly used in marketing, providing a structured approach to applying analytics in real-world marketing scenarios. We begin with a foundational framework that guides the analytical process, ensuring a cohesive structure throughout the course.

Module 1: Customer Segmentation
We examine how supervised and unsupervised learning methods help identify meaningful customer segments, enabling more effective targeting and engagement strategies.

Module 2: Customer Valuation
These sessions focus on measuring customer value through models such as customer profitability analysis, customer lifetime value (CLV), and customer equity. We explore predictive techniques, including churn models, Markov chain models, and CLV optimization to enhance decision-making.

Module 3: Brand Touchpoints & Personalization
We analyze how brands create and deliver impactful customer interactions, with a focus on personalization, targeting, and customization. Additionally, we explore strategic prompt engineering in marketing communication to optimize AI-driven interactions.

Module 4: Marketing Metrics & Evaluation
The final week covers key performance metrics and best practices for evaluating marketing effectiveness, including true and quasi-experimental designs for causal inference. I will introduce propensity score matching as time permits.

By the end of the course, students will gain a practical understanding of how to leverage marketing models to drive strategic decisions and improve customer relationships. Students will be provided with a data set from a real company each week and will be asked to apply the methods discussed during the lectures to the data and propose marketing strategies.