Graph Neural Networks Fundamentals and Marketing Management

This course delves into the world of Graph Neural Networks (GNNs), a powerful AI technique designed to analyze data represented as graphs. You'll explore the core concepts behind GNNs, their unique ability to capture relationships within data, and how they can be leveraged for tasks in Marketing, Pricing Strategy, and Sales Management. This course equips you to unlock valuable insights from customer interactions, optimize marketing campaigns based on network structures, and improve sales forecasting through advanced data analysis. 

• Define Graph Neural Networks and their ability to analyze data structured as graphs (e.g., customer networks, social media interactions).

• Understand the core architecture of GNNs, including message passing mechanisms for information exchange between nodes within a graph.

• Explore popular GNN techniques like Message Passing Neural Networks (MPNNs) for various marketing applications.

• Identify real-world applications of GNNs in Marketing, Pricing, and Sales (e.g., customer segmentation based on product recommendations, predicting customer churn based on network interactions, optimizing targeted advertising campaigns).

• Analyze the potential of GNNs for future advancements in AI-powered marketing strategies and customer relationship management.

1. Unveiling the Power of Graph Neural Networks

• Introduction to Graph Neural Networks: Understanding the core concepts and their ability to analyze relationships within data relevant to marketing tasks.

• Demystifying Graph Network Structures: Exploring the fundamental components of graphs (nodes, edges) and how GNNs leverage them for information processing.

• Deep dive into Message Passing Mechanisms: Understanding how GNNs enable information exchange and learning across interconnected nodes within a graph.

• Hands-on Exercises (Optional): Utilizing online tools or simplified code examples to explore basic GNN functionalities (e.g., node classification on marketing-related graphs).

• Case Studies: Examining how companies leverage GNNs for tasks like customer segmentation based on purchase history and social connections, or predicting product virality based on social network analysis.

2. GNNs for Marketing, Pricing & Sales Applications

• Exploring Applications in Marketing: Utilizing GNNs for tasks like customer segmentation based on product recommendations and network interactions, optimizing marketing campaigns based on customer influencer networks, and analyzing customer lifetime value (CLTV) considering network effects.

• Unveiling the Potential for Sales & Pricing: Exploring applications of GNNs in sales forecasting by incorporating customer network dynamics (e.g., referrals), lead scoring based on social network analysis, and developing dynamic pricing strategies considering competitor offerings within a market network.

• The Future of GNNs in Marketing & Sales: Discussing emerging applications of GNNs in recommender systems, personalized marketing automation, and network-based sales force optimization.

• Course Wrap-up: Addressing limitations of GNNs, potential biases in network data, and best practices for responsible AI implementation in marketing, pricing, and sales.

• Strong understanding of linear algebra, calculus, and probability theory

• Proficiency in programming with Python and deep learning frameworks (e.g., TensorFlow, PyTorch)

• Familiarity with basic machine learning concepts and techniques

• Knowledge of graph theory and network analysis is beneficial but not required