Graph Neural Networks Fundamentals for Customer Experience (CX) Professionals 

This course introduces you to the fascinating world of Graph Neural Networks (GNNs), a powerful AI technique designed to analyze data structured as graphs. Graphs are networks of interconnected nodes, where nodes represent entities (e.g., customers, products) and connections represent relationships between them. GNNs unlock the potential of this data structure for enhancing customer experience (CX) initiatives within your organization. 

• Explain the concept of graph data and its relevance to various CX applications.

• Understand the fundamental architecture of Graph Neural Networks (GNNs) and how they process information on graphs.

• Identify different GNN variations and their potential applications in tasks like customer segmentation, recommendation systems, and network analysis for improved CX.

• Explore how GNNs can leverage relationships between customers, products, and interactions to gain deeper insights for personalized experiences.

• Evaluate the limitations and considerations for responsible implementation of GNNs in real-world CX applications.

1. Unveiling the Power of Graph Data and GNNs:

• Introduction to Graph Data: Understanding the concept of graphs, nodes, edges, and their importance in representing customer-related information.

• Demystifying Graph Neural Networks: Exploring the core architecture of GNNs and how they learn from graph-structured data.

• Case Study 1: Utilizing GNNs for customer segmentation within a social media platform, enabling targeted marketing campaigns and personalized recommendations.

• Hands-on Session: Working with a user-friendly platform to visualize graphs and explore basic GNN functionalities.

• Pre-Requisite Math Refresher (Optional): Reviewing foundational matrix operations relevant to GNNs (provided upon request).

2. Exploring GNN Applications for Enhanced CX:

• Beyond Customer Segmentation: Unveiling the potential of GNNs for tasks like product recommendation based on customer relationships and purchase history.

• Network Analysis with GNNs: Exploring how GNNs can analyze customer networks to identify influencers, predict churn, and optimize customer service strategies.

• Case Study 2: Utilizing GNNs to analyze customer support ticket networks, identifying common issues and improving resolution times.

• Guest Speaker Session: Inviting a data scientist or AI engineer who has implemented GNNs for CX applications to share their experience.

• Group Discussion: Brainstorming potential applications of GNNs for specific CX challenges within your department, considering the network structure of your data.

3. From Understanding to Implementation: Responsible GNNs in CX:

• Data Considerations for GNNs: Emphasizing the importance of high-quality and well-structured graph data for effective GNN training in CX applications.

• Popular GNN Architectures: Introducing different GNN variations (e.g., Graph Convolutional Networks) and their suitability for specific CX tasks.

• Interactive Workshop: Experimenting with a pre-built GNN model on a sample customer dataset relevant to your chosen CX challenge.

• Project Planning & Data Exploration: Developing a project plan outlining the chosen GNN application for CX, identifying relevant data sources, and outlining initial data exploration steps.

• Course Wrap-up & Project Presentations: Teams present their project plans, outlining the chosen GNN application, data considerations, and potential impact on the customer experience.

• Resource Sharing: Discussing best practices and ongoing learning opportunities for staying up-to-date with GNN advancements in the CX field.

• 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