From Tagging to Intelligence: Visualizing Customer Service Insights with Interactive Dashboards

*Interactive Data Visualization with Plotly Dash | Customer Service Data Analysis | Sunburst Chart Tutorial* Build a powerful, interactive dashboard to analyze customer service data tagged with LLMs! This tutorial uses Python, Plotly, and Dash to create a dynamic Sunburst chart that lets you explore hierarchical data. Learn how to: Visualize primary and secondary tags (e.g., "Product Quality" issues, "Documentation Gaps"). Implement drill-down functionality for in-depth analysis. Filter data dynamically based on user selections. Use Plotly and Dash callbacks for interactivity. Create a user-friendly dashboard with pagination. Structure and scale your Dash application. Chapters: 00:23 Introduction and Project Context We discuss tagging customer service data using LLMs (building on a previous video and LinkedIn article) and setting the stage for visualization. We'll reuse data from a prior script. 01:28 Data Setup and Visualization Initialization Importing libraries (Plotly, Dash), connecting to the data, grouping data, and configuring the Sunburst plot for hierarchical tags. 03:41 Sunburst Plot Functionality and Interactivity See how to drill down into categories, dynamically filter data, and understand nested counts within the Sunburst chart. 05:58 Dashboard Interactivity and Customization Learn about pagination, dynamic filtering using callbacks, and how `selected_data` and `active_cell` work. 07:52 Code Structure and Scalability We'll discuss the app layout (`dbc.Container`), linking callbacks, and options for expanding the dashboard (multi-page apps, styling). 10:49 Conclusion and Next Steps A summary of the dashboard's features and an invitation for further tutorial requests. Keywords: Plotly, Dash, Data Visualization, Sunburst Chart, Customer Service, Data Analysis, Python, LLM, Interactive Dashboard, Callbacks, Data Exploration, Business Intelligence, Analytics, Tutorial