Pandas for Customer Support Analytics

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This skill equips Claude with specialized expertise in pandas for deep-dive customer support analytics, covering everything from ticket management to SLA tracking. It provides optimized patterns for DataFrame operations, time series analysis, and multi-level aggregations tailored specifically for support operations. Whether you need to build production-ready ETL pipelines from PostgreSQL or generate detailed agent performance reports, this skill ensures data accuracy and performance through proven implementation patterns and automated cleaning routines.

Características Principales

• 17 GitHub stars
• Comprehensive agent performance and CSAT metric calculation
• Advanced data cleaning and validation for support datasets
• Time series resampling for ticket volume trend reporting
• SLA compliance and response time analysis patterns
• PostgreSQL database integration via SQLAlchemy and ETL logic

Casos de Uso

• Calculating and reporting on weekly SLA compliance rates across support teams
• Analyzing customer satisfaction trends relative to response times and ticket priority
• Building automated data pipelines to transform raw ticket logs into performance dashboards