Support Data Analyzer

Analyzing support tickets involves categorizing issues into themes and assigning P0-P3 priorities based on volume, frustration, and revenue impact. Upload Excel or CSV files containing your support data; the Skill aggregates themes by product area, scores frustration levels, calculates revenue-at-risk metrics, and generates a markdown report with customer quotes and trend analysis.

Yes. The Skill accepts both Excel and CSV datasets containing PIT/Roadblocks, Support Tickets, and optional CSAT data. You can specify optional time windows—such as the last 60 days or all-time—to focus the analysis on relevant periods before generating prioritized themes and business impact metrics.

The Skill integrates multi-source support data, identifies recurring themes, embeds real customer quotes, scores frustration sentiment, and calculates revenue-at-risk. It outputs a comprehensive markdown report with P0-P3 priority themes, trend analysis, and actionable recommendations in a format ready for executive review.

Yes. The Skill processes optional CSAT data alongside support tickets and PIT/Roadblocks, applies revenue-at-risk calculations to quantify business impact, and scores frustration across issues. This multi-dimensional approach ensures priorities reflect both customer sentiment and financial exposure.

The Skill requires Python 3.8 or later, openpyxl 3.0.0 or later for Excel parsing, and pandas 1.3.0 or later for data aggregation. Input data must be in Excel (.xlsx) or CSV format containing structured support ticket, PIT/Roadblock, or CSAT records.

Yes. The Skill aggregates issues by product area, applies theme detection across multiple data sources, scores frustration levels, and surfaces trend patterns. Output includes top themes ranked by priority, trend insights, and revenue impact—all segmented and queryable in the generated markdown report.