Support Data Analyzer

Overview

This Skill analyzes customer support feedback from multiple data sources (PIT/Roadblocks, Support Tickets, CSAT surveys, Ideas Forum) to identify pain points, categorize by theme, prioritize by customer impact, and generate comprehensive strategic reports.

Supports two analysis modes:

1. Comprehensive Analysis: Full categorization, prioritization, and strategic reporting (initial analysis)
2. Targeted Deep-Dive: Timeline analysis, theme investigation, resolution tracking (follow-up queries)

When to Apply

Use this skill for analysis of structured support data in the expected format:

Initial Analysis:

• User provides Excel/CSV files with PIT/Roadblocks, Support Tickets, or CSAT data columns
• User asks to "analyze support data", "prioritize customer feedback", or "generate impact report"
• User wants to understand top customer pain points from support channels
• User needs to scope analysis to a specific time period (e.g., "last 60 days")

Expected data format: Excel/CSV with columns like frustration level, MRR, ticket categories, CSAT scores

Follow-up Analysis:

• User asks about specific findings: "Has X issue dropped off?", "Show timeline for Y"
• User wants to investigate a theme mentioned in existing reports
• User needs to verify if an issue is resolved/declining/active
• User asks for examples or details about a specific category

In scope:

• Categorizing issues by product area and sub-category
• Calculating volume, frustration levels, and revenue impact
• Assigning priority levels (P0, P1, P2, P3) based on multiple factors
• Generating markdown reports with executive summaries and customer examples
• Timeline analysis and trend tracking for specific issues
• Deep-dive investigation of specific themes
• Comparing recent trends vs. historical baselines
• CSAT analysis and correlation

Out of scope:

• Sentiment analysis requiring NLP models not available in environment
• Predictive forecasting of future support volumes
• Root cause analysis requiring engineering investigation
• Data collection or export from live systems (only analyzes provided files)

Inputs

Required:

• Excel file(s) or CSV file(s) containing support data with these datasets:
  • PIT & Roadblocks: Strategic customer issues with frustration levels (1-5), MRR data, categories, submitted dates
  • Support Tickets: Support cases with product areas, roadblock types, created dates, ticket content
  • Optional but recommended: CSAT survey responses, Ideas Forum requests

Optional:

• Time range filter (e.g., "last 60 days", "Q4 2025")
• Historical baseline data for trend comparison
• Specific focus areas (e.g., "focus on association issues")

Expected data columns:

• PIT data: Submitted At Date, Category, Sub Category, Frustration Level (1-5), MRR (CS), MRR (Sales), Use Case Title, Use Case Body
• Support data: Created UTC Date, Support Product Area, Support roadblock, Ticket Name, Content
• CSAT data: Created At Date, Score (1-5), Text, Event Trigger

Outputs

Primary artifact:

• Comprehensive markdown report (*_Support_Analysis.md) containing:
  • Executive summary with top 3 critical areas
  • Prioritized themes by P0, P1, P2, P3 with metrics
  • Customer feedback examples (5 per major theme)
  • Strategic recommendations
  • Revenue at risk analysis
  • Frustration heatmap
  • Trend comparison (if historical data provided)

Optional artifacts:

• Customer feedback examples document with detailed quotes
• CSV extracts of filtered data for further analysis

Success criteria:

• Issues categorized into clear themes (e.g., "Object > Associations")
• Priority levels assigned with clear rationale
• At least 3-5 real customer examples per P0/P1 issue
• Actionable recommendations with impact estimates

Instructions for Claude

Step 1: Data Loading and Validation

1. Convert Excel files to CSV if needed using Python openpyxl
2. Read all data sources and display summary:
   • Total records per source
   • Date range coverage
   • Column validation (check for required fields)
3. If time filter specified, parse and apply date filtering
4. Report filtered counts and date ranges

Step 2: Theme Categorization

1. Combine issues by theme: {Product Area} > {Sub Category/Roadblock}
2. For each theme, calculate:
   • Total count (PIT + Support tickets)
   • PIT count and Support count separately
   • Average frustration (from PIT data where available)
   • High frustration count (frustration ≥ 4)
   • Total MRR impact (sum of MRR (CS) + MRR (Sales) from PIT data)
3. Sort themes by total count descending
4. Calculate percentage of total for each theme

Step 3: Priority Assignment

Apply the prioritization framework from resources/PRIORITIZATION_FRAMEWORK.md:

P0 (Critical):

• Volume >150 issues (60-day) OR >1,000 issues (historical) OR
• High frustration (avg ≥3.5) + volume >100 OR
• Revenue impact >$75K (60-day) OR >$4M (historical) OR
• Increasing trend (>20% vs baseline)

P1 (High):

• Volume 100-150 (60-day) OR 700-1,000 (historical) OR
• Frustration avg 3.0-3.5 with volume >50 OR
• Revenue impact $25K-$75K (60-day) OR $1.5M-$4M (historical)

P2 (Medium):

• Volume 30-100 (60-day) OR 200-700 (historical) OR
• High frustration (≥3.5) with lower volume OR
• Specialized use cases affecting specific segments

P3 (Lower):

• Volume <30 (60-day) OR <200 (historical)
• Administrative/low-frequency issues
• Lower frustration with low volume

Step 4: Customer Example Selection

For each P0 and P1 theme:

1. Select 5 diverse customer examples:
   • Mix of PIT issues (strategic feedback with frustration/MRR) and support tickets (tactical issues)
   • Prioritize high frustration examples (≥4) first
   • Include variety: different use cases, customer sizes, specific vs. general
2. For each example, extract:
   • Issue title/ticket name
   • Frustration level (if PIT data)
   • MRR (if available)
   • Full description/content (first 300 chars)
3. Add interpretation: "What this means:" explaining the customer impact

Step 5: CSAT Analysis (if data provided)

1. Calculate overall metrics:
   • Average score
   • Distribution by score (1-5)
   • Dissatisfaction rate (<4)
2. Identify top event triggers (what actions prompted survey)
3. Correlate CSAT triggers with support themes
4. Extract sample low-score feedback with categories

Step 6: Trend Analysis (if historical data provided)

1. Compare key metrics between time periods:
   • Average frustration change
   • Volume change by theme (percentage point shifts)
   • New themes emerging in top 10
2. Identify "getting worse" vs "improving" patterns
3. Flag NEW issues (not in historical top 10 but now top 5)

Step 7: Report Generation

Use the structure from resources/REPORT_TEMPLATE.md:

1. Executive Summary (must include):

   • Total issues analyzed
   • Top 3 critical areas with one-line description
   • Key changes vs historical (if applicable)
   • Overall frustration trend
   • Total revenue at risk

2. Detailed Analysis by Priority:

   • For each P0, P1, P2 theme:
     • Metrics table (volume, frustration, MRR, % of total)
     • Key pain points (bulleted list)
     • 5 customer examples with "What this means"
     • "Why P0/P1/P2" explanation

3. Strategic Recommendations:

   • Immediate actions (P0)
   • High priority (P1)
   • Cross-cutting initiatives
   • Impact estimates

4. Supporting Sections:

   • Revenue at risk table
   • Frustration heatmap
   • Comparison tables (if trend analysis)
   • Methodology appendix

Follow-up Analysis Workflow (Deep-Dive Queries)

When user asks specific questions about existing reports (e.g., "Has autosave issue been resolved?", "Show timeline for association complaints"), use this targeted analysis approach:

Step 1: Understand the Context

1. Read existing analysis report (if available) to understand:
   • Source file path from YAML frontmatter
   • Date range of existing analysis
   • What theme/issue the user is asking about
2. Optionally grep or read data.csv to see how issues were categorized

Step 2: Install Required Dependencies

pip3 install openpyxl pandas --break-system-packages --quiet

Important: Use --break-system-packages flag on macOS to bypass externally-managed-environment errors.

Step 3: Load Raw Data with Python

Use Python via Bash tool with heredoc to load and analyze Excel data:

python3 << 'EOF'
import openpyxl
import pandas as pd
from datetime import datetime, timedelta

# Load Excel file
wb = openpyxl.load_workbook('/full/path/to/feedback-data/file.xlsx')

# Process PIT and Roadblocks sheet
pit_sheet = wb['PIT and Roadblocks']
pit_data = []
pit_headers = [cell.value for cell in pit_sheet[1]]

for row in pit_sheet.iter_rows(min_row=2, values_only=True):
    pit_data.append(dict(zip(pit_headers, row)))

pit_df = pd.DataFrame(pit_data)

# Process Support Data sheet
support_sheet = wb['Support Data']
support_data = []
support_headers = [cell.value for cell in support_sheet[1]]

for row in support_sheet.iter_rows(min_row=2, values_only=True):
    support_data.append(dict(zip(support_headers, row)))

support_df = pd.DataFrame(support_data)

# Continue with analysis...
EOF

Step 4: Filter and Analyze

# Filter for specific keyword/theme
pit_filtered = pit_df[
    pit_df['Use Case Title'].fillna('').str.contains('keyword', case=False, na=False) |
    pit_df['Use Case Body'].fillna('').str.contains('keyword', case=False, na=False)
].copy()

support_filtered = support_df[
    support_df['Ticket Name'].fillna('').str.contains('keyword', case=False, na=False) |
    support_df['Content'].fillna('').str.contains('keyword', case=False, na=False)
].copy()

# Timeline analysis
support_filtered['Created UTC Date'] = pd.to_datetime(support_filtered['Created UTC Date'], errors='coerce')
support_filtered['Month'] = support_filtered['Created UTC Date'].dt.to_period('M')
monthly_counts = support_filtered.groupby('Month').size()

print("Monthly breakdown:")
for month, count in monthly_counts.items():
    print(f"  {month}: {count} issues")

# Trend analysis
print(f"\nMost recent: {support_filtered['Created UTC Date'].max()}")
print(f"Oldest in dataset: {support_filtered['Created UTC Date'].min()}")
print(f"Total issues: {len(pit_filtered) + len(support_filtered)}")

# Check recent activity (last 30 days)
recent_cutoff = datetime.now() - timedelta(days=30)
recent = support_filtered[support_filtered['Created UTC Date'] >= recent_cutoff]
print(f"Issues in last 30 days: {len(recent)}")

Step 5: Generate Insights

Based on the Python output, provide:

1. Total volume of issues matching the query
2. Timeline breakdown (monthly or weekly counts)
3. Trend analysis: % change from peak to current, identify drop-offs
4. Status determination: Resolved / Declining / Active / Increasing
5. Specific examples with dates, frustration scores, MRR values
6. Recommendation: Whether issue needs action or can be marked resolved

Step 6: Update Existing Report (if applicable)

If the deep-dive reveals the issue is resolved/declining, update the existing report.md:

• Add notes to relevant examples marking them as "(RESOLVED)" or "(DECLINING)"
• Add timeline analysis to report appendix
• Update Strategic Recommendations section
• Add update note to YAML frontmatter

Example: Autosave Timeline Analysis

User question: "Has the autosave issue been resolved?"

Steps taken:

1. Read report.md to get source file: feedback-data/2025-11-03_data-platform-code-orange.xlsx
2. Installed pandas/openpyxl
3. Loaded raw Excel, filtered for "autosave" mentions in both PIT and Support sheets
4. Grouped by month to see trend: April 2025 (9 issues) → Oct 2025 (2 issues)
5. Calculated 89% decline from peak
6. Determined status: RESOLVED
7. Updated report.md with timeline analysis and marked examples as resolved

Edge Cases and Tie-Breakers

• Missing MRR data: Use volume + frustration only for priority
• Ties in volume: Prioritize higher average frustration
• Low sample PIT data: Weight support ticket volume more heavily
• Conflicting signals (high volume, low frustration): Assign P1 and note in report
• New themes: Flag explicitly as "NEW to top X" in report

Quality Checks

Before finalizing report:

• Executive summary has specific numbers (not vague)
• Each P0/P1 has exactly 5 customer examples
• Examples include "What this means" interpretation
• Recommendations are actionable with timelines/impact
• All percentages sum correctly
• MRR totals are accurate
• Customer voice quotes are real (not synthesized)

Examples

Example Input

User: "Can you analyze this support data and prioritize the top issues? Focus on the last 60 days."

Files provided:
- Data Platform data - Code Orange.xlsx (contains sheets: Ideas Forum, CSAT, PIT and Roadblocks, Support Data)

Example Output

# Data Platform Support Data - Prioritized Analysis (Last 60 Days)

**Analysis Date:** October 31, 2025
**Time Period:** September 1, 2025 - October 31, 2025 (60 days)
**Total Issues Analyzed:** 2,201

## Executive Summary

Analysis of **2,201 customer issues from the last 60 days** reveals:

### Top 3 Critical Areas (P0):

1. **Object Associations** - 468 issues (27.5%), $141K MRR, 3.38 avg frustration
   - Association labels unavailable in reports (most common complaint)

2. **Property Edit** - 232 issues (13.6%), $96K MRR, 3.06 avg frustration
   - Cannot edit internal values after creation

3. **Property Values & History** - 223 issues (13.1%), $80K MRR
   - NEW to top 3 - sync delays and data integrity issues

**Key Changes vs. Historical:**
- Frustration increased 14% (2.71 → 3.09/5)
- Associations now 27.5% of issues (up from 24%)
- Property Values jumped to #3 (was #4)

**Total Revenue at Risk:** $684K MRR

---

## P0 - CRITICAL IMPACT

### 1. Object Associations (468 issues | $141K MRR)

**Metrics:**
- Volume: 21 PIT + 447 Support
- Average Frustration: 3.38/5
- Percentage of Total: 27.5%

**Key Pain Points:**
- Association labels unavailable in reports and lists
- Cannot filter or segment by association labels
- Workflow retrieval requiring custom code

**Customer Examples:**

#### Example 1: Association Label Limits
**Frustration:** 5/5 | **MRR:** $4,049
> "Client wants to be able to have up to 900+ nested relationships, but we cap our association labels at 50."

**What this means:** Customers with complex business models (B2B2B, franchises) hit hard limits on relationship types, forcing them to abandon tracking or use workarounds.

[... 4 more examples ...]

**Why P0:** Highest volume (27.5% of all issues), increasing trend, critical for CRM functionality.

---

## Strategic Recommendations

### Immediate Actions (P0)
1. **Associations Crisis Management**
   - Action: Enable association labels in reports/lists
   - Impact: 468 issues, $141K MRR
   - Timeline: 30 days

[... continued ...]

Testing Checklist

• Frontmatter present with name ≤64 chars, description ≤200 chars
• Description clearly states when to use ("analyze customer support data")
• YAML dependencies listed (python, openpyxl)
• At least one complete input/output example
• Resources files present and referenced
• No hardcoded customer data or secrets
• Priority framework is objective and consistent
• Example customer quotes are real (not generated)

Security & Privacy

Data Handling:

• Support data may contain customer PII (names, emails, company names)
• Do NOT redact customer examples in internal reports (needed for context)
• Do NOT include customer data in Skill files themselves (only process at runtime)
• If creating external reports, ask user if customer names should be redacted

File Storage:

• Converted CSV files stored in /tmp (ephemeral)
• Final reports saved to user-specified location (default: ~/Downloads)
• Do not commit support data to git repositories

MRR Data:

• Treat MRR values as confidential
• Include in internal reports but flag if report will be shared externally

Resources

See resources/ folder for:

• PRIORITIZATION_FRAMEWORK.md: Detailed criteria for assigning P0-P3
• REPORT_TEMPLATE.md: Complete markdown template with all sections
• EXAMPLES.md: Sample analyses with annotations
• ANALYSIS_SCRIPT.py: Python helper for data processing (optional)