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Building a Support Analytics Pipeline: From Tickets to Insights

H

Hoshang Mehta

Building a Support Analytics Pipeline: From Tickets to Insights

Support teams can't answer basic questions: What are the top issues this week? Which customers have the most tickets? What's causing escalations? Analytics are manual and delayed. Teams react instead of preventing issues. The result: support teams operate blind, problems escalate before they're detected, and leadership lacks visibility.

The problem: A support manager wants to know what's causing the spike in tickets this week. They manually export ticket data, pivot in Excel, look for patterns. After 2 hours, they find that 40% of tickets are about a specific feature. By then, the issue has been affecting customers for 3 days. If they had known on day one, they could have prevented 200 frustrated customers.

The solution: An automated analytics pipeline that aggregates ticket data across sources, identifies patterns and anomalies, surfaces insights using LLMs, and generates executive summaries—all automatically. This post walks through building a repeatable architecture that transforms ticket data into actionable intelligence.

The Problem with Manual Support Analytics

Manual analytics break down at scale. Here's what support teams face:

The Data Gathering Problem

Analytics require data from multiple sources:

  • Support System: Ticket volume, resolution times, satisfaction scores
  • CRM: Customer tier, account value, renewal dates
  • Product Analytics: Feature usage, error rates, user behavior
  • Billing: Payment status, revenue impact

The math: If gathering data from 4 sources takes 30 minutes and you do this weekly, that's 26 hours per year just collecting data—before any analysis.

The Pattern Detection Problem

Teams can't identify patterns in real-time:

  • 20 customers report the same bug, but each ticket is treated individually
  • A feature causes issues, but no one notices until it's a crisis
  • Seasonal patterns exist, but they're discovered after the fact

The impact: Problems escalate before they're detected. Early pattern detection could prevent 80% of escalations.

The Insight Generation Problem

Raw data doesn't tell a story:

  • Ticket volume increased 30% this week—but why?
  • Resolution time increased—which issues are causing it?
  • Satisfaction scores dropped—what changed?

The cost: Support managers spend hours analyzing data instead of acting on insights.

The Reporting Problem

Executive summaries are manual:

  • Weekly reports take 4 hours to compile
  • Data is stale by the time it's shared
  • Different stakeholders need different views
  • Reports don't answer "so what?"

The missed opportunity: Automated insights could surface issues daily, enabling proactive response instead of reactive firefighting.

A Repeatable Analytics Architecture

The solution is a pipeline architecture that aggregates ticket data, detects patterns, generates insights, and produces summaries—all automatically.

graph TD A[Support System] --> E[Data Aggregation Layer] B[CRM System] --> E C[Product Analytics] --> E D[Billing System] --> E E --> F[Ticket Analytics View] F --> G[Pattern Detection] F --> H[Anomaly Detection] G --> I[Insight Generation] H --> I I --> J[Executive Summary] I --> K[Alert Generation] L[Weekly Aggregation] --> M[Trend Analysis] M --> I style E fill:#e1f5ff style G fill:#fff4e1 style I fill:#e8f5e9

Architecture Components

1. Data Aggregation Layer

  • Connects to source systems (support, CRM, product, billing)
  • Normalizes ticket data across systems
  • Joins customer context and product data
  • Handles data quality issues

2. Ticket Analytics View

  • Materialized view aggregating ticket metrics
  • Pre-computed aggregations (daily, weekly, monthly)
  • Calculated fields (resolution time, satisfaction trends)
  • Time-series data for trend analysis

3. Pattern Detection Engine

  • Identifies recurring issues
  • Detects topic clusters
  • Finds correlation patterns
  • Surfaces emerging trends

4. Anomaly Detection System

  • Monitors metric deviations
  • Alerts on spikes and drops
  • Identifies outliers
  • Tracks metric health

5. Insight Generation with LLMs

  • Analyzes patterns and anomalies
  • Generates natural language insights
  • Explains "why" behind metrics
  • Provides actionable recommendations

6. Executive Summary Automation

  • Aggregates key metrics
  • Highlights top insights
  • Generates weekly/monthly reports
  • Formats for different audiences

Aggregating Ticket Data Across Sources

The first stage is aggregating ticket data from support systems and enriching it with customer and product context.

Building the Ticket Analytics View

Create a comprehensive view that joins ticket data with customer and product context:

CREATE MATERIALIZED VIEW ticket_analytics_comprehensive AS
SELECT 
    -- Ticket Info
    t.ticket_id,
    t.subject,
    t.description,
    t.status,
    t.priority,
    t.created_at,
    t.resolved_at,
    t.first_response_at,
    t.satisfaction_score,
    
    -- Time Metrics
    EXTRACT(EPOCH FROM (t.resolved_at - t.created_at)) / 3600 as resolution_time_hours,
    EXTRACT(EPOCH FROM (t.first_response_at - t.created_at)) / 60 as first_response_time_minutes,
    EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - t.created_at)) / 3600 as age_hours,
    
    -- Customer Context
    cc.customer_id,
    cc.customer_name,
    cc.customer_tier,
    cc.mrr,
    cc.plan_name,
    cc.renewal_date,
    
    -- Product Context
    pc.product_version,
    pc.feature_name,
    pc.error_type,
    
    -- Intent Classification
    tic.intent_category,
    tic.intent_confidence,
    
    -- Assignment Info
    ta.assigned_team,
    ta.assigned_agent_id,
    
    -- Date Dimensions
    DATE(t.created_at) as ticket_date,
    DATE_PART('week', t.created_at) as ticket_week,
    DATE_PART('month', t.created_at) as ticket_month,
    DATE_PART('quarter', t.created_at) as ticket_quarter,
    DATE_PART('year', t.created_at) as ticket_year,
    
    -- Calculated Fields
    CASE 
        WHEN t.status = 'resolved' AND EXTRACT(EPOCH FROM (t.resolved_at - t.created_at)) / 3600 < 4 THEN 'within_sla'
        WHEN t.status = 'resolved' THEN 'over_sla'
        WHEN EXTRACT(EPOCH FROM (CURRENT_TIMESTAMP - t.created_at)) / 3600 > 4 THEN 'at_risk'
        ELSE 'on_track'
    END as sla_status,
    
    CASE 
        WHEN t.satisfaction_score >= 4.5 THEN 'high'
        WHEN t.satisfaction_score >= 3.5 THEN 'medium'
        WHEN t.satisfaction_score < 3.5 THEN 'low'
        ELSE 'no_rating'
    END as satisfaction_category
    
FROM tickets t
LEFT JOIN customer_context_comprehensive cc ON t.customer_email = cc.email
LEFT JOIN product_context pc ON t.ticket_id = pc.ticket_id
LEFT JOIN ticket_intent_classification tic ON t.ticket_id = tic.ticket_id
LEFT JOIN ticket_assignments ta ON t.ticket_id = ta.ticket_id;

Weekly Aggregation

Pre-aggregate data for faster analytics:

CREATE MATERIALIZED VIEW ticket_analytics_weekly AS
SELECT 
    ticket_week,
    ticket_year,
    DATE_TRUNC('week', ticket_date) as week_start_date,
    
    -- Volume Metrics
    COUNT(*) as total_tickets,
    COUNT(*) FILTER (WHERE status = 'open') as open_tickets,
    COUNT(*) FILTER (WHERE status = 'resolved') as resolved_tickets,
    COUNT(*) FILTER (WHERE status = 'closed') as closed_tickets,
    
    -- Time Metrics
    AVG(resolution_time_hours) as avg_resolution_time_hours,
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY resolution_time_hours) as median_resolution_time_hours,
    AVG(first_response_time_minutes) as avg_first_response_minutes,
    
    -- Satisfaction Metrics
    AVG(satisfaction_score) as avg_satisfaction_score,
    COUNT(*) FILTER (WHERE satisfaction_category = 'high') as high_satisfaction_count,
    COUNT(*) FILTER (WHERE satisfaction_category = 'low') as low_satisfaction_count,
    
    -- SLA Metrics
    COUNT(*) FILTER (WHERE sla_status = 'within_sla') as within_sla_count,
    COUNT(*) FILTER (WHERE sla_status = 'over_sla') as over_sla_count,
    COUNT(*) FILTER (WHERE sla_status = 'at_risk') as at_risk_count,
    
    -- Intent Breakdown
    COUNT(*) FILTER (WHERE intent_category = 'billing') as billing_tickets,
    COUNT(*) FILTER (WHERE intent_category = 'technical') as technical_tickets,
    COUNT(*) FILTER (WHERE intent_category = 'account_access') as account_access_tickets,
    COUNT(*) FILTER (WHERE intent_category = 'feature_request') as feature_request_tickets,
    
    -- Customer Tier Breakdown
    COUNT(*) FILTER (WHERE customer_tier = 'enterprise') as enterprise_tickets,
    COUNT(*) FILTER (WHERE customer_tier = 'mid_market') as mid_market_tickets,
    COUNT(*) FILTER (WHERE customer_tier = 'self_serve') as self_serve_tickets,
    
    -- Team Performance
    COUNT(DISTINCT assigned_team) as teams_involved,
    COUNT(DISTINCT assigned_agent_id) as agents_involved,
    
    -- Trends (vs previous week)
    COUNT(*) - LAG(COUNT(*)) OVER (ORDER BY ticket_week, ticket_year) as ticket_volume_change,
    AVG(resolution_time_hours) - LAG(AVG(resolution_time_hours)) OVER (ORDER BY ticket_week, ticket_year) as resolution_time_change,
    AVG(satisfaction_score) - LAG(AVG(satisfaction_score)) OVER (ORDER BY ticket_week, ticket_year) as satisfaction_change
    
FROM ticket_analytics_comprehensive
GROUP BY ticket_week, ticket_year, DATE_TRUNC('week', ticket_date);

Pattern Detection and Anomaly Identification

Identify patterns in ticket data to surface recurring issues and emerging trends.

Pattern Detection: Recurring Issues

Find tickets with similar content:

-- Identify recurring issues
CREATE MATERIALIZED VIEW recurring_issues AS
SELECT 
    intent_category,
    LOWER(REGEXP_REPLACE(subject, '[^a-z0-9\s]', '', 'g')) as normalized_subject,
    COUNT(*) as occurrence_count,
    COUNT(DISTINCT customer_id) as affected_customers,
    AVG(resolution_time_hours) as avg_resolution_time,
    MIN(created_at) as first_occurrence,
    MAX(created_at) as last_occurrence,
    ARRAY_AGG(DISTINCT customer_tier) as customer_tiers_affected,
    ARRAY_AGG(DISTINCT product_version) as product_versions_affected
FROM ticket_analytics_comprehensive
WHERE status = 'resolved'
  AND created_at > CURRENT_TIMESTAMP - INTERVAL '30 days'
GROUP BY intent_category, LOWER(REGEXP_REPLACE(subject, '[^a-z0-9\s]', '', 'g'))
HAVING COUNT(*) >= 3  -- At least 3 occurrences
ORDER BY occurrence_count DESC;

Anomaly Detection: Metric Deviations

Detect when metrics deviate from normal:

-- Anomaly detection for ticket volume
CREATE MATERIALIZED VIEW ticket_volume_anomalies AS
WITH weekly_stats AS (
    SELECT 
        week_start_date,
        total_tickets,
        AVG(total_tickets) OVER (ORDER BY week_start_date ROWS BETWEEN 3 PRECEDING AND 1 PRECEDING) as avg_previous_weeks,
        STDDEV(total_tickets) OVER (ORDER BY week_start_date ROWS BETWEEN 3 PRECEDING AND 1 PRECEDING) as stddev_previous_weeks
    FROM ticket_analytics_weekly
)
SELECT 
    week_start_date,
    total_tickets,
    avg_previous_weeks,
    CASE 
        WHEN total_tickets > avg_previous_weeks + (2 * stddev_previous_weeks) THEN 'spike'
        WHEN total_tickets < avg_previous_weeks - (2 * stddev_previous_weeks) THEN 'drop'
        ELSE 'normal'
    END as anomaly_type,
    ABS(total_tickets - avg_previous_weeks) / NULLIF(avg_previous_weeks, 0) as deviation_percentage
FROM weekly_stats
WHERE total_tickets > avg_previous_weeks + (2 * stddev_previous_weeks)
   OR total_tickets < avg_previous_weeks - (2 * stddev_previous_weeks);

Topic Clustering

Group similar tickets to identify themes:

-- Topic clustering (simplified - use proper NLP in production)
CREATE MATERIALIZED VIEW ticket_topics AS
SELECT 
    intent_category,
    CASE 
        WHEN LOWER(subject || ' ' || description) LIKE '%api%' OR LOWER(subject || ' ' || description) LIKE '%integration%' THEN 'api_integration'
        WHEN LOWER(subject || ' ' || description) LIKE '%payment%' OR LOWER(subject || ' ' || description) LIKE '%billing%' THEN 'billing_payment'
        WHEN LOWER(subject || ' ' || description) LIKE '%login%' OR LOWER(subject || ' ' || description) LIKE '%access%' THEN 'account_access'
        WHEN LOWER(subject || ' ' || description) LIKE '%error%' OR LOWER(subject || ' ' || description) LIKE '%bug%' THEN 'technical_errors'
        ELSE 'other'
    END as topic_cluster,
    COUNT(*) as ticket_count,
    AVG(resolution_time_hours) as avg_resolution_time,
    AVG(satisfaction_score) as avg_satisfaction
FROM ticket_analytics_comprehensive
WHERE created_at > CURRENT_TIMESTAMP - INTERVAL '7 days'
GROUP BY intent_category, topic_cluster
ORDER BY ticket_count DESC;

Insight Generation with LLMs

Use LLMs to analyze patterns and anomalies, generating natural language insights that explain what's happening and why.

Insight Generation Process

def generate_insights(weekly_data: dict, patterns: list, anomalies: list) -> list:
    insights = []
    
    # Analyze ticket volume
    if weekly_data['ticket_volume_change'] > 0.2:  # 20% increase
        insight = analyze_ticket_volume_increase(weekly_data, patterns)
        insights.append(insight)
    
    # Analyze recurring issues
    for pattern in patterns:
        if pattern['occurrence_count'] >= 5:
            insight = analyze_recurring_issue(pattern)
            insights.append(insight)
    
    # Analyze anomalies
    for anomaly in anomalies:
        if anomaly['deviation_percentage'] > 0.3:  # 30% deviation
            insight = analyze_anomaly(anomaly)
            insights.append(insight)
    
    return insights

def analyze_ticket_volume_increase(weekly_data: dict, patterns: list) -> dict:
    prompt = f"""Analyze this support ticket data and provide insights:

    Week: {weekly_data['week_start_date']}
    Total Tickets: {weekly_data['total_tickets']}
    Change from Previous Week: {weekly_data['ticket_volume_change']} tickets ({weekly_data['ticket_volume_change'] / (weekly_data['total_tickets'] - weekly_data['ticket_volume_change']) * 100:.1f}% increase)
    
    Top Issues:
    {chr(10).join([f"- {p['intent_category']}: {p['occurrence_count']} occurrences" for p in patterns[:5]])}
    
    Provide:
    1. What's causing the increase?
    2. Which issues are driving it?
    3. What actions should be taken?
    
    Return JSON with 'summary', 'root_causes', and 'recommendations'."""
    
    response = openai.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}],
        response_format={"type": "json_object"}
    )
    
    result = json.loads(response.choices[0].message.content)
    return {
        "type": "volume_increase",
        "severity": "high" if weekly_data['ticket_volume_change'] > 50 else "medium",
        "summary": result["summary"],
        "root_causes": result["root_causes"],
        "recommendations": result["recommendations"]
    }

def analyze_recurring_issue(pattern: dict) -> dict:
    prompt = f"""Analyze this recurring support issue:

    Issue: {pattern['normalized_subject']}
    Occurrences: {pattern['occurrence_count']} times
    Affected Customers: {pattern['affected_customers']}
    Average Resolution Time: {pattern['avg_resolution_time']:.1f} hours
    Customer Tiers: {', '.join(pattern['customer_tiers_affected'])}
    Product Versions: {', '.join(pattern['product_versions_affected'])}
    
    Provide:
    1. Why is this recurring?
    2. What's the root cause?
    3. How can we prevent it?
    
    Return JSON with 'analysis', 'root_cause', and 'prevention_strategy'."""
    
    response = openai.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}],
        response_format={"type": "json_object"}
    )
    
    result = json.loads(response.choices[0].message.content)
    return {
        "type": "recurring_issue",
        "severity": "high" if pattern['occurrence_count'] >= 10 else "medium",
        "issue": pattern['normalized_subject'],
        "analysis": result["analysis"],
        "root_cause": result["root_cause"],
        "prevention_strategy": result["prevention_strategy"]
    }

Insight Storage

-- Insights table
CREATE TABLE support_insights (
    insight_id UUID PRIMARY KEY,
    insight_type VARCHAR(50), -- 'volume_increase', 'recurring_issue', 'anomaly', etc.
    severity VARCHAR(20), -- 'high', 'medium', 'low'
    summary TEXT,
    details JSONB,
    recommendations JSONB,
    generated_at TIMESTAMP,
    week_start_date DATE
);

-- Store generated insights
INSERT INTO support_insights (insight_type, severity, summary, details, recommendations, generated_at, week_start_date)
VALUES (
    'volume_increase',
    'high',
    'Ticket volume increased 35% this week, driven by API integration issues',
    '{"ticket_count": 450, "previous_week": 333, "increase": 117}',
    '["Investigate API integration errors", "Review recent API changes", "Add API troubleshooting docs"]',
    CURRENT_TIMESTAMP,
    DATE_TRUNC('week', CURRENT_DATE)
);

Executive Summary Generation

Generate concise executive summaries that distill analytics into key takeaways for leadership.

Summary Generation Process

def generate_executive_summary(weekly_data: dict, insights: list) -> str:
    prompt = f"""Generate an executive summary for support analytics:

    Week: {weekly_data['week_start_date']}
    
    Key Metrics:
    - Total Tickets: {weekly_data['total_tickets']}
    - Resolved: {weekly_data['resolved_tickets']}
    - Average Resolution Time: {weekly_data['avg_resolution_time_hours']:.1f} hours
    - Average Satisfaction: {weekly_data['avg_satisfaction_score']:.1f}/5.0
    - SLA Compliance: {weekly_data['within_sla_count'] / weekly_data['total_tickets'] * 100:.1f}%
    
    Top Insights:
    {chr(10).join([f"- {insight['summary']}" for insight in insights[:5]])}
    
    Generate a 3-paragraph executive summary:
    1. Overall performance and trends
    2. Key issues and patterns
    3. Recommendations for leadership
    
    Keep it concise and actionable."""
    
    response = openai.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}],
        temperature=0.3
    )
    
    return response.choices[0].message.content

Weekly Summary View

-- Executive summary view
CREATE MATERIALIZED VIEW executive_summary_weekly AS
SELECT 
    week_start_date,
    
    -- Key Metrics
    total_tickets,
    resolved_tickets,
    ROUND(avg_resolution_time_hours, 1) as avg_resolution_time_hours,
    ROUND(avg_satisfaction_score, 1) as avg_satisfaction_score,
    ROUND(within_sla_count::float / NULLIF(total_tickets, 0) * 100, 1) as sla_compliance_percent,
    
    -- Trends
    ticket_volume_change,
    resolution_time_change,
    satisfaction_change,
    
    -- Top Issues
    (
        SELECT jsonb_agg(
            jsonb_build_object(
                'category', intent_category,
                'count', ticket_count
            )
        )
        FROM (
            SELECT intent_category, COUNT(*) as ticket_count
            FROM ticket_analytics_comprehensive
            WHERE DATE_TRUNC('week', ticket_date) = tw.week_start_date
            GROUP BY intent_category
            ORDER BY ticket_count DESC
            LIMIT 5
        ) top_issues
    ) as top_issues,
    
    -- Generated Summary
    (
        SELECT summary
        FROM support_insights
        WHERE week_start_date = tw.week_start_date
          AND insight_type = 'executive_summary'
        ORDER BY generated_at DESC
        LIMIT 1
    ) as executive_summary
    
FROM ticket_analytics_weekly tw
ORDER BY week_start_date DESC;

MCP Tool for Analytics

{
  "name": "get_support_analytics",
  "description": "Retrieves support analytics including ticket volume, resolution times, satisfaction scores, and insights. Use when support managers need to understand support performance, identify trends, or generate reports.",
  "inputSchema": {
    "type": "object",
    "properties": {
      "time_period": {
        "type": "string",
        "enum": ["week", "month", "quarter", "year"],
        "description": "Time period for analytics. Defaults to 'week'.",
        "default": "week"
      },
      "include_insights": {
        "type": "boolean",
        "description": "Include AI-generated insights. Defaults to true.",
        "default": true
      },
      "include_executive_summary": {
        "type": "boolean",
        "description": "Include executive summary. Defaults to false.",
        "default": false
      }
    }
  },
  "query": "SELECT * FROM executive_summary_weekly WHERE week_start_date >= CURRENT_DATE - INTERVAL '1 week' ORDER BY week_start_date DESC LIMIT 1",
  "policies": ["authenticated", "role:support OR role:manager"]
}

Schema Overview

The analytics pipeline uses a hierarchical schema that traces from raw tickets to aggregated analytics and insights.

erDiagram TICKETS ||--o{ TICKET_ANALYTICS_COMPREHENSIVE : enriches CUSTOMER_CONTEXT ||--o{ TICKET_ANALYTICS_COMPREHENSIVE : joins TICKET_ANALYTICS_COMPREHENSIVE ||--o{ TICKET_ANALYTICS_WEEKLY : aggregates TICKET_ANALYTICS_WEEKLY ||--o{ RECURRING_ISSUES : detects_patterns TICKET_ANALYTICS_WEEKLY ||--o{ TICKET_VOLUME_ANOMALIES : detects_anomalies RECURRING_ISSUES ||--o{ SUPPORT_INSIGHTS : generates TICKET_VOLUME_ANOMALIES ||--o{ SUPPORT_INSIGHTS : generates SUPPORT_INSIGHTS ||--o{ EXECUTIVE_SUMMARY_WEEKLY : summarizes TICKETS { string ticket_id PK string subject timestamp created_at timestamp resolved_at decimal satisfaction_score } TICKET_ANALYTICS_COMPREHENSIVE { string ticket_id PK decimal resolution_time_hours string intent_category string customer_tier date ticket_date } TICKET_ANALYTICS_WEEKLY { date week_start_date PK integer total_tickets decimal avg_resolution_time_hours decimal avg_satisfaction_score } SUPPORT_INSIGHTS { uuid insight_id PK string insight_type string severity text summary jsonb recommendations }

Key relationships:

  • tickets → ticket_analytics_comprehensive (enriched with context)
  • ticket_analytics_comprehensive → ticket_analytics_weekly (aggregated)
  • ticket_analytics_weekly → pattern/anomaly detection
  • Patterns/anomalies → support_insights (generated)
  • support_insights → executive_summary_weekly (summarized)

Traceability: Every insight can be traced back to source tickets, aggregation logic, and generation process.

Closing Thoughts

Building a support analytics pipeline transforms ticket data from a burden into an asset. The key is treating analytics as an automated process—not a manual task.

Key takeaways:

  1. Aggregation enables speed: Pre-compute aggregations. Support managers need fast answers, not complex queries at query time.

  2. Pattern detection surfaces issues: Identify recurring problems early. Early detection prevents escalations and improves customer experience.

  3. Anomaly detection enables proactive response: Monitor metrics for deviations. Alert on spikes and drops before they become crises.

  4. LLMs generate actionable insights: Raw data doesn't tell a story. Use LLMs to explain what's happening and why, with recommendations.

  5. Automation scales insights: Generate summaries automatically. Don't wait for manual analysis—surface insights daily.

The architecture described here is repeatable. Start with basic aggregations, add pattern detection, then implement insight generation. Each stage builds on the previous one.

Most importantly: this isn't about replacing human analysis—it's about augmenting it. Support managers still need judgment and context. The analytics pipeline provides automated insights, freeing managers to act on those insights instead of spending time finding them.

This architecture pattern is implemented in Pylar, but the concepts apply to any system analyzing support data. The key is the aggregation → pattern detection → insight generation → summarization pipeline.