Trend Analysis

Trend Analysis

Overview

This skill provides comprehensive trend analysis for ServiceNow ITSM data. It enables you to:

• Analyze incident volume trends over time (daily, weekly, monthly, quarterly)
• Identify category and priority distribution changes
• Detect seasonal and cyclical patterns
• Recognize predictive indicators for capacity planning
• Generate actionable insights for resource allocation

When to use: For capacity planning, resource forecasting, identifying systemic issues, preparing trend reports, or detecting anomalies requiring investigation.

Prerequisites

• Roles: report_admin, itil, or analytics_admin
• Access: Read access to incident, problem, and related tables
• Knowledge: Basic statistics and understanding of organizational patterns
• Data: Minimum 90 days of historical data recommended; 12 months ideal for seasonal analysis

Procedure

Step 1: Establish Baseline Metrics

Query historical data to establish baselines for comparison.

Using MCP (Claude Code/Desktop):

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: sys_created_onONLast 90 days@javascript:gs.daysAgoStart(90)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,number,sys_created_on,priority,category,state,assignment_group
  limit: 5000

Using REST API:

GET /api/now/table/incident?sysparm_query=sys_created_onONLast 90 days@javascript:gs.daysAgoStart(90)@javascript:gs.daysAgoEnd(0)&sysparm_fields=sys_id,number,sys_created_on,priority,category,state,assignment_group&sysparm_limit=5000

Step 2: Daily Volume Trend Analysis

Track incident creation patterns by day.

Query for Daily Breakdown (Using GlideAggregate):

Tool: SN-Execute-Background-Script
Parameters:
  script: |
    var ga = new GlideAggregate('incident');
    ga.addQuery('sys_created_on', '>=', gs.daysAgo(30));
    ga.addAggregate('COUNT');
    ga.groupBy('sys_created_on');
    ga.orderBy('sys_created_on');
    ga.query();

    var dailyCounts = [];
    while (ga.next()) {
      var dateStr = ga.getValue('sys_created_on').substring(0, 10);
      dailyCounts.push({
        date: dateStr,
        count: parseInt(ga.getAggregate('COUNT'))
      });
    }
    gs.info('Daily Volume: ' + JSON.stringify(dailyCounts));
  description: Aggregate daily incident counts

Manual Calculation (Without GlideAggregate): Query incidents for specific date ranges:

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: sys_created_onON2024-01-15@javascript:gs.dateGenerate('2024-01-15','00:00:00')@javascript:gs.dateGenerate('2024-01-15','23:59:59')
  fields: sys_id
  limit: 500

Step 3: Weekly Volume Comparison

Compare week-over-week to identify trends.

Current Week:

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: sys_created_onONLast 7 days@javascript:gs.daysAgoStart(7)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,priority,category
  limit: 1000

Previous Week:

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: sys_created_onONLast 7 days@javascript:gs.daysAgoStart(14)@javascript:gs.daysAgoEnd(7)
  fields: sys_id,priority,category
  limit: 1000

Week-over-Week Analysis:

Step 4: Category Distribution Analysis

Track how incident categories change over time.

Query Category Distribution:

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: sys_created_onONLast 30 days@javascript:gs.daysAgoStart(30)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,category
  limit: 2000

Process results to create distribution:

Category Distribution (Last 30 Days):
| Category | Count | % of Total | Prev Month % | Change |
|----------|-------|------------|--------------|--------|
| Software | 245 | 32% | 28% | +4% |
| Network | 189 | 25% | 30% | -5% |
| Hardware | 152 | 20% | 18% | +2% |
| Email | 98 | 13% | 12% | +1% |
| Security | 76 | 10% | 12% | -2% |

Step 5: Priority Trend Analysis

Monitor priority distribution changes.

Query Priority Distribution Over Time:

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: sys_created_onONLast 30 days@javascript:gs.daysAgoStart(30)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,priority,sys_created_on
  limit: 2000

Priority Shift Indicators:

Step 6: Day-of-Week Patterns

Identify which days see highest incident volume.

Query Incidents by Day:

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: sys_created_onONLast 90 days@javascript:gs.daysAgoStart(90)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,sys_created_on
  limit: 5000

Process to extract day of week:

Day of Week Analysis (90-Day Average):
| Day | Avg Volume | vs Overall Avg | Staffing Recommendation |
|-----|------------|----------------|------------------------|
| Monday | 32 | +23% | Full staff + OT |
| Tuesday | 28 | +8% | Full staff |
| Wednesday | 26 | 0% | Normal |
| Thursday | 25 | -4% | Normal |
| Friday | 24 | -8% | Normal, early close OK |
| Saturday | 8 | -69% | Weekend skeleton |
| Sunday | 7 | -73% | Weekend skeleton |

Step 7: Hour-of-Day Patterns

Identify peak hours for staffing optimization.

Query with Time Breakdown:

Tool: SN-Execute-Background-Script
Parameters:
  script: |
    var ga = new GlideAggregate('incident');
    ga.addQuery('sys_created_on', '>=', gs.daysAgo(30));
    ga.addAggregate('COUNT');
    ga.query();

    // Note: For hourly analysis, use sys_created_on timestamp
    // and extract hour component in processing
    var total = ga.getAggregate('COUNT');
    gs.info('Total incidents: ' + total);
  description: Count incidents for hourly analysis

Typical Hour Distribution:

Hourly Volume Pattern (30-Day Analysis):
Peak Hours: 8am-11am (35% of daily volume)
Secondary Peak: 1pm-3pm (25% of daily volume)
Low Period: 6pm-6am (15% of daily volume)

Staffing Recommendation:
- Core Hours (8am-5pm): 80% of staff
- Extended Hours (5pm-8pm): 15% of staff
- Night/Weekend: 5% of staff

Step 8: Seasonal and Cyclical Patterns

Identify recurring patterns based on calendar events.

Query Monthly Volumes (Year-over-Year): For each month, query:

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: sys_created_onON2024-01-01@javascript:gs.dateGenerate('2024-01-01','00:00:00')@javascript:gs.dateGenerate('2024-01-31','23:59:59')
  fields: sys_id
  limit: 2000

Common Seasonal Patterns:

Step 9: Predictive Indicators

Identify leading indicators for future incident volume.

Change Volume Correlation: High change volume often precedes incident spikes.

Tool: SN-Query-Table
Parameters:
  table_name: change_request
  query: sys_created_onONLast 30 days@javascript:gs.daysAgoStart(30)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,type,cmdb_ci
  limit: 500

Problem Creation as Leading Indicator:

Tool: SN-Query-Table
Parameters:
  table_name: problem
  query: sys_created_onONLast 30 days@javascript:gs.daysAgoStart(30)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,category,first_reported_by_task
  limit: 100

Leading Indicators to Monitor:

Step 10: Anomaly Detection

Identify unusual patterns requiring investigation.

Calculate Statistical Boundaries:

Rolling 30-Day Average: 26 incidents/day
Standard Deviation: 6 incidents
Upper Threshold (2σ): 38 incidents/day
Lower Threshold (2σ): 14 incidents/day

Any day outside 14-38 range = Anomaly requiring investigation

Query Recent Anomalies: If daily count exceeds threshold, investigate:

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: sys_created_onONToday@javascript:gs.beginningOfToday()@javascript:gs.endOfToday()^ORDERBYcategory
  fields: sys_id,category,short_description,cmdb_ci
  limit: 100

Tool Usage

MCP Tools Reference

REST API Reference

Key Fields for Trend Analysis

Best Practices

• Consistent Time Zones: Ensure all queries use the same timezone
• Statistical Significance: Use 90+ days for reliable patterns
• Seasonal Adjustment: Compare same periods year-over-year
• Correlation Analysis: Link incidents to changes, problems, and CIs
• Visualization: Use charts for pattern recognition
• Automate Alerts: Set up alerts for statistical anomalies
• Document Exceptions: Note one-time events that skew data

Troubleshooting

"Data shows unexpected spikes"

Cause: Major incident, system outage, or bulk ticket creation Solution: Filter out known major incidents; document the event as an exception

"Year-over-year comparison not matching"

Cause: Business changes, mergers, or organizational restructuring Solution: Normalize for headcount or scope changes; note structural differences

"Seasonal patterns not appearing"

Cause: Insufficient historical data (less than 12 months) Solution: Wait for more data; use industry benchmarks as proxy

"Day-of-week patterns inconsistent"

Cause: Holidays, company events, or remote work changes Solution: Exclude holiday weeks; separate analysis for remote vs. office periods

Examples

Example 1: Weekly Trend Report

Objective: Generate weekly trend summary for management

Queries:

// This Week
Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: sys_created_onONLast 7 days@javascript:gs.daysAgoStart(7)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,priority,category,sys_created_on
  limit: 500

// Previous Week
Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: sys_created_onONLast 7 days@javascript:gs.daysAgoStart(14)@javascript:gs.daysAgoEnd(7)
  fields: sys_id,priority,category,sys_created_on
  limit: 500

// Same Week Last Year (if available)
Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: sys_created_onON2023-01-15@javascript:gs.dateGenerate('2023-01-15','00:00:00')@javascript:gs.dateGenerate('2023-01-21','23:59:59')
  fields: sys_id,priority,category
  limit: 500

Output Report:

=== WEEKLY TREND REPORT ===
Week: Jan 15-21, 2024

VOLUME COMPARISON:
| Period | Volume | Change |
|--------|--------|--------|
| This Week | 152 | - |
| Last Week | 138 | +10.1% |
| Same Week Last Year | 165 | -7.9% |
| 4-Week Average | 142 | +7.0% |

TREND ASSESSMENT: Moderate Increase (within normal range)

DAILY BREAKDOWN:
| Day | Volume | vs Avg |
|-----|--------|--------|
| Mon | 35 | +17% |
| Tue | 28 | -7% |
| Wed | 25 | -17% |
| Thu | 22 | -27% |
| Fri | 24 | -20% |
| Sat | 10 | -67% |
| Sun | 8 | -73% |

Monday spike correlates with 3 changes deployed over weekend.

CATEGORY SHIFTS:
| Category | This Week | Last Week | Change |
|----------|-----------|-----------|--------|
| Software | 52 (34%) | 42 (30%) | +4% |
| Network | 38 (25%) | 42 (30%) | -5% |
| Hardware | 30 (20%) | 28 (20%) | 0% |
| Email | 18 (12%) | 14 (10%) | +2% |
| Security | 14 (9%) | 12 (9%) | 0% |

SOFTWARE TREND: +4% increase requires investigation
- 15 incidents related to new CRM deployment
- Recommend knowledge article for common CRM issues

FORECAST (Next Week):
Based on 8-week trend and scheduled changes:
Expected Volume: 145-160 incidents
Confidence: Medium (upcoming holiday may reduce volume)

Example 2: Capacity Planning Report

Objective: Forecast staffing needs for next quarter

Analysis Process:

1. Historical Volume (Last 12 Months):

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: sys_created_onONLast 365 days@javascript:gs.daysAgoStart(365)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,sys_created_on,priority
  limit: 10000

2. Calculate Monthly Averages:

Monthly Volume Summary:
Jan: 720 | Feb: 680 | Mar: 750
Apr: 710 | May: 695 | Jun: 620
Jul: 580 | Aug: 590 | Sep: 780
Oct: 740 | Nov: 700 | Dec: 550

Average: 676/month
Peak: September (780) - Back to school/work
Low: December (550) - Holiday period

3. Forecast Next Quarter:

=== Q2 2024 CAPACITY FORECAST ===

VOLUME PROJECTION:
| Month | Projected | Confidence | Factors |
|-------|-----------|------------|---------|
| April | 720-750 | High | Fiscal year end |
| May | 700-730 | High | Normal |
| June | 630-660 | Medium | Summer start |

STAFFING REQUIREMENTS:
Current Capacity: 750 incidents/month (5 FTEs)
Incidents per FTE: 150/month

| Month | Volume | FTEs Needed | Current | Gap |
|-------|--------|-------------|---------|-----|
| April | 735 | 5.0 | 5 | 0 |
| May | 715 | 4.8 | 5 | +0.2 |
| June | 645 | 4.3 | 5 | +0.7 |

RECOMMENDATIONS:
1. April: Full staffing required, no vacations
2. May: Normal operations, 1 vacation slot OK
3. June: Good time for training, 2 vacation slots OK

RISK FACTORS:
- ERP upgrade scheduled for April 15: +20% volume risk
- New office opening in May: +50 incidents/month expected

ADJUSTED FORECAST WITH RISKS:
| Month | Base | Risk Adjusted | FTEs Needed |
|-------|------|---------------|-------------|
| April | 735 | 880 | 6.0 |
| May | 715 | 815 | 5.5 |
| June | 645 | 695 | 4.7 |

Recommendation: Hire 1 contractor for April-May to cover ERP risk

Example 3: Root Cause Trend Investigation

Objective: Investigate why Software incidents increased 15% this month

Step 1: Identify the increase

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: category=Software^sys_created_onONLast 30 days@javascript:gs.daysAgoStart(30)@javascript:gs.daysAgoEnd(0)
  fields: sys_id,short_description,cmdb_ci,subcategory,assignment_group,sys_created_on
  limit: 500

Step 2: Drill down by subcategory

Software Incident Breakdown:
| Subcategory | This Month | Last Month | Change |
|-------------|------------|------------|--------|
| CRM | 85 | 45 | +89% |
| Email | 42 | 48 | -12% |
| ERP | 38 | 35 | +9% |
| Office Suite | 35 | 40 | -12% |
| Other | 45 | 42 | +7% |

CRM subcategory driving entire increase!

Step 3: Analyze CRM incidents

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: category=software^cmdb_ci.nameLIKECRM^sys_created_on>=javascript:gs.beginningOfThisMonth()
  fields: sys_id,short_description,sys_created_on
  limit: 100

Step 4: Identify patterns

=== CRM INCIDENT TREND ANALYSIS ===

TIMELINE:
- Month start (Day 1-10): 15 incidents (normal)
- Mid-month (Day 11-20): 45 incidents (+200%)
- Month end (Day 21-30): 25 incidents (+67%)

CORRELATION:
- CRM v4.2 deployed on Day 10
- 70% of incidents reference "login" or "sync" issues
- All incidents assigned to Application Support

TOP ISSUES:
1. "Unable to sync contacts" - 32 incidents
2. "Login timeout errors" - 28 incidents
3. "Report generation failing" - 15 incidents
4. Other - 10 incidents

ROOT CAUSE:
CRM v4.2 deployment introduced authentication bug
affecting mobile sync and session timeout.

RECOMMENDATIONS:
1. IMMEDIATE: Deploy hotfix for authentication issue
2. SHORT-TERM: Create knowledge article for workaround
3. LONG-TERM: Improve deployment testing for auth changes

PREDICTED IMPACT:
After hotfix deployment, expect:
- Week 1: 50% reduction in CRM incidents
- Week 2: Return to baseline (45/month)

Related Skills

• reporting/sla-analysis - SLA performance tracking
• reporting/executive-dashboard - Executive KPI generation
• itsm/incident-lifecycle - Full incident management
• itsm/problem-analysis - Root cause investigation

References

• ServiceNow Performance Analytics
• ServiceNow Reporting
• ITIL Continual Service Improvement
• Statistical Process Control