name: support-operations
description: Expert support operations guidance for customer service excellence. Use when designing ticket management systems, creating SLA policies, building support tier structures (L1/L2/L3), optimizing knowledge bases, defining severity levels and escalation procedures, implementing support metrics (CSAT, FRT, TTR, FCR), configuring support tool stacks, or building support-to-CS feedback loops. Covers Zendesk, Intercom, Freshdesk, and help desk best practices.
Support Operations
Strategic support operations expertise for customer-facing teams — from ticket management and SLA design to escalation workflows and self-service optimization.
Philosophy
Great support isn't about closing tickets fast. It's about solving customer problems permanently while building scalable systems.
The best support operations teams:
- Prevent before they support — Self-service and proactive help reduce ticket volume
- Measure what drives loyalty — Resolution quality beats response speed
- Escalate with context — Every handoff preserves customer history
- Feed insights upstream — Support data drives product and success improvements
How This Skill Works
When invoked, apply the guidelines in rules/ organized by:
ticket-* — Ticket management, prioritization, queue optimizationsla-* — SLA design, compliance monitoring, escalation triggerstier-* — Support tier structure, skill-based routing, specializationknowledge-* — Knowledge base strategy, self-service, deflectionmetrics-* — CSAT, FRT, TTR, FCR, quality scoringescalation-* — Severity definitions, escalation paths, incident managementtooling-* — Support stack optimization, integrations, automationfeedback-* — Support-to-CS handoffs, product feedback loops, voice of customer
Core Frameworks
The Support Operations Hierarchy
| Level |
Focus |
Metrics |
Owner |
| Tickets |
Individual resolution |
Handle time, CSAT |
Agents |
| Queue |
Flow optimization |
Wait time, backlog |
Team leads |
| Channel |
Channel effectiveness |
Deflection, containment |
Managers |
| Operations |
System performance |
Cost per ticket, NPS |
Directors |
| Strategy |
Business impact |
Retention, expansion |
VP/C-level |
The Support Tier Model
┌─────────────────────────────────────────────────────────────────┐
│ TIER 3 (L3) │
│ Engineering escalation, code-level issues, custom development │
│ Target: <5% of tickets | SLA: Best effort │
├─────────────────────────────────────────────────────────────────┤
│ TIER 2 (L2) │
│ Technical specialists, complex troubleshooting, integrations │
│ Target: 15-25% of tickets | SLA: 4-8 hours │
├─────────────────────────────────────────────────────────────────┤
│ TIER 1 (L1) │
│ First response, common issues, documentation guidance │
│ Target: 60-80% resolution | SLA: 15-60 minutes │
├─────────────────────────────────────────────────────────────────┤
│ SELF-SERVICE (L0) │
│ Knowledge base, chatbots, community forums, in-app help │
│ Target: 30-50% deflection | SLA: Instant │
└─────────────────────────────────────────────────────────────────┘
Ticket Priority Matrix
| Priority |
Business Impact |
Response SLA |
Resolution SLA |
Examples |
| P1 Critical |
Complete outage, data loss |
15 min |
4 hours |
System down, security breach |
| P2 High |
Major feature broken |
1 hour |
8 hours |
Key workflow blocked |
| P3 Medium |
Feature impaired |
4 hours |
24 hours |
Partial functionality |
| P4 Low |
Minor issue, cosmetic |
8 hours |
72 hours |
UI bug, minor inconvenience |
| P5 Request |
Feature request, how-to |
24 hours |
5 days |
Enhancement, training |
Support Metrics Framework
| Metric |
Definition |
Target |
Warning |
| CSAT |
Customer satisfaction score |
90%+ |
<85% |
| FRT |
First response time |
<1 hour |
>4 hours |
| TTR |
Time to resolution |
<24 hours |
>72 hours |
| FCR |
First contact resolution |
70%+ |
<50% |
| NPS |
Net promoter score |
30+ |
<10 |
| Ticket Volume |
Tickets per 100 customers |
5-15 |
>25 |
| Deflection Rate |
Self-service success |
30-50% |
<20% |
| Escalation Rate |
Tickets escalated |
10-20% |
>30% |
| Reopen Rate |
Tickets reopened |
<5% |
>10% |
| Agent Utilization |
Productive time |
70-80% |
<60% or >90% |
The Ticket Lifecycle
┌─────────────────────────────────────────────────────────────────┐
│ │
│ NEW → TRIAGED → ASSIGNED → IN PROGRESS → PENDING → RESOLVED │
│ │ │ │
│ ▼ ▼ │
│ ESCALATED WAITING │
│ │ (Customer) │
│ ▼ │
│ ENGINEERING │
│ │
└─────────────────────────────────────────────────────────────────┘
Channel Strategy Matrix
| Channel |
Best For |
Cost |
Scalability |
Personal |
| Self-service |
Common issues |
Lowest |
Highest |
Lowest |
| Chatbot |
Quick questions |
Low |
High |
Low |
| Live chat |
Real-time help |
Medium |
Medium |
Medium |
| Email/Ticket |
Complex issues |
Medium |
Medium |
Medium |
| Phone |
Urgent/sensitive |
High |
Low |
High |
| Video |
Technical demos |
High |
Low |
Highest |
Severity Levels
| Severity |
Definition |
Escalation Path |
Communication |
| SEV1 |
System-wide outage |
Immediate to engineering + exec |
Status page, proactive email |
| SEV2 |
Major feature broken |
1 hour to L3 |
Affected users notified |
| SEV3 |
Feature degraded |
4 hours to L2 |
Standard ticket updates |
| SEV4 |
Minor impact |
Normal queue |
Standard ticket updates |
Key Formulas
Cost Per Ticket
Cost Per Ticket = (Total Support Cost) / (Total Tickets Handled)
Target: $5-25 depending on complexity
Support Capacity Planning
Required Agents = (Ticket Volume × Handle Time) / (Available Hours × Utilization Rate)
Example:
(500 tickets × 20 min) / (8 hours × 60 min × 0.75) = 28 agents
Self-Service ROI
Savings = (Deflected Tickets × Cost Per Ticket) - Self-Service Investment
Anti-Patterns
- Speed over quality — Fast wrong answers create repeat contacts
- Ticket tennis — Multiple handoffs without resolution
- Knowledge hoarding — Solutions in heads, not documentation
- Metric gaming — Closing tickets prematurely to hit targets
- Escalation avoidance — L1 struggling when L2 is needed
- Channel forcing — Making customers switch channels unnecessarily
- Copy-paste responses — Generic answers that don't address the issue
- Invisible backlog — Tickets aging without visibility
- No feedback loop — Support insights never reach product
- Over-automation — Bots handling issues that need humans
By