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