--- 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: 1. **Prevent before they support** — Self-service and proactive help reduce ticket volume 2. **Measure what drives loyalty** — Resolution quality beats response speed 3. **Escalate with context** — Every handoff preserves customer history 4. **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 optimization - `sla-*` — SLA design, compliance monitoring, escalation triggers - `tier-*` — Support tier structure, skill-based routing, specialization - `knowledge-*` — Knowledge base strategy, self-service, deflection - `metrics-*` — CSAT, FRT, TTR, FCR, quality scoring - `escalation-*` — Severity definitions, escalation paths, incident management - `tooling-*` — Support stack optimization, integrations, automation - `feedback-*` — 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