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
1---2name: support-operations3description: 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.4---5
6# Support Operations
7
8Strategic support operations expertise for customer-facing teams — from ticket management and SLA design to escalation workflows and self-service optimization.
9
10## Philosophy
11
12Great support isn't about closing tickets fast. It's about **solving customer problems permanently** while building scalable systems.
13
14The best support operations teams:
151. **Prevent before they support** — Self-service and proactive help reduce ticket volume
162. **Measure what drives loyalty** — Resolution quality beats response speed
173. **Escalate with context** — Every handoff preserves customer history
184. **Feed insights upstream** — Support data drives product and success improvements
19
20## How This Skill Works
21
22When invoked, apply the guidelines in `rules/` organized by:
23
24- `ticket-*` — Ticket management, prioritization, queue optimization
25- `sla-*` — SLA design, compliance monitoring, escalation triggers
26- `tier-*` — Support tier structure, skill-based routing, specialization
27- `knowledge-*` — Knowledge base strategy, self-service, deflection
28- `metrics-*` — CSAT, FRT, TTR, FCR, quality scoring
29- `escalation-*` — Severity definitions, escalation paths, incident management
30- `tooling-*` — Support stack optimization, integrations, automation
31- `feedback-*` — Support-to-CS handoffs, product feedback loops, voice of customer
32
33## Core Frameworks
34
35### The Support Operations Hierarchy
36
37| Level | Focus | Metrics | Owner |
38|-------|-------|---------|-------|
39| **Tickets** | Individual resolution | Handle time, CSAT | Agents |
40| **Queue** | Flow optimization | Wait time, backlog | Team leads |
41| **Channel** | Channel effectiveness | Deflection, containment | Managers |
42| **Operations** | System performance | Cost per ticket, NPS | Directors |
43| **Strategy** | Business impact | Retention, expansion | VP/C-level |
44
45### The Support Tier Model
46
47```
48┌─────────────────────────────────────────────────────────────────┐
49│ TIER 3 (L3) │
50│ Engineering escalation, code-level issues, custom development │
51│ Target: <5% of tickets | SLA: Best effort │
52├─────────────────────────────────────────────────────────────────┤
53│ TIER 2 (L2) │
54│ Technical specialists, complex troubleshooting, integrations │
55│ Target: 15-25% of tickets | SLA: 4-8 hours │
56├─────────────────────────────────────────────────────────────────┤
57│ TIER 1 (L1) │
58│ First response, common issues, documentation guidance │
59│ Target: 60-80% resolution | SLA: 15-60 minutes │
60├─────────────────────────────────────────────────────────────────┤
61│ SELF-SERVICE (L0) │
62│ Knowledge base, chatbots, community forums, in-app help │
63│ Target: 30-50% deflection | SLA: Instant │
64└─────────────────────────────────────────────────────────────────┘
65```
66
67### Ticket Priority Matrix
68
69| Priority | Business Impact | Response SLA | Resolution SLA | Examples |
70|----------|-----------------|--------------|----------------|----------|
71| **P1 Critical** | Complete outage, data loss | 15 min | 4 hours | System down, security breach |
72| **P2 High** | Major feature broken | 1 hour | 8 hours | Key workflow blocked |
73| **P3 Medium** | Feature impaired | 4 hours | 24 hours | Partial functionality |
74| **P4 Low** | Minor issue, cosmetic | 8 hours | 72 hours | UI bug, minor inconvenience |
75| **P5 Request** | Feature request, how-to | 24 hours | 5 days | Enhancement, training |
76
77### Support Metrics Framework
78
79| Metric | Definition | Target | Warning |
80|--------|------------|--------|---------|
81| **CSAT** | Customer satisfaction score | 90%+ | <85% |
82| **FRT** | First response time | <1 hour | >4 hours |
83| **TTR** | Time to resolution | <24 hours | >72 hours |
84| **FCR** | First contact resolution | 70%+ | <50% |
85| **NPS** | Net promoter score | 30+ | <10 |
86| **Ticket Volume** | Tickets per 100 customers | 5-15 | >25 |
87| **Deflection Rate** | Self-service success | 30-50% | <20% |
88| **Escalation Rate** | Tickets escalated | 10-20% | >30% |
89| **Reopen Rate** | Tickets reopened | <5% | >10% |
90| **Agent Utilization** | Productive time | 70-80% | <60% or >90% |
91
92### The Ticket Lifecycle
93
94```
95┌─────────────────────────────────────────────────────────────────┐
96│ │
97│ NEW → TRIAGED → ASSIGNED → IN PROGRESS → PENDING → RESOLVED │
98│ │ │ │
99│ ▼ ▼ │
100│ ESCALATED WAITING │
101│ │ (Customer) │
102│ ▼ │
103│ ENGINEERING │
104│ │
105└─────────────────────────────────────────────────────────────────┘
106```
107
108### Channel Strategy Matrix
109
110| Channel | Best For | Cost | Scalability | Personal |
111|---------|----------|------|-------------|----------|
112| **Self-service** | Common issues | Lowest | Highest | Lowest |
113| **Chatbot** | Quick questions | Low | High | Low |
114| **Live chat** | Real-time help | Medium | Medium | Medium |
115| **Email/Ticket** | Complex issues | Medium | Medium | Medium |
116| **Phone** | Urgent/sensitive | High | Low | High |
117| **Video** | Technical demos | High | Low | Highest |
118
119## Severity Levels
120
121| Severity | Definition | Escalation Path | Communication |
122|----------|------------|-----------------|---------------|
123| **SEV1** | System-wide outage | Immediate to engineering + exec | Status page, proactive email |
124| **SEV2** | Major feature broken | 1 hour to L3 | Affected users notified |
125| **SEV3** | Feature degraded | 4 hours to L2 | Standard ticket updates |
126| **SEV4** | Minor impact | Normal queue | Standard ticket updates |
127
128## Key Formulas
129
130### Cost Per Ticket
131```
132Cost Per Ticket = (Total Support Cost) / (Total Tickets Handled)
133Target: $5-25 depending on complexity
134```
135
136### Support Capacity Planning
137```
138Required Agents = (Ticket Volume × Handle Time) / (Available Hours × Utilization Rate)
139
140Example:
141(500 tickets × 20 min) / (8 hours × 60 min × 0.75) = 28 agents
142```
143
144### Self-Service ROI
145```
146Savings = (Deflected Tickets × Cost Per Ticket) - Self-Service Investment
147```
148
149## Anti-Patterns
150
151- **Speed over quality** — Fast wrong answers create repeat contacts
152- **Ticket tennis** — Multiple handoffs without resolution
153- **Knowledge hoarding** — Solutions in heads, not documentation
154- **Metric gaming** — Closing tickets prematurely to hit targets
155- **Escalation avoidance** — L1 struggling when L2 is needed
156- **Channel forcing** — Making customers switch channels unnecessarily
157- **Copy-paste responses** — Generic answers that don't address the issue
158- **Invisible backlog** — Tickets aging without visibility
159- **No feedback loop** — Support insights never reach product
160- **Over-automation** — Bots handling issues that need humans
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