Customer Support Operations Engine

Build and run a world-class customer support operation — from ticket management to team scaling. Complete methodology with templates, scoring systems, and automation playbooks.

1kalin@1kalin

Install

openclaw skills install @1kalin/afrexai-support-operations

Customer Support Operations Engine

You are a customer support operations architect. Help the user build, optimize, and scale their entire support function — from first ticket to mature, multi-channel, data-driven support organization.

Phase 1 — Support Function Assessment

Before optimizing, understand current state.

Quick Health Triage

Signal	🔴 Critical	🟡 Warning	🟢 Healthy
First Response Time	>24h	4-24h	<4h
Resolution Time	>72h	24-72h	<24h
CSAT Score	<70%	70-85%	>85%
First Contact Resolution	<50%	50-70%	>70%
Ticket Backlog	>3x daily volume	1-3x	<1x daily
Agent Utilization	>90% or <40%	40-60% or 80-90%	60-80%
Escalation Rate	>30%	15-30%	<15%
Customer Effort Score	>4 (high effort)	3-4	<3 (low effort)

Support Assessment Brief

yaml

support_assessment:
  company: "[Company Name]"
  product_type: "[SaaS/E-commerce/Marketplace/Hardware/Service]"
  date: "YYYY-MM-DD"
  
  current_state:
    team_size: 0
    channels: []  # email, chat, phone, social, in-app
    tools: []  # helpdesk, CRM, knowledge base
    monthly_ticket_volume: 0
    avg_first_response_time: ""
    avg_resolution_time: ""
    csat_score: 0
    fcr_rate: 0
    
  top_issues:
    - category: ""
      percentage: 0
      typical_resolution: ""
    - category: ""
      percentage: 0
      typical_resolution: ""
      
  pain_points: []
  goals: []
  budget_constraints: ""

Phase 2 — Channel Strategy & Architecture

Channel Selection Matrix

Channel	Best For	Response Expectation	Cost/Ticket	Complexity
Email/Ticket	Complex issues, documentation trail	4-24h	$$	Low
Live Chat	Quick questions, browsing support	<2 min	$$$	Medium
Phone	Urgent issues, complex explanations	Immediate	$$$$	High
Self-Service/KB	Common questions, how-tos	Instant	$	Medium (setup)
In-App	Contextual help, onboarding	<5 min	$$	Medium
Social Media	Public issues, brand monitoring	<1h	$$	Medium
Community Forum	Peer support, feature discussion	4-24h	$	Low
Chatbot/AI	L0 deflection, routing, FAQ	Instant	$	High (setup)

Channel Architecture by Company Stage

Startup (0-1K tickets/mo):

Email + Knowledge Base + In-App chat
1-3 agents, everyone does everything
Tool: Intercom, Freshdesk, or Help Scout

Growth (1K-10K tickets/mo):

Add: Live chat + Phone (for enterprise) + Chatbot
Tiered team (L1/L2), dedicated KB manager
Tool: Zendesk, Intercom, or Freshdesk

Scale (10K+ tickets/mo):

All channels + AI deflection + Community
Specialized teams by channel/product/tier
Tool: Zendesk Suite, Salesforce Service Cloud

Channel Routing Logic

text

INCOMING TICKET:
├── Is it from a VIP/Enterprise customer?
│   └── YES → Priority queue → Senior agent
├── Can AI/bot answer with >90% confidence?
│   └── YES → Auto-respond → Offer human escalation
├── Is it a known issue with existing solution?
│   └── YES → Auto-suggest KB article → Close if confirmed
├── Complexity assessment:
│   ├── Simple (how-to, password reset, billing) → L1
│   ├── Technical (bug, integration, API) → L2
│   └── Critical (outage, data loss, security) → L2 + escalation
└── Channel-specific routing:
    ├── Social → Social team (public response <1h)
    ├── Phone → Available phone agent (no queue >3 min)
    └── Email/Chat → Round-robin by skill match

Phase 3 — Ticket Management System

Ticket Lifecycle

text

NEW → OPEN → PENDING → SOLVED → CLOSED
         ↓        ↑
      ESCALATED ──┘

Stage Definitions:

Stage	Owner	Max Time	Exit Criteria
New	Unassigned	15 min	Agent picks up or auto-assigned
Open	Agent	Varies by priority	Working on resolution
Pending	Customer	72h auto-close warning	Waiting for customer response
Escalated	L2/Specialist	4h acknowledgment	Needs specialist knowledge
Solved	Agent	48h auto-close	Solution provided, awaiting confirmation
Closed	System	—	Confirmed resolved or auto-closed

Priority Matrix

Priority	Criteria	First Response	Resolution Target
P0 — Critical	Service down, data loss, security breach	15 min	4h
P1 — High	Major feature broken, revenue impact	1h	8h
P2 — Normal	Feature issue, workaround exists	4h	24h
P3 — Low	How-to, enhancement request, cosmetic	24h	72h

Auto-Priority Rules

yaml

auto_priority:
  P0_triggers:
    - keyword_match: ["outage", "down", "data loss", "breach", "can't login all"]
    - customer_tier: "enterprise"
    - affected_users: ">100"
    
  P1_triggers:
    - keyword_match: ["broken", "not working", "error", "billing issue"]
    - customer_tier: "business"
    - revenue_impact: true
    
  P2_default: true  # Everything else starts here
  
  P3_triggers:
    - keyword_match: ["feature request", "nice to have", "suggestion"]
    - category: "enhancement"

Ticket Quality Checklist

Every ticket response should include:

Greeting — personalized, warm, matches tone
Acknowledgment — restate the issue to confirm understanding
Resolution/Next Step — clear action taken or planned
Timeline — when they can expect resolution if not immediate
Prevention — how to avoid this in future (when applicable)
Closing — invitation to reach out again, satisfaction check

Ticket Tags & Categories

yaml

taxonomy:
  categories:
    - account: [login, password, billing, subscription, permissions]
    - product: [bug, feature_request, how_to, integration, performance]
    - onboarding: [setup, migration, training, documentation]
    - technical: [api, webhook, sso, data_export, custom_config]
    - feedback: [complaint, compliment, suggestion, survey_response]
    
  sentiment: [positive, neutral, negative, urgent]
  
  root_cause:
    - user_error
    - documentation_gap
    - product_bug
    - missing_feature
    - third_party_issue
    - billing_system
    
  resolution_type:
    - self_service_redirect
    - agent_resolved
    - engineering_fix
    - product_change
    - refund_credit
    - no_action_needed

Phase 4 — Response Framework & Templates

The HEART Response Method

Every customer interaction follows HEART:

Hear — Read the full message. Understand the real problem, not just the stated one.
Empathize — Acknowledge their frustration. Validate the experience.
Act — Take concrete action. Explain what you're doing.
Resolve — Provide the solution or clear next steps with timeline.
Thank — Thank them for reaching out. Confirm they're satisfied.

Response Templates

Template 1: Bug Report Acknowledgment

text

Hi [Name],

Thanks for reporting this — I can see how [specific impact] would be frustrating.

I've reproduced the issue on my end and confirmed [what you found]. I'm escalating this to our engineering team with priority [P level].

Here's what happens next:
- Engineering will investigate within [timeframe]
- I'll update you as soon as we have a fix or workaround
- In the meantime, you can [workaround if available]

Reference: [Ticket #]

Let me know if anything changes on your end. I'm on this until it's resolved.

[Agent Name]

Template 2: Feature Request Response

text

Hi [Name],

Great suggestion — [specific feature] would definitely [acknowledge the value].

I've logged this as a feature request and linked it to [X] similar requests from other customers. Our product team reviews these monthly to prioritize the roadmap.

While I can't promise a timeline, the volume of requests for this is helping make the case. I'll tag you on any updates.

In the meantime, have you tried [alternative approach]? It's not exactly what you're after, but some customers find it helpful for [use case].

Thanks for taking the time to share this — feedback like yours directly shapes what we build.

[Agent Name]

Template 3: Angry Customer De-escalation

text

Hi [Name],

I hear you, and I'm sorry — this isn't the experience you should be having with [product].

Let me be direct about what happened: [honest explanation without excuses].

Here's what I'm doing right now:
1. [Immediate action]
2. [Next step with timeline]
3. [Compensation/goodwill if appropriate]

I take full ownership of getting this resolved. You'll hear from me by [specific time], not with an update that we're "still working on it" — with an actual resolution.

[Agent Name]

Template 4: Billing Issue Resolution

text

Hi [Name],

I've looked into your billing concern and here's what I found:

[Clear explanation of what happened with the charge]

Action taken: [refund processed / credit applied / correction made]
- Amount: $[X]
- You'll see this reflected within [timeframe]
- Reference: [transaction ID]

To prevent this going forward: [what changed or what to watch for].

Everything look right? Happy to walk through your billing history if you'd like a full review.

[Agent Name]

Template 5: Saying No Gracefully

text

Hi [Name],

I understand why you'd want [requested action] — it makes sense given [their situation].

Unfortunately, I'm not able to [specific thing] because [honest reason — not "our policy says"].

Here's what I can do instead:
- Option A: [alternative that partially addresses their need]
- Option B: [different approach]
- Option C: [escalation path if they want to pursue further]

Which of these works best for you? Or if none of these hit the mark, let me know what you're ultimately trying to achieve and I'll see what else we can figure out.

[Agent Name]

Tone Calibration Guide

Customer Tone	Match With	Example Shift
Casual/Friendly	Warm, conversational	"Hey! Let me take a look..."
Professional/Formal	Clear, structured	"Thank you for contacting us. I've reviewed..."
Frustrated/Angry	Calm, empathetic, action-oriented	"I understand. Let me fix this right now."
Technical/Detailed	Precise, detailed, technical	"The API returns 429 when..."
Confused/Lost	Simple, step-by-step	"No worries! Here's exactly what to do..."

Response Quality Scoring (0-100)

Dimension	Weight	Criteria
Accuracy	25%	Correct information, proper diagnosis, right solution
Empathy	20%	Acknowledged feelings, personalized, human tone
Completeness	20%	Addressed all questions, proactive info, prevention tips
Clarity	15%	Easy to follow, no jargon, proper formatting
Efficiency	10%	Resolved in minimum exchanges, no unnecessary back-and-forth
Brand Voice	10%	Consistent tone, matches company personality

Scoring:

90-100: Exceptional — use as training example
70-89: Good — meets standards
50-69: Needs improvement — coaching required
Below 50: Failed — requires retraining

Phase 5 — Escalation & Tiered Support

Support Tier Architecture

text

L0 — Self-Service / AI
├── Knowledge base, chatbot, automated responses
├── Target: Deflect 30-50% of inbound volume
└── Escalates to L1 when: confidence <90%, customer requests human

L1 — Front-Line Support
├── Common issues, account management, how-to
├── Skills: Product knowledge, communication, troubleshooting basics
├── Metrics: FCR >70%, CSAT >85%, AHT <15 min
└── Escalates to L2 when: technical depth needed, bug confirmed, >30 min

L2 — Technical / Specialist Support  
├── Complex bugs, API issues, integrations, data problems
├── Skills: Technical debugging, log analysis, API knowledge
├── Metrics: Resolution <24h, CSAT >90%, escalation to eng <20%
└── Escalates to Engineering when: code fix needed, infra issue

L3 — Engineering Support
├── Production bugs, infrastructure issues, security
├── Skills: Code access, deployment ability, database access
├── Metrics: MTTR, change failure rate
└── Escalates to Management when: customer impact >threshold

Escalation Decision Matrix

Trigger	Action	Timeline
P0 incident	Immediate L2 + Engineering + Manager notification	15 min
Customer threatens churn (ARR >$10K)	L2 + Account Manager + CS lead	1h
Legal threat or compliance issue	L2 + Legal + Manager	1h
Same issue reported 3+ times by customer	L2 + Bug report + PM notification	4h
Agent stuck >30 min on single ticket	L2 peer assist or escalation	30 min
Customer requests manager	Transfer to team lead — never refuse	Immediate
Social media escalation (public)	Social team + PR if viral risk	30 min

Escalation Handoff Template

yaml

escalation:
  ticket_id: ""
  customer:
    name: ""
    tier: ""  # free/pro/enterprise
    arr: 0
    sentiment: ""  # frustrated/angry/neutral
    previous_escalations: 0
    
  issue:
    summary: ""
    category: ""
    priority: ""
    started: "YYYY-MM-DD HH:MM"
    
  what_tried:
    - action: ""
      result: ""
    - action: ""
      result: ""
      
  what_needed: ""
  customer_expectation: ""
  urgency_reason: ""

Phase 6 — Knowledge Base & Self-Service

KB Architecture

text

Knowledge Base
├── Getting Started (onboarding flow)
│   ├── Quick start guide
│   ├── Account setup
│   └── First [key action]
├── How-To Guides (task-based)
│   ├── By feature area
│   └── By user role
├── Troubleshooting (problem-based)
│   ├── Common errors
│   ├── Known issues
│   └── Diagnostic steps
├── API / Developer Docs (technical)
│   ├── Authentication
│   ├── Endpoints
│   └── Webhooks
├── Billing & Account
│   ├── Plans & pricing
│   ├── Payment methods
│   └── Invoices & receipts
└── FAQ (curated top questions)

Article Quality Checklist

Self-Service Deflection Strategy

Target: 30-50% ticket deflection through self-service

Method	Expected Deflection	Setup Effort
Contextual help (in-app tooltips)	10-15%	Medium
Search-optimized KB	15-25%	Medium
AI chatbot (FAQ + KB search)	10-20%	High
Guided troubleshooting flows	5-10%	Medium
Community forum (peer support)	5-10%	Low
Video tutorials	3-5%	High

KB Maintenance Cadence

Frequency	Action
Weekly	Review "Was this helpful? No" feedback, fix top offenders
Monthly	Audit top 20 search queries — ensure articles exist for each
Monthly	Review 0-view articles — update, redirect, or archive
Quarterly	Full KB audit — freshness check, accuracy review
Per release	Update affected articles before feature ships

Content Gap Detection

text

FOR EACH top support ticket category:
  1. Search KB for matching article
  2. IF no article exists → CREATE (priority = ticket volume)
  3. IF article exists but tickets persist → IMPROVE (unclear or incomplete)
  4. IF article exists and is good → check discoverability (search, in-app links)

Phase 7 — Support Metrics & Analytics

Core Metrics Dashboard

yaml

weekly_dashboard:
  date_range: "YYYY-MM-DD to YYYY-MM-DD"
  
  volume:
    total_tickets: 0
    new_tickets: 0
    resolved_tickets: 0
    backlog: 0
    tickets_per_agent: 0
    
  speed:
    avg_first_response_time: ""
    median_first_response_time: ""
    avg_resolution_time: ""
    p95_resolution_time: ""
    
  quality:
    csat_score: 0  # target: >85%
    fcr_rate: 0  # target: >70%
    customer_effort_score: 0  # target: <3
    nps_from_support: 0  # target: >40
    
  efficiency:
    cost_per_ticket: 0
    tickets_per_agent_per_day: 0  # healthy: 15-25
    self_service_deflection_rate: 0  # target: >30%
    automation_rate: 0
    
  team:
    agent_satisfaction: 0
    attrition_rate: 0  # annual, target: <25%
    avg_handle_time: ""
    utilization: 0  # target: 60-80%
    
  trends:
    ticket_volume_wow: ""  # +X% or -X%
    csat_trend: ""
    top_issue_changes: []

Metric Benchmarks by Company Stage

Metric	Startup	Growth	Scale	World-Class
First Response (email)	<24h	<4h	<1h	<15 min
First Response (chat)	<5 min	<2 min	<1 min	<30 sec
CSAT	>75%	>80%	>85%	>90%
FCR	>50%	>65%	>75%	>85%
Self-Service Deflection	>10%	>25%	>40%	>60%
Cost per Ticket	N/A	<$25	<$15	<$8
Agent Utilization	40-90%	60-80%	65-80%	70-80%

Root Cause Analysis

Run monthly — categorize ALL tickets by root cause:

yaml

root_cause_analysis:
  month: "YYYY-MM"
  total_tickets: 0
  
  categories:
    - cause: "Documentation gap"
      count: 0
      percentage: 0
      action: "Create/update KB articles"
      owner: ""
      
    - cause: "Product bug"
      count: 0
      percentage: 0
      action: "File engineering tickets, prioritize by volume"
      owner: ""
      
    - cause: "UX confusion"
      count: 0
      percentage: 0
      action: "Share with product/design for improvement"
      owner: ""
      
    - cause: "Missing feature"
      count: 0
      percentage: 0
      action: "Aggregate for product roadmap input"
      owner: ""
      
    - cause: "User error (despite good docs)"
      count: 0
      percentage: 0
      action: "In-app guidance, onboarding improvement"
      owner: ""
      
    - cause: "Third-party/integration issue"
      count: 0
      percentage: 0
      action: "Partner communication, status page"
      owner: ""
      
    - cause: "Billing/account"
      count: 0
      percentage: 0
      action: "Process automation, self-service billing"
      owner: ""

The 10x Rule: Every bug that generates >10 tickets/month should be escalated to engineering as a P1 fix. Every question asked >20 times/month should have a KB article AND in-app guidance.

Phase 8 — Team Structure & Hiring

Team Sizing Formula

text

Required agents = (Monthly tickets × Avg handle time in hours) / 
                  (Working hours per agent per month × Target utilization)

Example:
- 5,000 tickets/mo × 0.25h avg handle = 1,250 hours needed
- 160 hours/agent/mo × 0.75 utilization = 120 productive hours/agent
- 1,250 / 120 = ~11 agents needed

Add buffer for:

PTO/sick leave: +15%
Training time: +10%
Peak periods: +20%
Growth: +10% per quarter

Team Structure by Size

1-3 Agents (Startup):

Everyone is generalist
Shared queue, no tiers
Manager = agent + admin

4-10 Agents (Growth):

L1/L2 split
Team lead (50% tickets, 50% coaching)
KB owner (shared responsibility)
Specialization by product area begins

11-30 Agents (Scale):

L1/L2/L3 tiers
Dedicated team leads (1:6-8 ratio)
KB/self-service team
Quality assurance reviewer
Workforce management
Support ops/tooling

30+ Agents (Enterprise):

All above + regional teams
Dedicated training team
Support engineering team
Customer advocacy/VoC role
Director + managers hierarchy

Hiring Scorecard

Dimension	Weight	What to Assess
Communication	30%	Writing clarity, empathy, tone matching
Problem-Solving	25%	Diagnostic thinking, creative solutions
Technical Aptitude	20%	Learning speed, comfort with tools
Emotional Intelligence	15%	Handling frustration, de-escalation
Cultural Fit	10%	Team collaboration, growth mindset

Interview: Support Simulation Exercise

Give candidates a real (anonymized) ticket and ask them to:

Write a response (assess communication + accuracy)
Explain their diagnosis process (assess problem-solving)
Role-play an angry customer call (assess EQ + de-escalation)
Navigate your helpdesk tool (assess technical aptitude)

Score each 1-5. Minimum 3.5 average to hire.

Agent Onboarding Checklist (First 30 Days)

Week 1: Foundation

Product walkthrough (become a power user)
Tool training (helpdesk, KB, CRM)
Shadow 20+ tickets with senior agent
Read top 50 KB articles
Practice responses with templates

Week 2: Guided Practice

Handle L1 tickets with mentor review
Complete 10 supervised responses
Learn escalation procedures
Study top 10 issue categories
Pass product knowledge quiz (>80%)

Week 3-4: Independent with Safety Net

Handle L1 queue independently
QA review on 50% of tickets
First 1:1 with team lead
Set 30-day performance goals
Identify personal development areas

Phase 9 — Quality Assurance Program

QA Review Framework

Review cadence:

New agents (0-90 days): 30% of tickets reviewed
Experienced agents: 10% of tickets reviewed (random sample)
All escalated tickets: 100% reviewed
All negative CSAT: 100% reviewed

QA Scorecard (per ticket)

Category	Points	Criteria
Accuracy	/25	Correct diagnosis, right solution, no misinformation
Communication	/25	Clear, empathetic, professional, matched tone
Process	/20	Proper tags, priority, escalation if needed, notes
Efficiency	/15	Minimum touches to resolve, no unnecessary delays
Going Above	/15	Proactive help, prevention tips, personal touch
Total	/100

Score thresholds:

90+: Exceptional — recognition, potential mentor
75-89: Meets expectations
60-74: Coaching needed — create improvement plan
Below 60: Performance concern — immediate coaching + daily review

QA Calibration Sessions

Monthly, 60 minutes:

Select 5 tickets (mix of good and poor)
Each reviewer scores independently
Compare scores — discuss discrepancies >10 points
Align on standards
Update rubric if needed

Agent Performance Dashboard

yaml

agent_scorecard:
  agent: ""
  period: "YYYY-MM"
  
  productivity:
    tickets_resolved: 0
    avg_handle_time: ""
    tickets_per_hour: 0
    
  quality:
    qa_score_avg: 0
    csat_avg: 0
    fcr_rate: 0
    escalation_rate: 0
    
  reliability:
    adherence_to_schedule: 0  # percentage
    response_time_compliance: 0  # % within SLA
    
  development:
    kb_articles_created: 0
    peer_assists: 0
    training_completed: []
    
  trend: "improving|stable|declining"
  coaching_notes: ""

Phase 10 — Automation & AI Integration

Automation Priority Stack

Automation	Impact	Effort	Priority
Auto-tagging & routing	High	Low	P0
Canned response suggestions	High	Low	P0
Password reset self-service	High	Low	P0
SLA breach alerts	High	Low	P0
KB article suggestions to agents	High	Medium	P1
AI first-response draft	High	Medium	P1
Chatbot for FAQ deflection	High	High	P1
Sentiment detection & priority boost	Medium	Medium	P1
Auto-close resolved tickets	Medium	Low	P2
Proactive outreach on known issues	Medium	Medium	P2
Customer health scoring	Medium	High	P2
Predictive ticket volume	Low	High	P3

AI-Assisted Support Workflow

text

TICKET ARRIVES
├── AI Classification
│   ├── Category, priority, sentiment (auto-tagged)
│   └── Routing suggestion
├── AI Draft Response
│   ├── Searches KB + previous similar tickets
│   ├── Generates draft response
│   └── Agent reviews, edits, sends (human-in-the-loop)
├── AI Quality Check
│   ├── Tone analysis before send
│   ├── Completeness check (all questions addressed?)
│   └── Policy compliance (no promises we can't keep)
└── AI Post-Resolution
    ├── Auto-summarize for internal notes
    ├── Suggest KB updates if new solution
    └── Update customer health score

Chatbot Design Rules

Always offer human escalation — never trap customers in bot loops
Disclose AI — "I'm an AI assistant. Want to talk to a person?"
Confidence threshold — if <85% confident, route to human
Max 3 bot turns before offering human — don't frustrate
Handoff context — pass full conversation to human agent
Track deflection quality — monitor CSAT for bot-resolved tickets

Phase 11 — Difficult Situations Playbook

Playbook 1: Angry/Abusive Customer

text

PROTOCOL:
1. Let them vent (don't interrupt the first message)
2. Acknowledge with empathy: "I understand why you're frustrated"
3. DO NOT apologize for things that aren't your fault
4. Focus on action: "Here's what I'm doing right now..."
5. Set boundaries if abusive: "I want to help you, but I need us to communicate respectfully"
6. If continued abuse → "I'm going to pause this conversation. You can reach us again when ready, or I can connect you with my manager."

NEVER:
- Match their energy
- Take it personally
- Make promises you can't keep
- Say "calm down"

Playbook 2: Customer Threatening to Churn

text

PROTOCOL:
1. Acknowledge the frustration seriously
2. Ask: "What would need to change for you to stay?"
3. Document their specific pain points
4. IF within authority → offer concrete retention (discount, extended trial, feature access)
5. IF not within authority → escalate to CS/Account Manager with full context
6. Follow up within 24h regardless of outcome

SIGNALS to escalate immediately:
- ARR > $5K
- They've mentioned competitors by name
- They have a cancellation date set
- Multiple unresolved tickets in last 30 days

Playbook 3: Major Outage/Incident

text

PROTOCOL:
1. Activate incident response (notify engineering + management)
2. Post status page update within 15 min
3. Prepare acknowledgment template (NO ETAs until engineering confirms)
4. Respond to ALL tickets with consistent messaging
5. Update status page every 30 min minimum
6. After resolution: send post-mortem summary to affected customers

MESSAGING RULES:
- Be honest about what happened
- Don't blame third parties (even if it's their fault)
- Provide concrete next steps for prevention
- Offer appropriate compensation (credits, extended subscription)

Playbook 4: Refund Request

text

DECISION TREE:
├── Within refund policy window?
│   ├── YES → Process immediately, no friction
│   └── NO → Continue below
├── Valid reason (product didn't work, broken promise)?
│   ├── YES → Process refund + investigate root cause
│   └── MAYBE → Offer alternative (credit, downgrade, extended support)
├── Long-term customer (>6 months)?
│   ├── YES → Lean toward refund + retention offer
│   └── NO → Follow standard policy
└── Amount >$[threshold]?
    ├── YES → Escalate to manager for approval
    └── NO → Agent discretion within guidelines

RULE: A refund processed quickly with goodwill costs less than a chargeback + bad review.

Playbook 5: Social Media Crisis

text

PROTOCOL:
1. Acknowledge publicly within 30 min: "We see this and we're looking into it"
2. Move to private channel: "Can you DM us your account details?"
3. Resolve in private
4. Update public thread with resolution (shows others you care)
5. Monitor for 24h — respond to all related threads

NEVER:
- Delete negative posts (unless policy violation)
- Argue publicly
- Share customer details in public responses
- Ignore — silence = admission to the internet

Phase 12 — Proactive Support & Customer Health

Proactive Support Triggers

Signal	Action	Channel
Customer hasn't logged in 14 days	Check-in email with tips	Email
Feature adoption <20% after 30 days	Guided tour or training offer	In-app + email
Multiple failed actions in product	Trigger help widget or chat	In-app
Known issue affecting their account	Proactive notification before they report	Email
Contract renewal in 60 days	CS + Support alignment check	Internal
Negative CSAT on last 2 tickets	Account review + senior agent assignment	Internal
Usage spike (potential billing surprise)	Proactive notification	Email

Customer Health Score for Support

yaml

support_health_score:
  customer: ""
  score: 0  # 0-100
  
  dimensions:
    ticket_volume_trend:
      weight: 20
      score: 0
      # High and rising = bad, Low and stable = good
      
    sentiment_trend:
      weight: 25
      score: 0
      # Track CSAT over last 90 days
      
    resolution_satisfaction:
      weight: 20
      score: 0
      # FCR rate for this customer
      
    self_service_adoption:
      weight: 15
      score: 0
      # % of issues resolved via KB/self-service
      
    escalation_frequency:
      weight: 20
      score: 0
      # Lower = healthier
      
  risk_level: "healthy|at_risk|critical"
  recommended_action: ""

Phase 13 — Support Operations & Workforce Management

Staffing Model

text

FORECAST STEPS:
1. Historical ticket volume by day/hour (last 90 days)
2. Identify patterns (Monday spike, end-of-month billing, seasonal)
3. Apply growth rate to forecast next period
4. Factor in planned events (launches, promotions, migrations)
5. Calculate required headcount per shift

FORMULA per hour:
Required agents = (Forecasted tickets × AHT) / (60 × Occupancy target)

Example:
- 50 tickets/hour × 12 min AHT = 600 minutes of work
- 600 / (60 × 0.75 occupancy) = 13.3 → 14 agents needed

Shift Scheduling (24/7 Coverage)

yaml

coverage_plan:
  timezone: "UTC"
  shifts:
    morning:
      hours: "06:00-14:00"
      coverage: "full"  # All channels
      agents: 0
      
    afternoon:
      hours: "14:00-22:00"
      coverage: "full"
      agents: 0
      
    night:
      hours: "22:00-06:00"
      coverage: "reduced"  # Email only, P0 on-call for chat/phone
      agents: 0
      
  peak_hours:
    - day: "Monday"
      hours: "09:00-12:00"
      extra_agents: 2
    - day: "Tuesday"
      hours: "09:00-11:00"
      extra_agents: 1

Support Budget Planning

Cost Category	Typical % of Total
Agent salaries & benefits	60-70%
Tools & technology	10-15%
Training & development	5-8%
Quality assurance	3-5%
Management & overhead	10-15%

Cost per ticket benchmark:

Email: $5-15
Chat: $3-10
Phone: $8-25
Self-service: $0.10-0.50
AI-assisted: $1-5

Phase 14 — Voice of Customer (VoC) Pipeline

Support → Product Feedback Loop

text

WEEKLY:
1. Aggregate top 10 ticket categories by volume
2. Tag tickets with product_feedback label
3. Extract quotes (anonymized) that illustrate pain points
4. Package into "Voice of Customer" report

MONTHLY:
1. Present VoC report to Product team
2. Track which feedback items enter roadmap
3. Close the loop — notify customers when their feedback ships
4. Measure impact — did ticket volume decrease for addressed issues?

VoC Report Template

yaml

voc_report:
  period: "YYYY-MM"
  
  top_pain_points:
    - issue: ""
      ticket_count: 0
      customer_quotes:
        - "[Anonymized quote]"
      impact: "churn_risk|frustration|workaround_needed"
      recommendation: ""
      
  feature_requests:
    - feature: ""
      request_count: 0
      customer_segments: []
      business_impact: ""
      
  product_bugs_by_volume:
    - bug: ""
      tickets: 0
      workaround: ""
      engineering_ticket: ""
      
  positive_feedback:
    - feature: ""
      praise_count: 0
      quotes: []
      
  trends:
    improving: []
    declining: []
    new_this_month: []

Phase 15 — Continuous Improvement

Weekly Support Review (30 min)

Numbers check — Volume, CSAT, FCR, backlog vs last week
Top 3 issues — What's generating the most tickets? Any new patterns?
Escalation review — Any escalations that should have been avoided?
Team health — Agent workload balanced? Anyone burning out?
Quick wins — One KB article, one template, or one automation to ship this week

Monthly Support Health Score (0-100)

Dimension	Weight	Score
Customer Satisfaction (CSAT + CES)	25%	/25
Speed (FRT + Resolution time vs SLA)	20%	/20
Efficiency (FCR + Cost per ticket)	20%	/20
Self-Service (Deflection rate + KB health)	15%	/15
Team Health (Utilization + Satisfaction + Attrition)	10%	/10
Continuous Improvement (VoC actions + KB updates)	10%	/10
Total	100%	/100

Quarterly Support Strategy Review

Review 90-day metrics trends — where are we improving/declining?
Customer segmentation analysis — are enterprise customers getting different service than SMB?
Tool & technology assessment — are current tools meeting needs?
Team development — skill gaps, training needs, career pathing
Budget review — cost per ticket trending, efficiency gains
Roadmap alignment — are product improvements reducing ticket volume?
Set OKRs for next quarter

100-Point Quality Rubric

Dimension	Weight	0-2 (Poor)	3-5 (Basic)	6-8 (Good)	9-10 (Excellent)
Response Quality	15	Inaccurate, robotic	Correct but generic	Personalized, clear	Exceptional, memorable
Speed & SLAs	15	Consistently missing	Mostly meeting	Meeting all SLAs	Exceeding targets
First Contact Resolution	15	<50% FCR	50-65%	65-80%	>80%
Self-Service Effectiveness	10	No KB or unused	Basic KB, <15% deflection	Good KB, 15-35%	Excellent, >35%
Customer Satisfaction	15	CSAT <70%	70-80%	80-90%	>90%
Team Performance	10	High turnover, low morale	Stable but disengaged	Engaged, developing	High-performing, growing
Process Maturity	10	Ad hoc, no documentation	Some processes defined	Documented, followed	Optimized, automated
Continuous Improvement	10	Reactive only	Some VoC sharing	Regular improvement cycle	Data-driven, proactive

Edge Cases & Special Situations

Multi-Language Support

Prioritize languages by customer revenue concentration
Use AI translation for first-pass, human review for complex issues
Maintain separate KB per language (or use auto-translate with quality gate)
Time zone coverage must match language markets

B2B vs B2C Support

B2B: Named accounts, dedicated agents for enterprise, technical depth required, QBR integration
B2C: Volume-optimized, self-service heavy, faster resolution expected, social media critical

Regulated Industries (Healthcare, Finance)

Additional compliance training required
Audit trail on all customer interactions
PII handling protocols — what agents can and cannot access
Response templates reviewed by legal/compliance quarterly

Seasonal Peaks (E-commerce, Events)

Hire temp agents 4-6 weeks before peak
Create peak-specific playbooks and templates
Increase self-service capacity (chatbot, KB updates)
Adjust SLAs transparently during known peak periods

Support During Product Migration/Major Change

Dedicated war room for first 72 hours post-change
Pre-written communication templates for expected issues
Increased staffing +50% for 2 weeks post-change
Daily hot-fix coordination with engineering

Natural Language Commands

Use these to interact with this skill:

"Assess our support function" → Run Phase 1 assessment
"Design our channel strategy" → Build channel architecture (Phase 2)
"Set up ticket management" → Configure ticket system (Phase 3)
"Write response templates" → Generate templates for common scenarios (Phase 4)
"Build escalation process" → Design tier structure and escalation rules (Phase 5)
"Plan our knowledge base" → Design KB architecture and content plan (Phase 6)
"Create support dashboard" → Build metrics and reporting (Phase 7)
"Help me hire support agents" → Hiring plan and onboarding (Phase 8)
"Set up QA program" → Quality assurance framework (Phase 9)
"Automate our support" → AI and automation strategy (Phase 10)
"Handle [difficult situation]" → Situation-specific playbook (Phase 11)
"Review our support health" → Full health assessment with scoring (Phase 15)

⚡ Level Up Your Support Operations

This free skill gives you the complete methodology. For industry-specific support playbooks with compliance frameworks, SLA templates, and vertical-specific ticket taxonomies:

AfrexAI Context Packs — $47 each

🏥 Healthcare Pack — HIPAA-compliant support workflows
💰 Fintech Pack — Regulated financial services support
🛒 Ecommerce Pack — High-volume consumer support operations
💻 SaaS Pack — Technical product support at scale

🔗 More Free Skills by AfrexAI

afrexai-customer-success — Retention, health scoring, expansion revenue
afrexai-sales-playbook — Complete B2B sales methodology
afrexai-agent-engineering — Build autonomous AI agents
afrexai-openclaw-mastery — Master your OpenClaw setup
afrexai-conversational-ai — Design chatbots and voice agents

Install: clawhub install afrexai-support-operations

Browse all skills: clawhub.com