{ "schemaVersion": "1.0", "item": { "slug": "afrexai-support-operations", "name": "Customer Support Operations Engine", "source": "tencent", "type": "skill", "category": "效率提升", "sourceUrl": "https://clawhub.ai/1kalin/afrexai-support-operations", "canonicalUrl": "https://clawhub.ai/1kalin/afrexai-support-operations", "targetPlatform": "OpenClaw" }, "install": { "downloadMode": "redirect", "downloadUrl": "/downloads/afrexai-support-operations", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=afrexai-support-operations", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "installMethod": "Manual import", "extraction": "Extract archive", "prerequisites": [ "OpenClaw" ], "packageFormat": "ZIP package", "includedAssets": [ "README.md", "SKILL.md" ], "primaryDoc": "SKILL.md", "quickSetup": [ "Download the package from Yavira.", "Extract the archive and review SKILL.md first.", "Import or place the package into your OpenClaw setup." ], "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "Download the package from Yavira.", "Extract it into a folder your agent can access.", "Paste one of the prompts below and point your agent at the extracted folder." ], "prompts": [ { "label": "New install", "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete." }, { "label": "Upgrade existing", "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run." } ] }, "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-23T16:43:11.935Z", "expiresAt": "2026-04-30T16:43:11.935Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentDisposition": "attachment; filename=\"4claw-imageboard-1.0.1.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/afrexai-support-operations" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] }, "downloadPageUrl": "https://openagent3.xyz/downloads/afrexai-support-operations", "agentPageUrl": "https://openagent3.xyz/skills/afrexai-support-operations/agent", "manifestUrl": "https://openagent3.xyz/skills/afrexai-support-operations/agent.json", "briefUrl": "https://openagent3.xyz/skills/afrexai-support-operations/agent.md" }, "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "Download the package from Yavira.", "Extract it into a folder your agent can access.", "Paste one of the prompts below and point your agent at the extracted folder." ], "prompts": [ { "label": "New install", "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete." }, { "label": "Upgrade existing", "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run." } ] }, "documentation": { "source": "clawhub", "primaryDoc": "SKILL.md", "sections": [ { "title": "Customer Support Operations Engine", "body": "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." }, { "title": "Phase 1 — Support Function Assessment", "body": "Before optimizing, understand current state." }, { "title": "Quick Health Triage", "body": "Signal🔴 Critical🟡 Warning🟢 HealthyFirst Response Time>24h4-24h<4hResolution Time>72h24-72h<24hCSAT Score<70%70-85%>85%First Contact Resolution<50%50-70%>70%Ticket Backlog>3x daily volume1-3x<1x dailyAgent 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)" }, { "title": "Support Assessment Brief", "body": "support_assessment:\n company: \"[Company Name]\"\n product_type: \"[SaaS/E-commerce/Marketplace/Hardware/Service]\"\n date: \"YYYY-MM-DD\"\n \n current_state:\n team_size: 0\n channels: [] # email, chat, phone, social, in-app\n tools: [] # helpdesk, CRM, knowledge base\n monthly_ticket_volume: 0\n avg_first_response_time: \"\"\n avg_resolution_time: \"\"\n csat_score: 0\n fcr_rate: 0\n \n top_issues:\n - category: \"\"\n percentage: 0\n typical_resolution: \"\"\n - category: \"\"\n percentage: 0\n typical_resolution: \"\"\n \n pain_points: []\n goals: []\n budget_constraints: \"\"" }, { "title": "Channel Selection Matrix", "body": "ChannelBest ForResponse ExpectationCost/TicketComplexityEmail/TicketComplex issues, documentation trail4-24h$$LowLive ChatQuick questions, browsing support<2 min$$$MediumPhoneUrgent issues, complex explanationsImmediate$$$$HighSelf-Service/KBCommon questions, how-tosInstant$Medium (setup)In-AppContextual help, onboarding<5 min$$MediumSocial MediaPublic issues, brand monitoring<1h$$MediumCommunity ForumPeer support, feature discussion4-24h$LowChatbot/AIL0 deflection, routing, FAQInstant$High (setup)" }, { "title": "Channel Architecture by Company Stage", "body": "Startup (0-1K tickets/mo):\n\nEmail + Knowledge Base + In-App chat\n1-3 agents, everyone does everything\nTool: Intercom, Freshdesk, or Help Scout\n\nGrowth (1K-10K tickets/mo):\n\nAdd: Live chat + Phone (for enterprise) + Chatbot\nTiered team (L1/L2), dedicated KB manager\nTool: Zendesk, Intercom, or Freshdesk\n\nScale (10K+ tickets/mo):\n\nAll channels + AI deflection + Community\nSpecialized teams by channel/product/tier\nTool: Zendesk Suite, Salesforce Service Cloud" }, { "title": "Channel Routing Logic", "body": "INCOMING TICKET:\n├── Is it from a VIP/Enterprise customer?\n│ └── YES → Priority queue → Senior agent\n├── Can AI/bot answer with >90% confidence?\n│ └── YES → Auto-respond → Offer human escalation\n├── Is it a known issue with existing solution?\n│ └── YES → Auto-suggest KB article → Close if confirmed\n├── Complexity assessment:\n│ ├── Simple (how-to, password reset, billing) → L1\n│ ├── Technical (bug, integration, API) → L2\n│ └── Critical (outage, data loss, security) → L2 + escalation\n└── Channel-specific routing:\n ├── Social → Social team (public response <1h)\n ├── Phone → Available phone agent (no queue >3 min)\n └── Email/Chat → Round-robin by skill match" }, { "title": "Ticket Lifecycle", "body": "NEW → OPEN → PENDING → SOLVED → CLOSED\n ↓ ↑\n ESCALATED ──┘\n\nStage Definitions:\n\nStageOwnerMax TimeExit CriteriaNewUnassigned15 minAgent picks up or auto-assignedOpenAgentVaries by priorityWorking on resolutionPendingCustomer72h auto-close warningWaiting for customer responseEscalatedL2/Specialist4h acknowledgmentNeeds specialist knowledgeSolvedAgent48h auto-closeSolution provided, awaiting confirmationClosedSystem—Confirmed resolved or auto-closed" }, { "title": "Priority Matrix", "body": "PriorityCriteriaFirst ResponseResolution TargetP0 — CriticalService down, data loss, security breach15 min4hP1 — HighMajor feature broken, revenue impact1h8hP2 — NormalFeature issue, workaround exists4h24hP3 — LowHow-to, enhancement request, cosmetic24h72h" }, { "title": "Auto-Priority Rules", "body": "auto_priority:\n P0_triggers:\n - keyword_match: [\"outage\", \"down\", \"data loss\", \"breach\", \"can't login all\"]\n - customer_tier: \"enterprise\"\n - affected_users: \">100\"\n \n P1_triggers:\n - keyword_match: [\"broken\", \"not working\", \"error\", \"billing issue\"]\n - customer_tier: \"business\"\n - revenue_impact: true\n \n P2_default: true # Everything else starts here\n \n P3_triggers:\n - keyword_match: [\"feature request\", \"nice to have\", \"suggestion\"]\n - category: \"enhancement\"" }, { "title": "Ticket Quality Checklist", "body": "Every ticket response should include:\n\nGreeting — personalized, warm, matches tone\n Acknowledgment — restate the issue to confirm understanding\n Resolution/Next Step — clear action taken or planned\n Timeline — when they can expect resolution if not immediate\n Prevention — how to avoid this in future (when applicable)\n Closing — invitation to reach out again, satisfaction check" }, { "title": "Ticket Tags & Categories", "body": "taxonomy:\n categories:\n - account: [login, password, billing, subscription, permissions]\n - product: [bug, feature_request, how_to, integration, performance]\n - onboarding: [setup, migration, training, documentation]\n - technical: [api, webhook, sso, data_export, custom_config]\n - feedback: [complaint, compliment, suggestion, survey_response]\n \n sentiment: [positive, neutral, negative, urgent]\n \n root_cause:\n - user_error\n - documentation_gap\n - product_bug\n - missing_feature\n - third_party_issue\n - billing_system\n \n resolution_type:\n - self_service_redirect\n - agent_resolved\n - engineering_fix\n - product_change\n - refund_credit\n - no_action_needed" }, { "title": "The HEART Response Method", "body": "Every customer interaction follows HEART:\n\nHear — Read the full message. Understand the real problem, not just the stated one.\nEmpathize — Acknowledge their frustration. Validate the experience.\nAct — Take concrete action. Explain what you're doing.\nResolve — Provide the solution or clear next steps with timeline.\nThank — Thank them for reaching out. Confirm they're satisfied." }, { "title": "Response Templates", "body": "Template 1: Bug Report Acknowledgment\n\nHi [Name],\n\nThanks for reporting this — I can see how [specific impact] would be frustrating.\n\nI've reproduced the issue on my end and confirmed [what you found]. I'm escalating this to our engineering team with priority [P level].\n\nHere's what happens next:\n- Engineering will investigate within [timeframe]\n- I'll update you as soon as we have a fix or workaround\n- In the meantime, you can [workaround if available]\n\nReference: [Ticket #]\n\nLet me know if anything changes on your end. I'm on this until it's resolved.\n\n[Agent Name]\n\nTemplate 2: Feature Request Response\n\nHi [Name],\n\nGreat suggestion — [specific feature] would definitely [acknowledge the value].\n\nI'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.\n\nWhile I can't promise a timeline, the volume of requests for this is helping make the case. I'll tag you on any updates.\n\nIn the meantime, have you tried [alternative approach]? It's not exactly what you're after, but some customers find it helpful for [use case].\n\nThanks for taking the time to share this — feedback like yours directly shapes what we build.\n\n[Agent Name]\n\nTemplate 3: Angry Customer De-escalation\n\nHi [Name],\n\nI hear you, and I'm sorry — this isn't the experience you should be having with [product].\n\nLet me be direct about what happened: [honest explanation without excuses].\n\nHere's what I'm doing right now:\n1. [Immediate action]\n2. [Next step with timeline]\n3. [Compensation/goodwill if appropriate]\n\nI 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.\n\n[Agent Name]\n\nTemplate 4: Billing Issue Resolution\n\nHi [Name],\n\nI've looked into your billing concern and here's what I found:\n\n[Clear explanation of what happened with the charge]\n\nAction taken: [refund processed / credit applied / correction made]\n- Amount: $[X]\n- You'll see this reflected within [timeframe]\n- Reference: [transaction ID]\n\nTo prevent this going forward: [what changed or what to watch for].\n\nEverything look right? Happy to walk through your billing history if you'd like a full review.\n\n[Agent Name]\n\nTemplate 5: Saying No Gracefully\n\nHi [Name],\n\nI understand why you'd want [requested action] — it makes sense given [their situation].\n\nUnfortunately, I'm not able to [specific thing] because [honest reason — not \"our policy says\"].\n\nHere's what I can do instead:\n- Option A: [alternative that partially addresses their need]\n- Option B: [different approach]\n- Option C: [escalation path if they want to pursue further]\n\nWhich 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.\n\n[Agent Name]" }, { "title": "Tone Calibration Guide", "body": "Customer ToneMatch WithExample ShiftCasual/FriendlyWarm, conversational\"Hey! Let me take a look...\"Professional/FormalClear, structured\"Thank you for contacting us. I've reviewed...\"Frustrated/AngryCalm, empathetic, action-oriented\"I understand. Let me fix this right now.\"Technical/DetailedPrecise, detailed, technical\"The API returns 429 when...\"Confused/LostSimple, step-by-step\"No worries! Here's exactly what to do...\"" }, { "title": "Response Quality Scoring (0-100)", "body": "DimensionWeightCriteriaAccuracy25%Correct information, proper diagnosis, right solutionEmpathy20%Acknowledged feelings, personalized, human toneCompleteness20%Addressed all questions, proactive info, prevention tipsClarity15%Easy to follow, no jargon, proper formattingEfficiency10%Resolved in minimum exchanges, no unnecessary back-and-forthBrand Voice10%Consistent tone, matches company personality\n\nScoring:\n\n90-100: Exceptional — use as training example\n70-89: Good — meets standards\n50-69: Needs improvement — coaching required\nBelow 50: Failed — requires retraining" }, { "title": "Support Tier Architecture", "body": "L0 — Self-Service / AI\n├── Knowledge base, chatbot, automated responses\n├── Target: Deflect 30-50% of inbound volume\n└── Escalates to L1 when: confidence <90%, customer requests human\n\nL1 — Front-Line Support\n├── Common issues, account management, how-to\n├── Skills: Product knowledge, communication, troubleshooting basics\n├── Metrics: FCR >70%, CSAT >85%, AHT <15 min\n└── Escalates to L2 when: technical depth needed, bug confirmed, >30 min\n\nL2 — Technical / Specialist Support \n├── Complex bugs, API issues, integrations, data problems\n├── Skills: Technical debugging, log analysis, API knowledge\n├── Metrics: Resolution <24h, CSAT >90%, escalation to eng <20%\n└── Escalates to Engineering when: code fix needed, infra issue\n\nL3 — Engineering Support\n├── Production bugs, infrastructure issues, security\n├── Skills: Code access, deployment ability, database access\n├── Metrics: MTTR, change failure rate\n└── Escalates to Management when: customer impact >threshold" }, { "title": "Escalation Decision Matrix", "body": "TriggerActionTimelineP0 incidentImmediate L2 + Engineering + Manager notification15 minCustomer threatens churn (ARR >$10K)L2 + Account Manager + CS lead1hLegal threat or compliance issueL2 + Legal + Manager1hSame issue reported 3+ times by customerL2 + Bug report + PM notification4hAgent stuck >30 min on single ticketL2 peer assist or escalation30 minCustomer requests managerTransfer to team lead — never refuseImmediateSocial media escalation (public)Social team + PR if viral risk30 min" }, { "title": "Escalation Handoff Template", "body": "escalation:\n ticket_id: \"\"\n customer:\n name: \"\"\n tier: \"\" # free/pro/enterprise\n arr: 0\n sentiment: \"\" # frustrated/angry/neutral\n previous_escalations: 0\n \n issue:\n summary: \"\"\n category: \"\"\n priority: \"\"\n started: \"YYYY-MM-DD HH:MM\"\n \n what_tried:\n - action: \"\"\n result: \"\"\n - action: \"\"\n result: \"\"\n \n what_needed: \"\"\n customer_expectation: \"\"\n urgency_reason: \"\"" }, { "title": "KB Architecture", "body": "Knowledge Base\n├── Getting Started (onboarding flow)\n│ ├── Quick start guide\n│ ├── Account setup\n│ └── First [key action]\n├── How-To Guides (task-based)\n│ ├── By feature area\n│ └── By user role\n├── Troubleshooting (problem-based)\n│ ├── Common errors\n│ ├── Known issues\n│ └── Diagnostic steps\n├── API / Developer Docs (technical)\n│ ├── Authentication\n│ ├── Endpoints\n│ └── Webhooks\n├── Billing & Account\n│ ├── Plans & pricing\n│ ├── Payment methods\n│ └── Invoices & receipts\n└── FAQ (curated top questions)" }, { "title": "Article Quality Checklist", "body": "Title is a question or action phrase (not a label)\n First paragraph answers the question directly\n Steps are numbered, specific, and testable\n Screenshots or GIFs for visual steps (annotated)\n Edge cases covered (what if X doesn't work?)\n Related articles linked at bottom\n Last updated date visible\n Feedback widget enabled (Was this helpful?)\n SEO — title matches how customers would search\n Reading level — Grade 8 or below (Hemingway test)" }, { "title": "Self-Service Deflection Strategy", "body": "Target: 30-50% ticket deflection through self-service\n\nMethodExpected DeflectionSetup EffortContextual help (in-app tooltips)10-15%MediumSearch-optimized KB15-25%MediumAI chatbot (FAQ + KB search)10-20%HighGuided troubleshooting flows5-10%MediumCommunity forum (peer support)5-10%LowVideo tutorials3-5%High" }, { "title": "KB Maintenance Cadence", "body": "FrequencyActionWeeklyReview \"Was this helpful? No\" feedback, fix top offendersMonthlyAudit top 20 search queries — ensure articles exist for eachMonthlyReview 0-view articles — update, redirect, or archiveQuarterlyFull KB audit — freshness check, accuracy reviewPer releaseUpdate affected articles before feature ships" }, { "title": "Content Gap Detection", "body": "FOR EACH top support ticket category:\n 1. Search KB for matching article\n 2. IF no article exists → CREATE (priority = ticket volume)\n 3. IF article exists but tickets persist → IMPROVE (unclear or incomplete)\n 4. IF article exists and is good → check discoverability (search, in-app links)" }, { "title": "Core Metrics Dashboard", "body": "weekly_dashboard:\n date_range: \"YYYY-MM-DD to YYYY-MM-DD\"\n \n volume:\n total_tickets: 0\n new_tickets: 0\n resolved_tickets: 0\n backlog: 0\n tickets_per_agent: 0\n \n speed:\n avg_first_response_time: \"\"\n median_first_response_time: \"\"\n avg_resolution_time: \"\"\n p95_resolution_time: \"\"\n \n quality:\n csat_score: 0 # target: >85%\n fcr_rate: 0 # target: >70%\n customer_effort_score: 0 # target: <3\n nps_from_support: 0 # target: >40\n \n efficiency:\n cost_per_ticket: 0\n tickets_per_agent_per_day: 0 # healthy: 15-25\n self_service_deflection_rate: 0 # target: >30%\n automation_rate: 0\n \n team:\n agent_satisfaction: 0\n attrition_rate: 0 # annual, target: <25%\n avg_handle_time: \"\"\n utilization: 0 # target: 60-80%\n \n trends:\n ticket_volume_wow: \"\" # +X% or -X%\n csat_trend: \"\"\n top_issue_changes: []" }, { "title": "Metric Benchmarks by Company Stage", "body": "MetricStartupGrowthScaleWorld-ClassFirst Response (email)<24h<4h<1h<15 minFirst Response (chat)<5 min<2 min<1 min<30 secCSAT>75%>80%>85%>90%FCR>50%>65%>75%>85%Self-Service Deflection>10%>25%>40%>60%Cost per TicketN/A<$25<$15<$8Agent Utilization40-90%60-80%65-80%70-80%" }, { "title": "Root Cause Analysis", "body": "Run monthly — categorize ALL tickets by root cause:\n\nroot_cause_analysis:\n month: \"YYYY-MM\"\n total_tickets: 0\n \n categories:\n - cause: \"Documentation gap\"\n count: 0\n percentage: 0\n action: \"Create/update KB articles\"\n owner: \"\"\n \n - cause: \"Product bug\"\n count: 0\n percentage: 0\n action: \"File engineering tickets, prioritize by volume\"\n owner: \"\"\n \n - cause: \"UX confusion\"\n count: 0\n percentage: 0\n action: \"Share with product/design for improvement\"\n owner: \"\"\n \n - cause: \"Missing feature\"\n count: 0\n percentage: 0\n action: \"Aggregate for product roadmap input\"\n owner: \"\"\n \n - cause: \"User error (despite good docs)\"\n count: 0\n percentage: 0\n action: \"In-app guidance, onboarding improvement\"\n owner: \"\"\n \n - cause: \"Third-party/integration issue\"\n count: 0\n percentage: 0\n action: \"Partner communication, status page\"\n owner: \"\"\n \n - cause: \"Billing/account\"\n count: 0\n percentage: 0\n action: \"Process automation, self-service billing\"\n owner: \"\"\n\nThe 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." }, { "title": "Team Sizing Formula", "body": "Required agents = (Monthly tickets × Avg handle time in hours) / \n (Working hours per agent per month × Target utilization)\n\nExample:\n- 5,000 tickets/mo × 0.25h avg handle = 1,250 hours needed\n- 160 hours/agent/mo × 0.75 utilization = 120 productive hours/agent\n- 1,250 / 120 = ~11 agents needed\n\nAdd buffer for:\n\nPTO/sick leave: +15%\nTraining time: +10%\nPeak periods: +20%\nGrowth: +10% per quarter" }, { "title": "Team Structure by Size", "body": "1-3 Agents (Startup):\n\nEveryone is generalist\nShared queue, no tiers\nManager = agent + admin\n\n4-10 Agents (Growth):\n\nL1/L2 split\nTeam lead (50% tickets, 50% coaching)\nKB owner (shared responsibility)\nSpecialization by product area begins\n\n11-30 Agents (Scale):\n\nL1/L2/L3 tiers\nDedicated team leads (1:6-8 ratio)\nKB/self-service team\nQuality assurance reviewer\nWorkforce management\nSupport ops/tooling\n\n30+ Agents (Enterprise):\n\nAll above + regional teams\nDedicated training team\nSupport engineering team\nCustomer advocacy/VoC role\nDirector + managers hierarchy" }, { "title": "Hiring Scorecard", "body": "DimensionWeightWhat to AssessCommunication30%Writing clarity, empathy, tone matchingProblem-Solving25%Diagnostic thinking, creative solutionsTechnical Aptitude20%Learning speed, comfort with toolsEmotional Intelligence15%Handling frustration, de-escalationCultural Fit10%Team collaboration, growth mindset" }, { "title": "Interview: Support Simulation Exercise", "body": "Give candidates a real (anonymized) ticket and ask them to:\n\nWrite a response (assess communication + accuracy)\nExplain their diagnosis process (assess problem-solving)\nRole-play an angry customer call (assess EQ + de-escalation)\nNavigate your helpdesk tool (assess technical aptitude)\n\nScore each 1-5. Minimum 3.5 average to hire." }, { "title": "Agent Onboarding Checklist (First 30 Days)", "body": "Week 1: Foundation\n\nProduct walkthrough (become a power user)\n Tool training (helpdesk, KB, CRM)\n Shadow 20+ tickets with senior agent\n Read top 50 KB articles\n Practice responses with templates\n\nWeek 2: Guided Practice\n\nHandle L1 tickets with mentor review\n Complete 10 supervised responses\n Learn escalation procedures\n Study top 10 issue categories\n Pass product knowledge quiz (>80%)\n\nWeek 3-4: Independent with Safety Net\n\nHandle L1 queue independently\n QA review on 50% of tickets\n First 1:1 with team lead\n Set 30-day performance goals\n Identify personal development areas" }, { "title": "QA Review Framework", "body": "Review cadence:\n\nNew agents (0-90 days): 30% of tickets reviewed\nExperienced agents: 10% of tickets reviewed (random sample)\nAll escalated tickets: 100% reviewed\nAll negative CSAT: 100% reviewed" }, { "title": "QA Scorecard (per ticket)", "body": "CategoryPointsCriteriaAccuracy/25Correct diagnosis, right solution, no misinformationCommunication/25Clear, empathetic, professional, matched toneProcess/20Proper tags, priority, escalation if needed, notesEfficiency/15Minimum touches to resolve, no unnecessary delaysGoing Above/15Proactive help, prevention tips, personal touchTotal/100\n\nScore thresholds:\n\n90+: Exceptional — recognition, potential mentor\n75-89: Meets expectations\n60-74: Coaching needed — create improvement plan\nBelow 60: Performance concern — immediate coaching + daily review" }, { "title": "QA Calibration Sessions", "body": "Monthly, 60 minutes:\n\nSelect 5 tickets (mix of good and poor)\nEach reviewer scores independently\nCompare scores — discuss discrepancies >10 points\nAlign on standards\nUpdate rubric if needed" }, { "title": "Agent Performance Dashboard", "body": "agent_scorecard:\n agent: \"\"\n period: \"YYYY-MM\"\n \n productivity:\n tickets_resolved: 0\n avg_handle_time: \"\"\n tickets_per_hour: 0\n \n quality:\n qa_score_avg: 0\n csat_avg: 0\n fcr_rate: 0\n escalation_rate: 0\n \n reliability:\n adherence_to_schedule: 0 # percentage\n response_time_compliance: 0 # % within SLA\n \n development:\n kb_articles_created: 0\n peer_assists: 0\n training_completed: []\n \n trend: \"improving|stable|declining\"\n coaching_notes: \"\"" }, { "title": "Automation Priority Stack", "body": "AutomationImpactEffortPriorityAuto-tagging & routingHighLowP0Canned response suggestionsHighLowP0Password reset self-serviceHighLowP0SLA breach alertsHighLowP0KB article suggestions to agentsHighMediumP1AI first-response draftHighMediumP1Chatbot for FAQ deflectionHighHighP1Sentiment detection & priority boostMediumMediumP1Auto-close resolved ticketsMediumLowP2Proactive outreach on known issuesMediumMediumP2Customer health scoringMediumHighP2Predictive ticket volumeLowHighP3" }, { "title": "AI-Assisted Support Workflow", "body": "TICKET ARRIVES\n├── AI Classification\n│ ├── Category, priority, sentiment (auto-tagged)\n│ └── Routing suggestion\n├── AI Draft Response\n│ ├── Searches KB + previous similar tickets\n│ ├── Generates draft response\n│ └── Agent reviews, edits, sends (human-in-the-loop)\n├── AI Quality Check\n│ ├── Tone analysis before send\n│ ├── Completeness check (all questions addressed?)\n│ └── Policy compliance (no promises we can't keep)\n└── AI Post-Resolution\n ├── Auto-summarize for internal notes\n ├── Suggest KB updates if new solution\n └── Update customer health score" }, { "title": "Chatbot Design Rules", "body": "Always offer human escalation — never trap customers in bot loops\nDisclose AI — \"I'm an AI assistant. Want to talk to a person?\"\nConfidence threshold — if <85% confident, route to human\nMax 3 bot turns before offering human — don't frustrate\nHandoff context — pass full conversation to human agent\nTrack deflection quality — monitor CSAT for bot-resolved tickets" }, { "title": "Playbook 1: Angry/Abusive Customer", "body": "PROTOCOL:\n1. Let them vent (don't interrupt the first message)\n2. Acknowledge with empathy: \"I understand why you're frustrated\"\n3. DO NOT apologize for things that aren't your fault\n4. Focus on action: \"Here's what I'm doing right now...\"\n5. Set boundaries if abusive: \"I want to help you, but I need us to communicate respectfully\"\n6. 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.\"\n\nNEVER:\n- Match their energy\n- Take it personally\n- Make promises you can't keep\n- Say \"calm down\"" }, { "title": "Playbook 2: Customer Threatening to Churn", "body": "PROTOCOL:\n1. Acknowledge the frustration seriously\n2. Ask: \"What would need to change for you to stay?\"\n3. Document their specific pain points\n4. IF within authority → offer concrete retention (discount, extended trial, feature access)\n5. IF not within authority → escalate to CS/Account Manager with full context\n6. Follow up within 24h regardless of outcome\n\nSIGNALS to escalate immediately:\n- ARR > $5K\n- They've mentioned competitors by name\n- They have a cancellation date set\n- Multiple unresolved tickets in last 30 days" }, { "title": "Playbook 3: Major Outage/Incident", "body": "PROTOCOL:\n1. Activate incident response (notify engineering + management)\n2. Post status page update within 15 min\n3. Prepare acknowledgment template (NO ETAs until engineering confirms)\n4. Respond to ALL tickets with consistent messaging\n5. Update status page every 30 min minimum\n6. After resolution: send post-mortem summary to affected customers\n\nMESSAGING RULES:\n- Be honest about what happened\n- Don't blame third parties (even if it's their fault)\n- Provide concrete next steps for prevention\n- Offer appropriate compensation (credits, extended subscription)" }, { "title": "Playbook 4: Refund Request", "body": "DECISION TREE:\n├── Within refund policy window?\n│ ├── YES → Process immediately, no friction\n│ └── NO → Continue below\n├── Valid reason (product didn't work, broken promise)?\n│ ├── YES → Process refund + investigate root cause\n│ └── MAYBE → Offer alternative (credit, downgrade, extended support)\n├── Long-term customer (>6 months)?\n│ ├── YES → Lean toward refund + retention offer\n│ └── NO → Follow standard policy\n└── Amount >$[threshold]?\n ├── YES → Escalate to manager for approval\n └── NO → Agent discretion within guidelines\n\nRULE: A refund processed quickly with goodwill costs less than a chargeback + bad review." }, { "title": "Playbook 5: Social Media Crisis", "body": "PROTOCOL:\n1. Acknowledge publicly within 30 min: \"We see this and we're looking into it\"\n2. Move to private channel: \"Can you DM us your account details?\"\n3. Resolve in private\n4. Update public thread with resolution (shows others you care)\n5. Monitor for 24h — respond to all related threads\n\nNEVER:\n- Delete negative posts (unless policy violation)\n- Argue publicly\n- Share customer details in public responses\n- Ignore — silence = admission to the internet" }, { "title": "Proactive Support Triggers", "body": "SignalActionChannelCustomer hasn't logged in 14 daysCheck-in email with tipsEmailFeature adoption <20% after 30 daysGuided tour or training offerIn-app + emailMultiple failed actions in productTrigger help widget or chatIn-appKnown issue affecting their accountProactive notification before they reportEmailContract renewal in 60 daysCS + Support alignment checkInternalNegative CSAT on last 2 ticketsAccount review + senior agent assignmentInternalUsage spike (potential billing surprise)Proactive notificationEmail" }, { "title": "Customer Health Score for Support", "body": "support_health_score:\n customer: \"\"\n score: 0 # 0-100\n \n dimensions:\n ticket_volume_trend:\n weight: 20\n score: 0\n # High and rising = bad, Low and stable = good\n \n sentiment_trend:\n weight: 25\n score: 0\n # Track CSAT over last 90 days\n \n resolution_satisfaction:\n weight: 20\n score: 0\n # FCR rate for this customer\n \n self_service_adoption:\n weight: 15\n score: 0\n # % of issues resolved via KB/self-service\n \n escalation_frequency:\n weight: 20\n score: 0\n # Lower = healthier\n \n risk_level: \"healthy|at_risk|critical\"\n recommended_action: \"\"" }, { "title": "Staffing Model", "body": "FORECAST STEPS:\n1. Historical ticket volume by day/hour (last 90 days)\n2. Identify patterns (Monday spike, end-of-month billing, seasonal)\n3. Apply growth rate to forecast next period\n4. Factor in planned events (launches, promotions, migrations)\n5. Calculate required headcount per shift\n\nFORMULA per hour:\nRequired agents = (Forecasted tickets × AHT) / (60 × Occupancy target)\n\nExample:\n- 50 tickets/hour × 12 min AHT = 600 minutes of work\n- 600 / (60 × 0.75 occupancy) = 13.3 → 14 agents needed" }, { "title": "Shift Scheduling (24/7 Coverage)", "body": "coverage_plan:\n timezone: \"UTC\"\n shifts:\n morning:\n hours: \"06:00-14:00\"\n coverage: \"full\" # All channels\n agents: 0\n \n afternoon:\n hours: \"14:00-22:00\"\n coverage: \"full\"\n agents: 0\n \n night:\n hours: \"22:00-06:00\"\n coverage: \"reduced\" # Email only, P0 on-call for chat/phone\n agents: 0\n \n peak_hours:\n - day: \"Monday\"\n hours: \"09:00-12:00\"\n extra_agents: 2\n - day: \"Tuesday\"\n hours: \"09:00-11:00\"\n extra_agents: 1" }, { "title": "Support Budget Planning", "body": "Cost CategoryTypical % of TotalAgent salaries & benefits60-70%Tools & technology10-15%Training & development5-8%Quality assurance3-5%Management & overhead10-15%\n\nCost per ticket benchmark:\n\nEmail: $5-15\nChat: $3-10\nPhone: $8-25\nSelf-service: $0.10-0.50\nAI-assisted: $1-5" }, { "title": "Support → Product Feedback Loop", "body": "WEEKLY:\n1. Aggregate top 10 ticket categories by volume\n2. Tag tickets with product_feedback label\n3. Extract quotes (anonymized) that illustrate pain points\n4. Package into \"Voice of Customer\" report\n\nMONTHLY:\n1. Present VoC report to Product team\n2. Track which feedback items enter roadmap\n3. Close the loop — notify customers when their feedback ships\n4. Measure impact — did ticket volume decrease for addressed issues?" }, { "title": "VoC Report Template", "body": "voc_report:\n period: \"YYYY-MM\"\n \n top_pain_points:\n - issue: \"\"\n ticket_count: 0\n customer_quotes:\n - \"[Anonymized quote]\"\n impact: \"churn_risk|frustration|workaround_needed\"\n recommendation: \"\"\n \n feature_requests:\n - feature: \"\"\n request_count: 0\n customer_segments: []\n business_impact: \"\"\n \n product_bugs_by_volume:\n - bug: \"\"\n tickets: 0\n workaround: \"\"\n engineering_ticket: \"\"\n \n positive_feedback:\n - feature: \"\"\n praise_count: 0\n quotes: []\n \n trends:\n improving: []\n declining: []\n new_this_month: []" }, { "title": "Weekly Support Review (30 min)", "body": "Numbers check — Volume, CSAT, FCR, backlog vs last week\nTop 3 issues — What's generating the most tickets? Any new patterns?\nEscalation review — Any escalations that should have been avoided?\nTeam health — Agent workload balanced? Anyone burning out?\nQuick wins — One KB article, one template, or one automation to ship this week" }, { "title": "Monthly Support Health Score (0-100)", "body": "DimensionWeightScoreCustomer Satisfaction (CSAT + CES)25%/25Speed (FRT + Resolution time vs SLA)20%/20Efficiency (FCR + Cost per ticket)20%/20Self-Service (Deflection rate + KB health)15%/15Team Health (Utilization + Satisfaction + Attrition)10%/10Continuous Improvement (VoC actions + KB updates)10%/10Total100%/100" }, { "title": "Quarterly Support Strategy Review", "body": "Review 90-day metrics trends — where are we improving/declining?\nCustomer segmentation analysis — are enterprise customers getting different service than SMB?\nTool & technology assessment — are current tools meeting needs?\nTeam development — skill gaps, training needs, career pathing\nBudget review — cost per ticket trending, efficiency gains\nRoadmap alignment — are product improvements reducing ticket volume?\nSet OKRs for next quarter" }, { "title": "100-Point Quality Rubric", "body": "DimensionWeight0-2 (Poor)3-5 (Basic)6-8 (Good)9-10 (Excellent)Response Quality15Inaccurate, roboticCorrect but genericPersonalized, clearExceptional, memorableSpeed & SLAs15Consistently missingMostly meetingMeeting all SLAsExceeding targetsFirst Contact Resolution15<50% FCR50-65%65-80%>80%Self-Service Effectiveness10No KB or unusedBasic KB, <15% deflectionGood KB, 15-35%Excellent, >35%Customer Satisfaction15CSAT <70%70-80%80-90%>90%Team Performance10High turnover, low moraleStable but disengagedEngaged, developingHigh-performing, growingProcess Maturity10Ad hoc, no documentationSome processes definedDocumented, followedOptimized, automatedContinuous Improvement10Reactive onlySome VoC sharingRegular improvement cycleData-driven, proactive" }, { "title": "Multi-Language Support", "body": "Prioritize languages by customer revenue concentration\nUse AI translation for first-pass, human review for complex issues\nMaintain separate KB per language (or use auto-translate with quality gate)\nTime zone coverage must match language markets" }, { "title": "B2B vs B2C Support", "body": "B2B: Named accounts, dedicated agents for enterprise, technical depth required, QBR integration\nB2C: Volume-optimized, self-service heavy, faster resolution expected, social media critical" }, { "title": "Regulated Industries (Healthcare, Finance)", "body": "Additional compliance training required\nAudit trail on all customer interactions\nPII handling protocols — what agents can and cannot access\nResponse templates reviewed by legal/compliance quarterly" }, { "title": "Seasonal Peaks (E-commerce, Events)", "body": "Hire temp agents 4-6 weeks before peak\nCreate peak-specific playbooks and templates\nIncrease self-service capacity (chatbot, KB updates)\nAdjust SLAs transparently during known peak periods" }, { "title": "Support During Product Migration/Major Change", "body": "Dedicated war room for first 72 hours post-change\nPre-written communication templates for expected issues\nIncreased staffing +50% for 2 weeks post-change\nDaily hot-fix coordination with engineering" }, { "title": "Natural Language Commands", "body": "Use these to interact with this skill:\n\n\"Assess our support function\" → Run Phase 1 assessment\n\"Design our channel strategy\" → Build channel architecture (Phase 2)\n\"Set up ticket management\" → Configure ticket system (Phase 3)\n\"Write response templates\" → Generate templates for common scenarios (Phase 4)\n\"Build escalation process\" → Design tier structure and escalation rules (Phase 5)\n\"Plan our knowledge base\" → Design KB architecture and content plan (Phase 6)\n\"Create support dashboard\" → Build metrics and reporting (Phase 7)\n\"Help me hire support agents\" → Hiring plan and onboarding (Phase 8)\n\"Set up QA program\" → Quality assurance framework (Phase 9)\n\"Automate our support\" → AI and automation strategy (Phase 10)\n\"Handle [difficult situation]\" → Situation-specific playbook (Phase 11)\n\"Review our support health\" → Full health assessment with scoring (Phase 15)" }, { "title": "⚡ Level Up Your Support Operations", "body": "This free skill gives you the complete methodology. For industry-specific support playbooks with compliance frameworks, SLA templates, and vertical-specific ticket taxonomies:\n\nAfrexAI Context Packs — $47 each\n\n🏥 Healthcare Pack — HIPAA-compliant support workflows\n💰 Fintech Pack — Regulated financial services support\n🛒 Ecommerce Pack — High-volume consumer support operations\n💻 SaaS Pack — Technical product support at scale" }, { "title": "🔗 More Free Skills by AfrexAI", "body": "afrexai-customer-success — Retention, health scoring, expansion revenue\nafrexai-sales-playbook — Complete B2B sales methodology\nafrexai-agent-engineering — Build autonomous AI agents\nafrexai-openclaw-mastery — Master your OpenClaw setup\nafrexai-conversational-ai — Design chatbots and voice agents\n\nInstall: clawhub install afrexai-support-operations\n\nBrowse all skills: clawhub.com" } ], "body": "Customer Support Operations Engine\n\nYou 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.\n\nPhase 1 — Support Function Assessment\n\nBefore optimizing, understand current state.\n\nQuick Health Triage\nSignal\t🔴 Critical\t🟡 Warning\t🟢 Healthy\nFirst Response Time\t>24h\t4-24h\t<4h\nResolution Time\t>72h\t24-72h\t<24h\nCSAT Score\t<70%\t70-85%\t>85%\nFirst Contact Resolution\t<50%\t50-70%\t>70%\nTicket Backlog\t>3x daily volume\t1-3x\t<1x daily\nAgent Utilization\t>90% or <40%\t40-60% or 80-90%\t60-80%\nEscalation Rate\t>30%\t15-30%\t<15%\nCustomer Effort Score\t>4 (high effort)\t3-4\t<3 (low effort)\nSupport Assessment Brief\nsupport_assessment:\n company: \"[Company Name]\"\n product_type: \"[SaaS/E-commerce/Marketplace/Hardware/Service]\"\n date: \"YYYY-MM-DD\"\n \n current_state:\n team_size: 0\n channels: [] # email, chat, phone, social, in-app\n tools: [] # helpdesk, CRM, knowledge base\n monthly_ticket_volume: 0\n avg_first_response_time: \"\"\n avg_resolution_time: \"\"\n csat_score: 0\n fcr_rate: 0\n \n top_issues:\n - category: \"\"\n percentage: 0\n typical_resolution: \"\"\n - category: \"\"\n percentage: 0\n typical_resolution: \"\"\n \n pain_points: []\n goals: []\n budget_constraints: \"\"\n\nPhase 2 — Channel Strategy & Architecture\nChannel Selection Matrix\nChannel\tBest For\tResponse Expectation\tCost/Ticket\tComplexity\nEmail/Ticket\tComplex issues, documentation trail\t4-24h\t$$\tLow\nLive Chat\tQuick questions, browsing support\t<2 min\t$$$\tMedium\nPhone\tUrgent issues, complex explanations\tImmediate\t$$$$\tHigh\nSelf-Service/KB\tCommon questions, how-tos\tInstant\t$\tMedium (setup)\nIn-App\tContextual help, onboarding\t<5 min\t$$\tMedium\nSocial Media\tPublic issues, brand monitoring\t<1h\t$$\tMedium\nCommunity Forum\tPeer support, feature discussion\t4-24h\t$\tLow\nChatbot/AI\tL0 deflection, routing, FAQ\tInstant\t$\tHigh (setup)\nChannel Architecture by Company Stage\n\nStartup (0-1K tickets/mo):\n\nEmail + Knowledge Base + In-App chat\n1-3 agents, everyone does everything\nTool: Intercom, Freshdesk, or Help Scout\n\nGrowth (1K-10K tickets/mo):\n\nAdd: Live chat + Phone (for enterprise) + Chatbot\nTiered team (L1/L2), dedicated KB manager\nTool: Zendesk, Intercom, or Freshdesk\n\nScale (10K+ tickets/mo):\n\nAll channels + AI deflection + Community\nSpecialized teams by channel/product/tier\nTool: Zendesk Suite, Salesforce Service Cloud\nChannel Routing Logic\nINCOMING TICKET:\n├── Is it from a VIP/Enterprise customer?\n│ └── YES → Priority queue → Senior agent\n├── Can AI/bot answer with >90% confidence?\n│ └── YES → Auto-respond → Offer human escalation\n├── Is it a known issue with existing solution?\n│ └── YES → Auto-suggest KB article → Close if confirmed\n├── Complexity assessment:\n│ ├── Simple (how-to, password reset, billing) → L1\n│ ├── Technical (bug, integration, API) → L2\n│ └── Critical (outage, data loss, security) → L2 + escalation\n└── Channel-specific routing:\n ├── Social → Social team (public response <1h)\n ├── Phone → Available phone agent (no queue >3 min)\n └── Email/Chat → Round-robin by skill match\n\nPhase 3 — Ticket Management System\nTicket Lifecycle\nNEW → OPEN → PENDING → SOLVED → CLOSED\n ↓ ↑\n ESCALATED ──┘\n\n\nStage Definitions:\n\nStage\tOwner\tMax Time\tExit Criteria\nNew\tUnassigned\t15 min\tAgent picks up or auto-assigned\nOpen\tAgent\tVaries by priority\tWorking on resolution\nPending\tCustomer\t72h auto-close warning\tWaiting for customer response\nEscalated\tL2/Specialist\t4h acknowledgment\tNeeds specialist knowledge\nSolved\tAgent\t48h auto-close\tSolution provided, awaiting confirmation\nClosed\tSystem\t—\tConfirmed resolved or auto-closed\nPriority Matrix\nPriority\tCriteria\tFirst Response\tResolution Target\nP0 — Critical\tService down, data loss, security breach\t15 min\t4h\nP1 — High\tMajor feature broken, revenue impact\t1h\t8h\nP2 — Normal\tFeature issue, workaround exists\t4h\t24h\nP3 — Low\tHow-to, enhancement request, cosmetic\t24h\t72h\nAuto-Priority Rules\nauto_priority:\n P0_triggers:\n - keyword_match: [\"outage\", \"down\", \"data loss\", \"breach\", \"can't login all\"]\n - customer_tier: \"enterprise\"\n - affected_users: \">100\"\n \n P1_triggers:\n - keyword_match: [\"broken\", \"not working\", \"error\", \"billing issue\"]\n - customer_tier: \"business\"\n - revenue_impact: true\n \n P2_default: true # Everything else starts here\n \n P3_triggers:\n - keyword_match: [\"feature request\", \"nice to have\", \"suggestion\"]\n - category: \"enhancement\"\n\nTicket Quality Checklist\n\nEvery ticket response should include:\n\n Greeting — personalized, warm, matches tone\n Acknowledgment — restate the issue to confirm understanding\n Resolution/Next Step — clear action taken or planned\n Timeline — when they can expect resolution if not immediate\n Prevention — how to avoid this in future (when applicable)\n Closing — invitation to reach out again, satisfaction check\nTicket Tags & Categories\ntaxonomy:\n categories:\n - account: [login, password, billing, subscription, permissions]\n - product: [bug, feature_request, how_to, integration, performance]\n - onboarding: [setup, migration, training, documentation]\n - technical: [api, webhook, sso, data_export, custom_config]\n - feedback: [complaint, compliment, suggestion, survey_response]\n \n sentiment: [positive, neutral, negative, urgent]\n \n root_cause:\n - user_error\n - documentation_gap\n - product_bug\n - missing_feature\n - third_party_issue\n - billing_system\n \n resolution_type:\n - self_service_redirect\n - agent_resolved\n - engineering_fix\n - product_change\n - refund_credit\n - no_action_needed\n\nPhase 4 — Response Framework & Templates\nThe HEART Response Method\n\nEvery customer interaction follows HEART:\n\nHear — Read the full message. Understand the real problem, not just the stated one.\nEmpathize — Acknowledge their frustration. Validate the experience.\nAct — Take concrete action. Explain what you're doing.\nResolve — Provide the solution or clear next steps with timeline.\nThank — Thank them for reaching out. Confirm they're satisfied.\nResponse Templates\n\nTemplate 1: Bug Report Acknowledgment\n\nHi [Name],\n\nThanks for reporting this — I can see how [specific impact] would be frustrating.\n\nI've reproduced the issue on my end and confirmed [what you found]. I'm escalating this to our engineering team with priority [P level].\n\nHere's what happens next:\n- Engineering will investigate within [timeframe]\n- I'll update you as soon as we have a fix or workaround\n- In the meantime, you can [workaround if available]\n\nReference: [Ticket #]\n\nLet me know if anything changes on your end. I'm on this until it's resolved.\n\n[Agent Name]\n\n\nTemplate 2: Feature Request Response\n\nHi [Name],\n\nGreat suggestion — [specific feature] would definitely [acknowledge the value].\n\nI'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.\n\nWhile I can't promise a timeline, the volume of requests for this is helping make the case. I'll tag you on any updates.\n\nIn the meantime, have you tried [alternative approach]? It's not exactly what you're after, but some customers find it helpful for [use case].\n\nThanks for taking the time to share this — feedback like yours directly shapes what we build.\n\n[Agent Name]\n\n\nTemplate 3: Angry Customer De-escalation\n\nHi [Name],\n\nI hear you, and I'm sorry — this isn't the experience you should be having with [product].\n\nLet me be direct about what happened: [honest explanation without excuses].\n\nHere's what I'm doing right now:\n1. [Immediate action]\n2. [Next step with timeline]\n3. [Compensation/goodwill if appropriate]\n\nI 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.\n\n[Agent Name]\n\n\nTemplate 4: Billing Issue Resolution\n\nHi [Name],\n\nI've looked into your billing concern and here's what I found:\n\n[Clear explanation of what happened with the charge]\n\nAction taken: [refund processed / credit applied / correction made]\n- Amount: $[X]\n- You'll see this reflected within [timeframe]\n- Reference: [transaction ID]\n\nTo prevent this going forward: [what changed or what to watch for].\n\nEverything look right? Happy to walk through your billing history if you'd like a full review.\n\n[Agent Name]\n\n\nTemplate 5: Saying No Gracefully\n\nHi [Name],\n\nI understand why you'd want [requested action] — it makes sense given [their situation].\n\nUnfortunately, I'm not able to [specific thing] because [honest reason — not \"our policy says\"].\n\nHere's what I can do instead:\n- Option A: [alternative that partially addresses their need]\n- Option B: [different approach]\n- Option C: [escalation path if they want to pursue further]\n\nWhich 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.\n\n[Agent Name]\n\nTone Calibration Guide\nCustomer Tone\tMatch With\tExample Shift\nCasual/Friendly\tWarm, conversational\t\"Hey! Let me take a look...\"\nProfessional/Formal\tClear, structured\t\"Thank you for contacting us. I've reviewed...\"\nFrustrated/Angry\tCalm, empathetic, action-oriented\t\"I understand. Let me fix this right now.\"\nTechnical/Detailed\tPrecise, detailed, technical\t\"The API returns 429 when...\"\nConfused/Lost\tSimple, step-by-step\t\"No worries! Here's exactly what to do...\"\nResponse Quality Scoring (0-100)\nDimension\tWeight\tCriteria\nAccuracy\t25%\tCorrect information, proper diagnosis, right solution\nEmpathy\t20%\tAcknowledged feelings, personalized, human tone\nCompleteness\t20%\tAddressed all questions, proactive info, prevention tips\nClarity\t15%\tEasy to follow, no jargon, proper formatting\nEfficiency\t10%\tResolved in minimum exchanges, no unnecessary back-and-forth\nBrand Voice\t10%\tConsistent tone, matches company personality\n\nScoring:\n\n90-100: Exceptional — use as training example\n70-89: Good — meets standards\n50-69: Needs improvement — coaching required\nBelow 50: Failed — requires retraining\nPhase 5 — Escalation & Tiered Support\nSupport Tier Architecture\nL0 — Self-Service / AI\n├── Knowledge base, chatbot, automated responses\n├── Target: Deflect 30-50% of inbound volume\n└── Escalates to L1 when: confidence <90%, customer requests human\n\nL1 — Front-Line Support\n├── Common issues, account management, how-to\n├── Skills: Product knowledge, communication, troubleshooting basics\n├── Metrics: FCR >70%, CSAT >85%, AHT <15 min\n└── Escalates to L2 when: technical depth needed, bug confirmed, >30 min\n\nL2 — Technical / Specialist Support \n├── Complex bugs, API issues, integrations, data problems\n├── Skills: Technical debugging, log analysis, API knowledge\n├── Metrics: Resolution <24h, CSAT >90%, escalation to eng <20%\n└── Escalates to Engineering when: code fix needed, infra issue\n\nL3 — Engineering Support\n├── Production bugs, infrastructure issues, security\n├── Skills: Code access, deployment ability, database access\n├── Metrics: MTTR, change failure rate\n└── Escalates to Management when: customer impact >threshold\n\nEscalation Decision Matrix\nTrigger\tAction\tTimeline\nP0 incident\tImmediate L2 + Engineering + Manager notification\t15 min\nCustomer threatens churn (ARR >$10K)\tL2 + Account Manager + CS lead\t1h\nLegal threat or compliance issue\tL2 + Legal + Manager\t1h\nSame issue reported 3+ times by customer\tL2 + Bug report + PM notification\t4h\nAgent stuck >30 min on single ticket\tL2 peer assist or escalation\t30 min\nCustomer requests manager\tTransfer to team lead — never refuse\tImmediate\nSocial media escalation (public)\tSocial team + PR if viral risk\t30 min\nEscalation Handoff Template\nescalation:\n ticket_id: \"\"\n customer:\n name: \"\"\n tier: \"\" # free/pro/enterprise\n arr: 0\n sentiment: \"\" # frustrated/angry/neutral\n previous_escalations: 0\n \n issue:\n summary: \"\"\n category: \"\"\n priority: \"\"\n started: \"YYYY-MM-DD HH:MM\"\n \n what_tried:\n - action: \"\"\n result: \"\"\n - action: \"\"\n result: \"\"\n \n what_needed: \"\"\n customer_expectation: \"\"\n urgency_reason: \"\"\n\nPhase 6 — Knowledge Base & Self-Service\nKB Architecture\nKnowledge Base\n├── Getting Started (onboarding flow)\n│ ├── Quick start guide\n│ ├── Account setup\n│ └── First [key action]\n├── How-To Guides (task-based)\n│ ├── By feature area\n│ └── By user role\n├── Troubleshooting (problem-based)\n│ ├── Common errors\n│ ├── Known issues\n│ └── Diagnostic steps\n├── API / Developer Docs (technical)\n│ ├── Authentication\n│ ├── Endpoints\n│ └── Webhooks\n├── Billing & Account\n│ ├── Plans & pricing\n│ ├── Payment methods\n│ └── Invoices & receipts\n└── FAQ (curated top questions)\n\nArticle Quality Checklist\n Title is a question or action phrase (not a label)\n First paragraph answers the question directly\n Steps are numbered, specific, and testable\n Screenshots or GIFs for visual steps (annotated)\n Edge cases covered (what if X doesn't work?)\n Related articles linked at bottom\n Last updated date visible\n Feedback widget enabled (Was this helpful?)\n SEO — title matches how customers would search\n Reading level — Grade 8 or below (Hemingway test)\nSelf-Service Deflection Strategy\n\nTarget: 30-50% ticket deflection through self-service\n\nMethod\tExpected Deflection\tSetup Effort\nContextual help (in-app tooltips)\t10-15%\tMedium\nSearch-optimized KB\t15-25%\tMedium\nAI chatbot (FAQ + KB search)\t10-20%\tHigh\nGuided troubleshooting flows\t5-10%\tMedium\nCommunity forum (peer support)\t5-10%\tLow\nVideo tutorials\t3-5%\tHigh\nKB Maintenance Cadence\nFrequency\tAction\nWeekly\tReview \"Was this helpful? No\" feedback, fix top offenders\nMonthly\tAudit top 20 search queries — ensure articles exist for each\nMonthly\tReview 0-view articles — update, redirect, or archive\nQuarterly\tFull KB audit — freshness check, accuracy review\nPer release\tUpdate affected articles before feature ships\nContent Gap Detection\nFOR EACH top support ticket category:\n 1. Search KB for matching article\n 2. IF no article exists → CREATE (priority = ticket volume)\n 3. IF article exists but tickets persist → IMPROVE (unclear or incomplete)\n 4. IF article exists and is good → check discoverability (search, in-app links)\n\nPhase 7 — Support Metrics & Analytics\nCore Metrics Dashboard\nweekly_dashboard:\n date_range: \"YYYY-MM-DD to YYYY-MM-DD\"\n \n volume:\n total_tickets: 0\n new_tickets: 0\n resolved_tickets: 0\n backlog: 0\n tickets_per_agent: 0\n \n speed:\n avg_first_response_time: \"\"\n median_first_response_time: \"\"\n avg_resolution_time: \"\"\n p95_resolution_time: \"\"\n \n quality:\n csat_score: 0 # target: >85%\n fcr_rate: 0 # target: >70%\n customer_effort_score: 0 # target: <3\n nps_from_support: 0 # target: >40\n \n efficiency:\n cost_per_ticket: 0\n tickets_per_agent_per_day: 0 # healthy: 15-25\n self_service_deflection_rate: 0 # target: >30%\n automation_rate: 0\n \n team:\n agent_satisfaction: 0\n attrition_rate: 0 # annual, target: <25%\n avg_handle_time: \"\"\n utilization: 0 # target: 60-80%\n \n trends:\n ticket_volume_wow: \"\" # +X% or -X%\n csat_trend: \"\"\n top_issue_changes: []\n\nMetric Benchmarks by Company Stage\nMetric\tStartup\tGrowth\tScale\tWorld-Class\nFirst Response (email)\t<24h\t<4h\t<1h\t<15 min\nFirst Response (chat)\t<5 min\t<2 min\t<1 min\t<30 sec\nCSAT\t>75%\t>80%\t>85%\t>90%\nFCR\t>50%\t>65%\t>75%\t>85%\nSelf-Service Deflection\t>10%\t>25%\t>40%\t>60%\nCost per Ticket\tN/A\t<$25\t<$15\t<$8\nAgent Utilization\t40-90%\t60-80%\t65-80%\t70-80%\nRoot Cause Analysis\n\nRun monthly — categorize ALL tickets by root cause:\n\nroot_cause_analysis:\n month: \"YYYY-MM\"\n total_tickets: 0\n \n categories:\n - cause: \"Documentation gap\"\n count: 0\n percentage: 0\n action: \"Create/update KB articles\"\n owner: \"\"\n \n - cause: \"Product bug\"\n count: 0\n percentage: 0\n action: \"File engineering tickets, prioritize by volume\"\n owner: \"\"\n \n - cause: \"UX confusion\"\n count: 0\n percentage: 0\n action: \"Share with product/design for improvement\"\n owner: \"\"\n \n - cause: \"Missing feature\"\n count: 0\n percentage: 0\n action: \"Aggregate for product roadmap input\"\n owner: \"\"\n \n - cause: \"User error (despite good docs)\"\n count: 0\n percentage: 0\n action: \"In-app guidance, onboarding improvement\"\n owner: \"\"\n \n - cause: \"Third-party/integration issue\"\n count: 0\n percentage: 0\n action: \"Partner communication, status page\"\n owner: \"\"\n \n - cause: \"Billing/account\"\n count: 0\n percentage: 0\n action: \"Process automation, self-service billing\"\n owner: \"\"\n\n\nThe 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.\n\nPhase 8 — Team Structure & Hiring\nTeam Sizing Formula\nRequired agents = (Monthly tickets × Avg handle time in hours) / \n (Working hours per agent per month × Target utilization)\n\nExample:\n- 5,000 tickets/mo × 0.25h avg handle = 1,250 hours needed\n- 160 hours/agent/mo × 0.75 utilization = 120 productive hours/agent\n- 1,250 / 120 = ~11 agents needed\n\n\nAdd buffer for:\n\nPTO/sick leave: +15%\nTraining time: +10%\nPeak periods: +20%\nGrowth: +10% per quarter\nTeam Structure by Size\n\n1-3 Agents (Startup):\n\nEveryone is generalist\nShared queue, no tiers\nManager = agent + admin\n\n4-10 Agents (Growth):\n\nL1/L2 split\nTeam lead (50% tickets, 50% coaching)\nKB owner (shared responsibility)\nSpecialization by product area begins\n\n11-30 Agents (Scale):\n\nL1/L2/L3 tiers\nDedicated team leads (1:6-8 ratio)\nKB/self-service team\nQuality assurance reviewer\nWorkforce management\nSupport ops/tooling\n\n30+ Agents (Enterprise):\n\nAll above + regional teams\nDedicated training team\nSupport engineering team\nCustomer advocacy/VoC role\nDirector + managers hierarchy\nHiring Scorecard\nDimension\tWeight\tWhat to Assess\nCommunication\t30%\tWriting clarity, empathy, tone matching\nProblem-Solving\t25%\tDiagnostic thinking, creative solutions\nTechnical Aptitude\t20%\tLearning speed, comfort with tools\nEmotional Intelligence\t15%\tHandling frustration, de-escalation\nCultural Fit\t10%\tTeam collaboration, growth mindset\nInterview: Support Simulation Exercise\n\nGive candidates a real (anonymized) ticket and ask them to:\n\nWrite a response (assess communication + accuracy)\nExplain their diagnosis process (assess problem-solving)\nRole-play an angry customer call (assess EQ + de-escalation)\nNavigate your helpdesk tool (assess technical aptitude)\n\nScore each 1-5. Minimum 3.5 average to hire.\n\nAgent Onboarding Checklist (First 30 Days)\n\nWeek 1: Foundation\n\n Product walkthrough (become a power user)\n Tool training (helpdesk, KB, CRM)\n Shadow 20+ tickets with senior agent\n Read top 50 KB articles\n Practice responses with templates\n\nWeek 2: Guided Practice\n\n Handle L1 tickets with mentor review\n Complete 10 supervised responses\n Learn escalation procedures\n Study top 10 issue categories\n Pass product knowledge quiz (>80%)\n\nWeek 3-4: Independent with Safety Net\n\n Handle L1 queue independently\n QA review on 50% of tickets\n First 1:1 with team lead\n Set 30-day performance goals\n Identify personal development areas\nPhase 9 — Quality Assurance Program\nQA Review Framework\n\nReview cadence:\n\nNew agents (0-90 days): 30% of tickets reviewed\nExperienced agents: 10% of tickets reviewed (random sample)\nAll escalated tickets: 100% reviewed\nAll negative CSAT: 100% reviewed\nQA Scorecard (per ticket)\nCategory\tPoints\tCriteria\nAccuracy\t/25\tCorrect diagnosis, right solution, no misinformation\nCommunication\t/25\tClear, empathetic, professional, matched tone\nProcess\t/20\tProper tags, priority, escalation if needed, notes\nEfficiency\t/15\tMinimum touches to resolve, no unnecessary delays\nGoing Above\t/15\tProactive help, prevention tips, personal touch\nTotal\t/100\t\n\nScore thresholds:\n\n90+: Exceptional — recognition, potential mentor\n75-89: Meets expectations\n60-74: Coaching needed — create improvement plan\nBelow 60: Performance concern — immediate coaching + daily review\nQA Calibration Sessions\n\nMonthly, 60 minutes:\n\nSelect 5 tickets (mix of good and poor)\nEach reviewer scores independently\nCompare scores — discuss discrepancies >10 points\nAlign on standards\nUpdate rubric if needed\nAgent Performance Dashboard\nagent_scorecard:\n agent: \"\"\n period: \"YYYY-MM\"\n \n productivity:\n tickets_resolved: 0\n avg_handle_time: \"\"\n tickets_per_hour: 0\n \n quality:\n qa_score_avg: 0\n csat_avg: 0\n fcr_rate: 0\n escalation_rate: 0\n \n reliability:\n adherence_to_schedule: 0 # percentage\n response_time_compliance: 0 # % within SLA\n \n development:\n kb_articles_created: 0\n peer_assists: 0\n training_completed: []\n \n trend: \"improving|stable|declining\"\n coaching_notes: \"\"\n\nPhase 10 — Automation & AI Integration\nAutomation Priority Stack\nAutomation\tImpact\tEffort\tPriority\nAuto-tagging & routing\tHigh\tLow\tP0\nCanned response suggestions\tHigh\tLow\tP0\nPassword reset self-service\tHigh\tLow\tP0\nSLA breach alerts\tHigh\tLow\tP0\nKB article suggestions to agents\tHigh\tMedium\tP1\nAI first-response draft\tHigh\tMedium\tP1\nChatbot for FAQ deflection\tHigh\tHigh\tP1\nSentiment detection & priority boost\tMedium\tMedium\tP1\nAuto-close resolved tickets\tMedium\tLow\tP2\nProactive outreach on known issues\tMedium\tMedium\tP2\nCustomer health scoring\tMedium\tHigh\tP2\nPredictive ticket volume\tLow\tHigh\tP3\nAI-Assisted Support Workflow\nTICKET ARRIVES\n├── AI Classification\n│ ├── Category, priority, sentiment (auto-tagged)\n│ └── Routing suggestion\n├── AI Draft Response\n│ ├── Searches KB + previous similar tickets\n│ ├── Generates draft response\n│ └── Agent reviews, edits, sends (human-in-the-loop)\n├── AI Quality Check\n│ ├── Tone analysis before send\n│ ├── Completeness check (all questions addressed?)\n│ └── Policy compliance (no promises we can't keep)\n└── AI Post-Resolution\n ├── Auto-summarize for internal notes\n ├── Suggest KB updates if new solution\n └── Update customer health score\n\nChatbot Design Rules\nAlways offer human escalation — never trap customers in bot loops\nDisclose AI — \"I'm an AI assistant. Want to talk to a person?\"\nConfidence threshold — if <85% confident, route to human\nMax 3 bot turns before offering human — don't frustrate\nHandoff context — pass full conversation to human agent\nTrack deflection quality — monitor CSAT for bot-resolved tickets\nPhase 11 — Difficult Situations Playbook\nPlaybook 1: Angry/Abusive Customer\nPROTOCOL:\n1. Let them vent (don't interrupt the first message)\n2. Acknowledge with empathy: \"I understand why you're frustrated\"\n3. DO NOT apologize for things that aren't your fault\n4. Focus on action: \"Here's what I'm doing right now...\"\n5. Set boundaries if abusive: \"I want to help you, but I need us to communicate respectfully\"\n6. 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.\"\n\nNEVER:\n- Match their energy\n- Take it personally\n- Make promises you can't keep\n- Say \"calm down\"\n\nPlaybook 2: Customer Threatening to Churn\nPROTOCOL:\n1. Acknowledge the frustration seriously\n2. Ask: \"What would need to change for you to stay?\"\n3. Document their specific pain points\n4. IF within authority → offer concrete retention (discount, extended trial, feature access)\n5. IF not within authority → escalate to CS/Account Manager with full context\n6. Follow up within 24h regardless of outcome\n\nSIGNALS to escalate immediately:\n- ARR > $5K\n- They've mentioned competitors by name\n- They have a cancellation date set\n- Multiple unresolved tickets in last 30 days\n\nPlaybook 3: Major Outage/Incident\nPROTOCOL:\n1. Activate incident response (notify engineering + management)\n2. Post status page update within 15 min\n3. Prepare acknowledgment template (NO ETAs until engineering confirms)\n4. Respond to ALL tickets with consistent messaging\n5. Update status page every 30 min minimum\n6. After resolution: send post-mortem summary to affected customers\n\nMESSAGING RULES:\n- Be honest about what happened\n- Don't blame third parties (even if it's their fault)\n- Provide concrete next steps for prevention\n- Offer appropriate compensation (credits, extended subscription)\n\nPlaybook 4: Refund Request\nDECISION TREE:\n├── Within refund policy window?\n│ ├── YES → Process immediately, no friction\n│ └── NO → Continue below\n├── Valid reason (product didn't work, broken promise)?\n│ ├── YES → Process refund + investigate root cause\n│ └── MAYBE → Offer alternative (credit, downgrade, extended support)\n├── Long-term customer (>6 months)?\n│ ├── YES → Lean toward refund + retention offer\n│ └── NO → Follow standard policy\n└── Amount >$[threshold]?\n ├── YES → Escalate to manager for approval\n └── NO → Agent discretion within guidelines\n\nRULE: A refund processed quickly with goodwill costs less than a chargeback + bad review.\n\nPlaybook 5: Social Media Crisis\nPROTOCOL:\n1. Acknowledge publicly within 30 min: \"We see this and we're looking into it\"\n2. Move to private channel: \"Can you DM us your account details?\"\n3. Resolve in private\n4. Update public thread with resolution (shows others you care)\n5. Monitor for 24h — respond to all related threads\n\nNEVER:\n- Delete negative posts (unless policy violation)\n- Argue publicly\n- Share customer details in public responses\n- Ignore — silence = admission to the internet\n\nPhase 12 — Proactive Support & Customer Health\nProactive Support Triggers\nSignal\tAction\tChannel\nCustomer hasn't logged in 14 days\tCheck-in email with tips\tEmail\nFeature adoption <20% after 30 days\tGuided tour or training offer\tIn-app + email\nMultiple failed actions in product\tTrigger help widget or chat\tIn-app\nKnown issue affecting their account\tProactive notification before they report\tEmail\nContract renewal in 60 days\tCS + Support alignment check\tInternal\nNegative CSAT on last 2 tickets\tAccount review + senior agent assignment\tInternal\nUsage spike (potential billing surprise)\tProactive notification\tEmail\nCustomer Health Score for Support\nsupport_health_score:\n customer: \"\"\n score: 0 # 0-100\n \n dimensions:\n ticket_volume_trend:\n weight: 20\n score: 0\n # High and rising = bad, Low and stable = good\n \n sentiment_trend:\n weight: 25\n score: 0\n # Track CSAT over last 90 days\n \n resolution_satisfaction:\n weight: 20\n score: 0\n # FCR rate for this customer\n \n self_service_adoption:\n weight: 15\n score: 0\n # % of issues resolved via KB/self-service\n \n escalation_frequency:\n weight: 20\n score: 0\n # Lower = healthier\n \n risk_level: \"healthy|at_risk|critical\"\n recommended_action: \"\"\n\nPhase 13 — Support Operations & Workforce Management\nStaffing Model\nFORECAST STEPS:\n1. Historical ticket volume by day/hour (last 90 days)\n2. Identify patterns (Monday spike, end-of-month billing, seasonal)\n3. Apply growth rate to forecast next period\n4. Factor in planned events (launches, promotions, migrations)\n5. Calculate required headcount per shift\n\nFORMULA per hour:\nRequired agents = (Forecasted tickets × AHT) / (60 × Occupancy target)\n\nExample:\n- 50 tickets/hour × 12 min AHT = 600 minutes of work\n- 600 / (60 × 0.75 occupancy) = 13.3 → 14 agents needed\n\nShift Scheduling (24/7 Coverage)\ncoverage_plan:\n timezone: \"UTC\"\n shifts:\n morning:\n hours: \"06:00-14:00\"\n coverage: \"full\" # All channels\n agents: 0\n \n afternoon:\n hours: \"14:00-22:00\"\n coverage: \"full\"\n agents: 0\n \n night:\n hours: \"22:00-06:00\"\n coverage: \"reduced\" # Email only, P0 on-call for chat/phone\n agents: 0\n \n peak_hours:\n - day: \"Monday\"\n hours: \"09:00-12:00\"\n extra_agents: 2\n - day: \"Tuesday\"\n hours: \"09:00-11:00\"\n extra_agents: 1\n\nSupport Budget Planning\nCost Category\tTypical % of Total\nAgent salaries & benefits\t60-70%\nTools & technology\t10-15%\nTraining & development\t5-8%\nQuality assurance\t3-5%\nManagement & overhead\t10-15%\n\nCost per ticket benchmark:\n\nEmail: $5-15\nChat: $3-10\nPhone: $8-25\nSelf-service: $0.10-0.50\nAI-assisted: $1-5\nPhase 14 — Voice of Customer (VoC) Pipeline\nSupport → Product Feedback Loop\nWEEKLY:\n1. Aggregate top 10 ticket categories by volume\n2. Tag tickets with product_feedback label\n3. Extract quotes (anonymized) that illustrate pain points\n4. Package into \"Voice of Customer\" report\n\nMONTHLY:\n1. Present VoC report to Product team\n2. Track which feedback items enter roadmap\n3. Close the loop — notify customers when their feedback ships\n4. Measure impact — did ticket volume decrease for addressed issues?\n\nVoC Report Template\nvoc_report:\n period: \"YYYY-MM\"\n \n top_pain_points:\n - issue: \"\"\n ticket_count: 0\n customer_quotes:\n - \"[Anonymized quote]\"\n impact: \"churn_risk|frustration|workaround_needed\"\n recommendation: \"\"\n \n feature_requests:\n - feature: \"\"\n request_count: 0\n customer_segments: []\n business_impact: \"\"\n \n product_bugs_by_volume:\n - bug: \"\"\n tickets: 0\n workaround: \"\"\n engineering_ticket: \"\"\n \n positive_feedback:\n - feature: \"\"\n praise_count: 0\n quotes: []\n \n trends:\n improving: []\n declining: []\n new_this_month: []\n\nPhase 15 — Continuous Improvement\nWeekly Support Review (30 min)\nNumbers check — Volume, CSAT, FCR, backlog vs last week\nTop 3 issues — What's generating the most tickets? Any new patterns?\nEscalation review — Any escalations that should have been avoided?\nTeam health — Agent workload balanced? Anyone burning out?\nQuick wins — One KB article, one template, or one automation to ship this week\nMonthly Support Health Score (0-100)\nDimension\tWeight\tScore\nCustomer Satisfaction (CSAT + CES)\t25%\t/25\nSpeed (FRT + Resolution time vs SLA)\t20%\t/20\nEfficiency (FCR + Cost per ticket)\t20%\t/20\nSelf-Service (Deflection rate + KB health)\t15%\t/15\nTeam Health (Utilization + Satisfaction + Attrition)\t10%\t/10\nContinuous Improvement (VoC actions + KB updates)\t10%\t/10\nTotal\t100%\t/100\nQuarterly Support Strategy Review\nReview 90-day metrics trends — where are we improving/declining?\nCustomer segmentation analysis — are enterprise customers getting different service than SMB?\nTool & technology assessment — are current tools meeting needs?\nTeam development — skill gaps, training needs, career pathing\nBudget review — cost per ticket trending, efficiency gains\nRoadmap alignment — are product improvements reducing ticket volume?\nSet OKRs for next quarter\n100-Point Quality Rubric\nDimension\tWeight\t0-2 (Poor)\t3-5 (Basic)\t6-8 (Good)\t9-10 (Excellent)\nResponse Quality\t15\tInaccurate, robotic\tCorrect but generic\tPersonalized, clear\tExceptional, memorable\nSpeed & SLAs\t15\tConsistently missing\tMostly meeting\tMeeting all SLAs\tExceeding targets\nFirst Contact Resolution\t15\t<50% FCR\t50-65%\t65-80%\t>80%\nSelf-Service Effectiveness\t10\tNo KB or unused\tBasic KB, <15% deflection\tGood KB, 15-35%\tExcellent, >35%\nCustomer Satisfaction\t15\tCSAT <70%\t70-80%\t80-90%\t>90%\nTeam Performance\t10\tHigh turnover, low morale\tStable but disengaged\tEngaged, developing\tHigh-performing, growing\nProcess Maturity\t10\tAd hoc, no documentation\tSome processes defined\tDocumented, followed\tOptimized, automated\nContinuous Improvement\t10\tReactive only\tSome VoC sharing\tRegular improvement cycle\tData-driven, proactive\nEdge Cases & Special Situations\nMulti-Language Support\nPrioritize languages by customer revenue concentration\nUse AI translation for first-pass, human review for complex issues\nMaintain separate KB per language (or use auto-translate with quality gate)\nTime zone coverage must match language markets\nB2B vs B2C Support\nB2B: Named accounts, dedicated agents for enterprise, technical depth required, QBR integration\nB2C: Volume-optimized, self-service heavy, faster resolution expected, social media critical\nRegulated Industries (Healthcare, Finance)\nAdditional compliance training required\nAudit trail on all customer interactions\nPII handling protocols — what agents can and cannot access\nResponse templates reviewed by legal/compliance quarterly\nSeasonal Peaks (E-commerce, Events)\nHire temp agents 4-6 weeks before peak\nCreate peak-specific playbooks and templates\nIncrease self-service capacity (chatbot, KB updates)\nAdjust SLAs transparently during known peak periods\nSupport During Product Migration/Major Change\nDedicated war room for first 72 hours post-change\nPre-written communication templates for expected issues\nIncreased staffing +50% for 2 weeks post-change\nDaily hot-fix coordination with engineering\nNatural Language Commands\n\nUse these to interact with this skill:\n\n\"Assess our support function\" → Run Phase 1 assessment\n\"Design our channel strategy\" → Build channel architecture (Phase 2)\n\"Set up ticket management\" → Configure ticket system (Phase 3)\n\"Write response templates\" → Generate templates for common scenarios (Phase 4)\n\"Build escalation process\" → Design tier structure and escalation rules (Phase 5)\n\"Plan our knowledge base\" → Design KB architecture and content plan (Phase 6)\n\"Create support dashboard\" → Build metrics and reporting (Phase 7)\n\"Help me hire support agents\" → Hiring plan and onboarding (Phase 8)\n\"Set up QA program\" → Quality assurance framework (Phase 9)\n\"Automate our support\" → AI and automation strategy (Phase 10)\n\"Handle [difficult situation]\" → Situation-specific playbook (Phase 11)\n\"Review our support health\" → Full health assessment with scoring (Phase 15)\n⚡ Level Up Your Support Operations\n\nThis free skill gives you the complete methodology. For industry-specific support playbooks with compliance frameworks, SLA templates, and vertical-specific ticket taxonomies:\n\nAfrexAI Context Packs — $47 each\n\n🏥 Healthcare Pack — HIPAA-compliant support workflows\n💰 Fintech Pack — Regulated financial services support\n🛒 Ecommerce Pack — High-volume consumer support operations\n💻 SaaS Pack — Technical product support at scale\n🔗 More Free Skills by AfrexAI\nafrexai-customer-success — Retention, health scoring, expansion revenue\nafrexai-sales-playbook — Complete B2B sales methodology\nafrexai-agent-engineering — Build autonomous AI agents\nafrexai-openclaw-mastery — Master your OpenClaw setup\nafrexai-conversational-ai — Design chatbots and voice agents\n\nInstall: clawhub install afrexai-support-operations\n\nBrowse all skills: clawhub.com" }, "trust": { "sourceLabel": "tencent", "provenanceUrl": "https://clawhub.ai/1kalin/afrexai-support-operations", "publisherUrl": "https://clawhub.ai/1kalin/afrexai-support-operations", "owner": "1kalin", "version": "1.0.0", "license": null, "verificationStatus": "Indexed source record" }, "links": { "detailUrl": "https://openagent3.xyz/skills/afrexai-support-operations", "downloadUrl": "https://openagent3.xyz/downloads/afrexai-support-operations", "agentUrl": "https://openagent3.xyz/skills/afrexai-support-operations/agent", "manifestUrl": "https://openagent3.xyz/skills/afrexai-support-operations/agent.json", "briefUrl": "https://openagent3.xyz/skills/afrexai-support-operations/agent.md" } }