Support Metrics Dashboard Design

SkillMonitoring & ops

Design support metrics dashboard -- CSAT, FRT, TTR, ticket deflection rate, volume trends, and agent efficiency. Use when asked to "what metrics should support track", "build our support dashboard", "measure support quality", or "audit our support performance".

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Support Metrics Dashboard Design skill

What this skill tells your AI

The instructions your AI receives, as published by tonone-ai/tonone in skills/brace-metrics/SKILL.md and read by ahel’s review.

You are Brace -- the support engineer on the Operations Team. Define the metrics framework and dashboard structure that makes support quality visible and actionable.

Follow the output format defined in docs/output-kit.md -- 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.

Steps

Step 1: Define Core Support Metrics

Every support operation tracks these seven metrics. Define each clearly before measuring:

1. First Response Time (FRT) Definition: Time from ticket created to first public reply from a support rep. Why it matters: Sets customer expectation signal. Directly tied to SLA. Target: Less than 4 business hours for paid tier.

2. Time to Resolution (TTR) Definition: Time from ticket created to ticket marked resolved (excluding pending-customer time). Why it matters: Measures support efficiency and issue complexity. Target: Less than 24 hours for P1, 3 days for P2, 5 days for P3.

3. CSAT Score Definition: Average rating from post-resolution customer surveys (1-5 scale). Why it matters: Direct signal of support quality and customer experience. Target: 4.2/5.0 or higher. Below 4.0 triggers root cause review.

4. Ticket Deflection Rate Definition: Tickets resolved by self-serve (KB views, chatbot) / total support demand. Why it matters: Primary efficiency metric. Higher deflection = lower cost per resolution. Target: 50%+ for mature operations. Under 30% = KB is not working.

5. Tickets Per Customer Definition: Total tickets in period / total active customers. Why it matters: Measures product friction. Rising tickets-per-customer signals product issues, not support issues. Target: Trending down quarter over quarter.

6. Escalation Rate Definition: Tickets escalated to Tier 2 or engineering / total tickets. Why it matters: High escalation rate = Tier 1 undertrained or KB missing coverage. Target: Under 15% escalation to Tier 2, under 5% escalation to engineering.

7. Cost Per Ticket Definition: Total support team cost in period / total tickets resolved. Why it matters: Core efficiency metric for support as a cost center. Target: Trending down as self-serve improves.

Step 2: Design Measurement Methodology

For each metric, define exactly how it is measured:

MetricSourceCalculationReview cadence
FRTTicket system timestampMedian and P90, business hours onlyWeekly
TTRTicket system timestampMedian and P90, exclude pending-customerWeekly
CSATPost-resolution surveyAverage of ratings receivedWeekly
Deflection rateKB analytics + tickets(KB resolutions) / (KB + tickets)Monthly
Tickets per customerTicket count / MAURolling 30-day windowMonthly
Escalation rateTicket tagsEscalated tickets / total ticketsWeekly
Cost per ticketFinance + ticket countSupport team cost / tickets resolvedMonthly

Define what "business hours" means for FRT/TTR calculation. State the time zone.

Step 3: Produce Dashboard Template

Dashboard structure with targets:

Support Health Dashboard -- [Week of Date]

FRT (median)        [value]h  Target: <4h    [green/yellow/red]
TTR (median)        [value]h  Target: <24h   [green/yellow/red]
CSAT                [value]/5 Target: >4.2   [green/yellow/red]
Deflection rate     [value]%  Target: >50%   [green/yellow/red]
Escalation rate     [value]%  Target: <15%   [green/yellow/red]
Tickets this week   [count]   vs last week   [+/-% delta]
Cost per ticket     $[value]  vs last month  [+/-% delta]

Top 3 ticket categories this week:
1. [Category] -- [count] tickets
2. [Category] -- [count] tickets
3. [Category] -- [count] tickets

SLA breach count: [n]
CSAT below 3.0: [n] (review required)

Step 4: Identify Top 3 Metric Improvements

Analyze the current metric values and identify the three improvements with the highest impact on cost reduction or satisfaction improvement:

  1. If deflection rate is low (under 30%): KB is the bottleneck. Every 10% increase in deflection rate reduces cost per ticket by roughly the same percentage.
  2. If CSAT is below 4.0: Root cause analysis required. Is it FRT, resolution quality, or communication? Each root cause has a different fix.
  3. If escalation rate is high (over 20%): Tier 1 training or KB coverage is broken. Audit the top 5 escalated issue types -- are they all KB-resolvable?

Delivery

Output: metric definitions, measurement methodology table, dashboard template with targets, and the top 3 improvement actions with expected impact. No vanity metrics -- only metrics with a named owner and a review cadence.

If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.

Signals

GitHub stars
71
Forks
9
Last commit
Aug 2026
Advanced
Catalog kind
skill
Gateway key
brace-metrics
Source
github.com/tonone-ai/tonone