cs-analytics

Original：🇺🇸 English

Translated

Measure and optimize customer service performance using CSAT, NPS, CES, First Contact Resolution, and text mining on support tickets. Use this skill when the user needs to evaluate CS team performance, identify top complaint drivers, optimize staffing, or build CS dashboards — even if they say 'is our CS team doing well', 'what are customers complaining about', 'how many agents do we need', or 'build a CS dashboard'.

3installs

Sourceasgard-ai-platform/skills

Added on2026-04-15

NPX Install

npx skill4agent add asgard-ai-platform/skills cs-analytics

SKILL.md Content

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Customer Service Analytics

Framework

IRON LAW: Measure Satisfaction AND Efficiency — Never Just One

High CSAT with terrible resolution time = unsustainable (agents spend
too long per ticket). Fast resolution with low CSAT = cutting corners.
Both dimensions must be tracked and balanced.

Key Metrics

Satisfaction Metrics

Metric	What It Measures	How to Collect	Benchmark
CSAT	Satisfaction with specific interaction	Post-interaction survey (1-5 scale)	> 4.0/5
NPS	Likelihood to recommend	"How likely to recommend?" (0-10)	> 30
CES	Effort required to resolve	"How easy was it to resolve?" (1-7)	> 5.0/7

Efficiency Metrics

Metric	Formula	Benchmark
First Contact Resolution (FCR)	Resolved on first contact / Total contacts	> 70%
Average Handle Time (AHT)	Total handle time / Total contacts	5-8 min (varies by industry)
Average Response Time	Time from ticket creation to first response	< SLA target
Backlog	Open tickets / Daily throughput	< 1 day
Escalation Rate	Escalated tickets / Total tickets	< 20%
Reopen Rate	Reopened tickets / Resolved tickets	< 5%

Operational Metrics

Metric	Formula	Use
Ticket Volume	Tickets per day/week/month	Staffing planning
Channel Mix	% by channel (email, chat, phone, LINE)	Resource allocation
Peak Hours	Volume by hour-of-day	Shift scheduling
Category Distribution	% by issue type	Process improvement priority

Analysis Workflows

1. Top Contact Reason Analysis

Categorize all tickets by reason (auto-tag or manual)
Pareto chart: top 5 reasons usually account for 60-80% of volume
For each top reason: can it be self-served? Automated? Eliminated at source?

2. Text Mining on Tickets

Extract frequent keywords/phrases from ticket descriptions
Cluster into topics (LDA, BERTopic, or simple TF-IDF)
Identify emerging issues (new topics appearing in recent weeks)
Sentiment analysis on customer messages

3. Staffing Optimization

Required Agents = Peak Hour Volume × AHT / (60 × Utilization Target)

Example: 50 tickets/hour × 8 min AHT / (60 × 0.75 utilization) = 8.9 → 9 agents

Add buffer for breaks, meetings, and training (~15-20%).

4. Agent Performance

Metric	Compare	Action
Individual CSAT vs team avg	Identify coaching needs	Training for below-average
Individual AHT vs team avg	Identify efficiency gaps	Shadow high-performers
FCR by agent	Identify knowledge gaps	Knowledge base improvements

VOC (Voice of Customer) Tracking

Signal	Source	Frequency
Emerging complaints	Ticket text mining	Weekly
Feature requests	Tagged tickets + surveys	Monthly
Churn signals	"Cancel" intent tickets, low CSAT patterns	Weekly
Praise patterns	High CSAT + positive comments	Monthly (share with team)

Output Format

markdown

# CS Analytics Report: {Period}

## Summary Dashboard
| Metric | Current | Prior | Target | Status |
|--------|---------|-------|--------|--------|
| CSAT | {X}/5 | {X}/5 | >4.0 | 🟢/🟡/🔴 |
| FCR | {%} | {%} | >70% | 🟢/🟡/🔴 |
| Avg Response Time | {hrs} | {hrs} | <{X}hrs | 🟢/🟡/🔴 |
| Ticket Volume | {N} | {N} | — | ↑/↓ |

## Top Contact Reasons (Pareto)
| # | Reason | Volume | % | Self-Servable? |
|---|--------|--------|---|---------------|
| 1 | {reason} | {N} | {%} | Y/N |

## Emerging Issues
{New topics detected in text mining this period}

## Staffing
- Current agents: {N}
- Required (based on volume): {N}
- Gap: {over/under-staffed by N}

## Recommendations
1. {highest-impact improvement}

Gotchas

CSAT response bias: Only 10-20% of customers respond to surveys, usually the very happy and very unhappy. The silent majority's experience is unknown. Supplement with behavioral data (repeat contact, churn).
NPS is strategic, CSAT is tactical: NPS measures overall brand loyalty (long-term). CSAT measures specific interaction quality (short-term). Don't use NPS to evaluate individual agents.
AHT optimization can hurt quality: Pressure to reduce AHT may cause agents to rush, reducing FCR and CSAT. Optimize FCR first, then look at AHT.
Ticket categorization drift: Categories become outdated as products evolve. Review and update the category taxonomy quarterly.
Correlation ≠ causation in CS data: "Agents who use more templates have higher CSAT" might mean templates help, OR that experienced agents (who happen to use templates) are just better.

References

For NPS survey design, see
```
references/nps-methodology.md
```
For text mining on support tickets, see
```
references/ticket-text-mining.md
```