\"cs-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

# 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