asgard-ai-platform 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
# 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