CSAT Analyzer — Customer Satisfaction Metric Design + Analysis
A skill that enhances the CS performance measurement capabilities of cs-analyst.
Target Agents
- cs-analyst — Designs and analyzes CS metric systems
- cs-reviewer — Validates CS quality based on metrics
3 Core CS Metrics
CSAT (Customer Satisfaction Score)
Question: "How satisfied were you with this interaction?" (1-5 scale)
CSAT = (4 + 5 rated responses) / Total responses x 100%
Benchmarks:
Excellent: 85%+
Good: 70-84%
Needs Improvement: 60-69%
At Risk: <60%
Measurement Timing: Immediately after interaction (interaction-based)
NPS (Net Promoter Score)
Question: "How likely are you to recommend this service to a friend or colleague?" (0-10 scale)
Classification:
Promoter: 9-10
Passive: 7-8
Detractor: 0-6
NPS = Promoter% - Detractor% (Range: -100 to +100)
Benchmarks (B2C SaaS):
Excellent: 50+
Good: 30-49
Average: 0-29
At Risk: <0
Measurement Timing: Quarterly (relationship-based)
CES (Customer Effort Score)
Question: "How easy was it to resolve your issue?" (1-7 scale)
CES = Average of all responses
Benchmarks:
Excellent: 5.5+
Good: 4.5-5.4
Needs Improvement: 3.5-4.4
At Risk: <3.5
Measurement Timing: Immediately after interaction (effort-based)
Note: Strongest predictor of repeat purchase behavior
Operational Metrics
Efficiency Metrics
| Metric |
Formula |
Benchmark |
| FCR (First Contact Resolution) |
1st contact resolutions / Total cases x 100 |
70-75% |
| AHT (Average Handle Time) |
Total handle time / Number of cases |
Chat 5-8 min, Phone 6-10 min |
| ASA (Average Speed of Answer) |
Total wait time / Answered cases |
Chat 30 sec, Phone 60 sec |
| Escalation Rate |
Escalated cases / Total cases |
<15% |
| Re-inquiry Rate |
Re-inquiries within 7 days / Total cases |
<20% |
Productivity Metrics
| Metric |
Formula |
Purpose |
| Cases per Agent |
Daily cases / Number of agents |
Capacity planning |
| Cost per Channel |
Channel cost / Channel cases |
Channel optimization |
| Self-Service Ratio |
FAQ/bot resolutions / Total inquiries |
Automation effectiveness |
VOC (Voice of Customer) Analysis Framework
Sentiment Classification
Positive Keywords: thank you, fast, friendly, resolved, satisfied, great
Negative Keywords: complaint, slow, inconvenient, repeated, no response, angry
Neutral: inquiry, confirmation, curious, please let me know
Sentiment Score = (Positive count - Negative count) / Total count
Topic Classification
1. Category-based Classification:
- Product Feature Issues (40%)
- Billing/Refunds (25%)
- Shipping/Logistics (15%)
- Account/Authentication (10%)
- Other (10%)
2. Trend Analysis:
- Surging topic detection (week-over-week +50%)
- New topic identification
- Seasonal patterns
3. Severity Classification:
- Critical: Service outage, financial loss
- Major: Feature malfunction, recurring issues
- Minor: Inconvenience, improvement requests
CS Dashboard Design
Real-Time Monitor (Operations Team):
+------------+------------+------------+
| Queue Count| Avg Wait | Agents |
| [Real-time]| [Real-time]| [Avail/Total]|
+------------+------------+------------+
| Hourly Incoming Volume Graph |
+---------------------------------------+
| Channel Status (Chat/Phone/Email) |
+---------------------------------------+
Weekly/Monthly Report (Management):
+------------+------------+------------+
| CSAT | NPS | FCR |
| [Trend] | [Trend] | [Trend] |
+------------+------------+------------+
| Inquiry Distribution by Topic + WoW |
+---------------------------------------+
| Agent Performance (Volume, CSAT) |
+---------------------------------------+
| Top 5 VOC Issues |
+---------------------------------------+
Improvement Framework
When CSAT is low:
1. Agent training (response quality)
2. Improve response templates
3. Authority delegation (immediate resolution capability)
When FCR is low:
1. Strengthen FAQ/knowledge base
2. Expand agent authority
3. Redefine escalation criteria
When AHT is high:
1. Provide macros/templates
2. Improve internal tool UX
3. Training + mentoring
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