How to Measure AI Support Success: The KPIs That Actually Matter
You launched AI support. Now what? Here are the 8 metrics that tell you if it's actually working—and how to improve them.
Why Most Teams Track the Wrong Metrics
The Problem:
- Teams track vanity metrics (total messages, response time)
- Ignore business impact (resolution rate, escalation quality)
- Miss optimization opportunities hiding in the data
What to do instead: Focus on metrics that answer these 3 questions:
1. Is AI solving customer problems? (Effectiveness)
2. Is it making customers happy? (Satisfaction)
3. Is it saving money? (Efficiency)
The 8 Essential AI Support Metrics
Tier 1: Customer Success Metrics
1. **Resolution Rate** 🎯
What it is: % of conversations where AI fully resolves the issue without human help
Target: 60-80% for mature systems, 40-60% in first 3 months
Formula: (AI-resolved conversations / Total conversations) × 100
Why it matters: This is THE metric. If AI isn't resolving issues, nothing else matters.
How to improve:
- ✓Expand knowledge base for common questions
- ✓Improve escalation triggers (stop premature escalations)
- ✓Train AI on past successful resolutions
2. **Customer Satisfaction Score (CSAT)** 😊
What it is: % of customers who rate their AI interaction positively
Target: 80%+ (anything below 70% needs immediate attention)
How to measure: Post-conversation survey: "Did this solve your problem?"
Important: Track CSAT separately for AI-only vs escalated conversations.
Healthy System:
- AI-only CSAT: 85%
- Escalated CSAT: 90%
- (Escalations are higher because complex issues get expert help)
Problem System:
- AI-only CSAT: 60%
- Escalated CSAT: 75%
- (AI is confusing customers, humans are cleaning up mess)
3. **First Contact Resolution (FCR)** ⚡
What it is: % of issues resolved in the first interaction (no follow-up needed)
Target: 70%+ (varies by industry)
Formula: (Issues resolved in 1 conversation / Total issues) × 100
Why it matters: Low FCR = customers bouncing between AI and humans, creating frustration.
Red flags:
- 🚩 Customers ask same question multiple times
- 🚩 AI gives partial answers requiring follow-up
- 🚩 Customers say "as I mentioned before..."
Tier 2: Operational Efficiency Metrics
4. **Automation Rate** 🤖
What it is: % of total support volume handled entirely by AI
Target: 70-85% (not 100%—some issues SHOULD go to humans)
Formula: (AI-only conversations / Total conversations) × 100
Sweet spot: 75-80% automation with high CSAT
| Industry | Typical Rate | High Performers |
|---|---|---|
| E-commerce | 70-75% | 80-85% |
| SaaS | 65-70% | 75-80% |
| Financial | 60-65% | 70-75% |
5. **Escalation Rate & Quality** 🆙
What to track:
- Rate: % of conversations escalated to humans (20-30% is healthy)
- Quality: % of escalations that were necessary
Target: <25% escalation rate, 90%+ escalations are "good"
Good escalations:
- ✅Complex technical issues
- ✅Emotional/upset customers
- ✅Policy exceptions
- ✅VIP customers
Bad escalations:
- ❌AI doesn't understand simple questions
- ❌Knowledge base gaps (info exists but AI can't find it)
- ❌Premature escalation (AI gives up too soon)
How to improve:
- Analyze escalated conversations weekly
- Add missing content to knowledge base
- Adjust escalation triggers
- Train AI on edge cases
6. **Average Handle Time (AHT)** ⏱️
What it is: Average time to resolve an issue
AI Target: <2 minutes
Human Target: 5-15 minutes (varies by complexity)
Why it matters: Shows if AI is efficient or wasting customer time
Warning signs:
- ⚠️AI conversations lasting >5 minutes
- ⚠️Multiple back-and-forth exchanges
- ⚠️Customer gives up mid-conversation
Fix: Look for conversations where AI asks too many clarifying questions or goes in circles.
Tier 3: Business Impact Metrics
7. **Cost Per Resolution** 💰
What it is: Total cost / Number of resolved issues
Typical costs:
- AI: $0.50 - $2 per resolution
- Human: $5 - $15 per resolution
ROI calculation:
Monthly cost savings = (Human cost - AI cost) × AI resolutions
Real example:
Before AI:
- 10,000 tickets/month
- $10 cost per ticket
- Total: $100,000/month
After AI:
- 7,500 AI resolutions at $1 = $7,500
- 2,500 human resolutions at $10 = $25,000
- Total: $32,500/month
Savings: $67,500/month ($810,000/year)
8. **Containment Rate** 🔒
What it is: % of customers who don't contact support again within 7 days for the same issue
Target: 85%+ (low rate = AI isn't really solving problems)
Formula: (Customers not returning / Total customers) × 100
Why it matters: Differentiates between "AI gave an answer" and "AI actually solved the problem."
How to Track These Metrics
Essential Analytics Dashboard
Daily Monitoring
- ✓Resolution rate
- ✓Escalation rate
- ✓CSAT score
Weekly Review
- ✓Automation rate trends
- ✓Top unresolved topics
- ✓Escalation quality
Monthly Deep Dive
- ✓Cost per resolution
- ✓Containment rate
- ✓ROI calculation
- ✓Knowledge base gaps
Benchmarks by Maturity
First 3 Months (Learning Phase)
- Resolution rate: 40-60%
- Automation rate: 50-70%
- CSAT: 70-80%
- Escalation rate: 30-40%
6-12 Months (Optimized)
- Resolution rate: 70-85%
- Automation rate: 75-85%
- CSAT: 85%+
- Escalation rate: 15-25%
Key insight: Don't expect perfection on day 1. Track trends, not absolutes.
Red Flags to Watch For
🚨 Declining Resolution Rate
Possible causes:
- Product changed, knowledge base didn't
- Seasonal topics not covered
- New customer segment with different questions
Fix: Review recent unresolved conversations, identify patterns, update knowledge base
🚨 High Escalation Rate (>40%)
Possible causes:
- Overly aggressive escalation triggers
- Knowledge base gaps
- AI not confident enough
Fix: Analyze escalated conversations, adjust triggers, add missing content
🚨 Low CSAT Despite High Resolution
Possible causes:
- AI is technically correct but tone is off
- Responses too long/complex
- Customers don't trust AI
Fix: Review low-rated conversations, adjust response style, add transparency
🚨 High AHT (>5 min)
Possible causes:
- AI asking too many clarifying questions
- Going in circles
- Poor knowledge base structure
Fix: Streamline conversation flows, improve documentation
Action Plan: Weekly Optimization Routine
Monday: Review Dashboard
- Check week-over-week trends
- Flag any metrics outside target range
- Identify top 3 priorities for the week
Tuesday-Thursday: Deep Dives
- Day 1: Review 10 unresolved conversations
- Day 2: Review 10 escalated conversations
- Day 3: Review 10 low-CSAT conversations
Friday: Implement Fixes
- Update knowledge base (1-2 hours)
- Adjust escalation rules if needed
- Document learnings
Time commitment: 3-5 hours/week
Impact: Compound improvements of 5-10% per month
The One Metric Dashboard
If you can only track one metric, make it this:
**Effective Resolution Rate (ERR)**
Formula:
(AI-resolved conversations with CSAT >4) / Total conversations × 100
Why: Combines resolution success AND customer satisfaction
Target: 60%+
This single number tells you if your AI is both solving problems AND making customers happy.
Getting Started Today
Week 1: Set Up Tracking
- ✓Implement post-conversation CSAT survey
- ✓Tag conversations (AI-only, escalated, unresolved)
- ✓Set up basic analytics dashboard
- ✓Establish baseline for each metric
Week 2: Analyze
- ✓Review 20 AI-only conversations
- ✓Review 20 escalated conversations
- ✓Identify top 3 failure patterns
- ✓Document improvement opportunities
Week 3: Optimize
- ✓Update knowledge base to address gaps
- ✓Adjust escalation triggers
- ✓Train AI on identified edge cases
- ✓Re-measure metrics
Week 4: Repeat
Build this into a continuous improvement cycle.
The Bottom Line
Measure what matters:
1. Is AI solving problems? → Resolution Rate
2. Are customers happy? → CSAT
3. Is it efficient? → Automation Rate & Cost per Resolution
Track trends, not absolutes. A system improving 5% per month will beat a "perfect" static system in 6 months.
Review weekly. 30 minutes of analysis prevents hours of firefighting.
Ready to Launch AI Support with Built-In Analytics?
Resly includes real-time dashboards tracking all 8 essential metrics.