You say: "We reduced p99 from 2s to 200ms". The CEO asks: "So what?". This disconnect between technical metrics and business impact is one of the biggest barriers to performance investment. This article teaches you how to build that bridge.
Technical metrics are for engineers. Business impact is for decisions.
The Chasm Between Technical and Business
The technical language
"We reduced p95 latency from 500ms to 100ms"
"We increased throughput from 1000 to 5000 req/s"
"We decreased error rate from 2% to 0.1%"
What business understands
"?"
What business needs to hear
"Users now complete checkout 5x faster,
reducing cart abandonment by 15%"
"We can serve 5x more concurrent users
with the same infrastructure"
"We lose 95% fewer sales to technical errors"
Mapping Metrics to Impact
Correlation framework
Technical Metric → User Experience → Business Outcome
Examples:
High latency → Frustration → Abandonment → Revenue loss
Frequent errors → Distrust → Churn → Reduced LTV
Low throughput → Queues → Timeout → Lost sales
Connecting metrics
Latency:
Technical: Response p95
User: Perceived wait time
Business: Conversion rate, bounce rate
Availability:
Technical: Uptime, error rate
User: "Does the site work when I need it?"
Business: Revenue lost to downtime
Throughput:
Technical: req/s, transactions/s
User: "Can I use it during peaks?"
Business: Maximum revenue capacity
Case Studies: The Real Connection
Case 1: Latency and Conversion
Context: E-commerce with 1M visits/month
Before:
- Load time: 4.2s
- Conversion rate: 2.1%
Optimization:
- Load time: 1.8s
- Conversion rate: 2.8%
Impact calculation:
- Visitors: 1,000,000/month
- Conversion before: 21,000 sales
- Conversion after: 28,000 sales
- Average ticket: $30
- Incremental revenue: 7,000 × $30 = $210,000/month
Case 2: Availability and Revenue
Context: B2B SaaS, $100K MRR
Situation:
- Current availability: 99.5% (3.6h downtime/month)
- Target: 99.9% (43min downtime/month)
Downtime calculation:
- Productive hours/month: 720h
- Revenue/hour: $100,000 / 720 = $139/h
- Loss at 99.5%: 3.6h × $139 = $500/month
- Loss at 99.9%: 0.7h × $139 = $97/month
- Savings: $403/month
Note: Beyond financial, consider impact on:
- Customer trust
- Contractual SLAs
- Support cost during incidents
Case 3: Performance and Churn
Context: Mobile app, 100K active users
Observed correlation:
- Users with p95 < 1s: 2% churn/month
- Users with p95 1-3s: 5% churn/month
- Users with p95 > 3s: 12% churn/month
Current distribution:
- 40% good experience (< 1s)
- 35% medium experience (1-3s)
- 25% poor experience (> 3s)
Optimization impact:
If moving 25% from "poor" to "good":
- Churn reduction: 2,500 users × 10% = 250 users/month
- Average LTV: $100
- Preserved value: $25,000/month
Building Business-Oriented SLOs
What are SLOs
SLO (Service Level Objective):
Internal service quality target
that connects performance to experience
Components:
- SLI (Service Level Indicator): The metric
- Target: The acceptable value
- Window: The evaluation period
SLOs that make sense
Bad (technical only):
"p95 latency < 200ms"
→ Doesn't say why it matters
Good (connected to experience):
"95% of checkouts complete in < 3s"
→ Reflects user journey
Great (connected to business):
"95% of checkouts complete in < 3s,
maintaining conversion above 2.5%"
→ Links technical to outcome
Framework for defining SLOs
1. Identify critical journeys:
- What action generates revenue?
- What action causes churn if it fails?
2. Define acceptable experience:
- What wait time is tolerable?
- What error rate is acceptable?
3. Translate to metrics:
- Latency → journey time
- Errors → journey completion
- Throughput → capacity to serve
4. Validate with historical data:
- Is the target realistic?
- What's the current gap?
Impact Metrics by Vertical
E-commerce
Technical metrics → Impact:
Search time:
→ Products viewed
→ Purchase probability
Checkout time:
→ Abandonment rate
→ Lost revenue
Availability during promotions:
→ Peak sales
→ Marketing ROI
B2B SaaS
Technical metrics → Impact:
Dashboard latency:
→ Tool adoption
→ Contract renewal
Integration reliability:
→ Perceived value
→ Account expansion
API performance:
→ Developer satisfaction
→ Ecosystem growth
Fintech
Technical metrics → Impact:
Transaction latency:
→ Payment completion
→ Processed volume
Availability:
→ Compliance
→ Regulatory fines
Reconciliation time:
→ Operational cost
→ Cash flow
Communicating with Stakeholders
For the CEO
## Executive Summary - Performance Q1
### Revenue Impact
- Optimizations generated $420K in incremental revenue
- Project ROI: 15x
### Mitigated Risks
- Black Friday capacity: ✅ Guaranteed
- Critical incidents: ↓ 70%
### Next Steps
- Investment needed: $30K
- Expected return: $160K
For the CFO
## Financial Analysis - Performance
### Avoided Costs
| Item | Monthly Value |
|------|--------------|
| Avoided downtime | $9K |
| Optimized infrastructure | $6K |
| Reduced incidents | $4K |
| **Total** | **$19K** |
### Investment ROI
- Annual investment: $100K
- Annual savings: $228K
- Payback: 5.3 months
For the CPO
## Product Impact - Performance
### User Experience
- Average task time: -40%
- Satisfaction (NPS): +12 points
- Slowness complaints: -85%
### Engagement Metrics
- Sessions/user: +25%
- Features used: +30%
- Time on platform: +20%
Unified Dashboard
Recommended structure
┌─────────────────────────────────────────────────────┐
│ BUSINESS IMPACT │
│ [Revenue/hour] [Conversion] [Churn Risk] [NPS] │
├─────────────────────────────────────────────────────┤
│ USER EXPERIENCE │
│ [Checkout time] [Payment success] [Visible errors] │
├─────────────────────────────────────────────────────┤
│ TECHNICAL HEALTH │
│ [Latency] [Throughput] [Error Rate] [Saturation] │
├─────────────────────────────────────────────────────┤
│ CORRELATIONS │
│ [Latency × Conversion] [Errors × Churn] [Load × $] │
└─────────────────────────────────────────────────────┘
Correlated metrics
# Latency × conversion correlation
# Calculate conversion rate by latency bucket
# Conversion for requests < 1s
sum(rate(checkout_success{latency_bucket="<1s"}[1h]))
/ sum(rate(checkout_attempts{latency_bucket="<1s"}[1h]))
# Conversion for requests 1-3s
sum(rate(checkout_success{latency_bucket="1-3s"}[1h]))
/ sum(rate(checkout_attempts{latency_bucket="1-3s"}[1h]))
# Conversion for requests > 3s
sum(rate(checkout_success{latency_bucket=">3s"}[1h]))
/ sum(rate(checkout_attempts{latency_bucket=">3s"}[1h]))
Conclusion
Connecting technical metrics to business impact:
- Translates technical language for decision makers
- Justifies investment in performance
- Prioritizes optimizations by ROI
- Aligns teams technical and business
- Demonstrates value of engineering
Performance isn't about milliseconds. It's about money, users, and growth.
This article is part of the series on the OCTOPUS Performance Engineering methodology.