campaign-metrics
Cold email campaign KPIs, benchmarks, and diagnostic patterns
Best use case
campaign-metrics is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Cold email campaign KPIs, benchmarks, and diagnostic patterns
Teams using campaign-metrics should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/campaign-metrics/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How campaign-metrics Compares
| Feature / Agent | campaign-metrics | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Cold email campaign KPIs, benchmarks, and diagnostic patterns
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
AI Agents for Marketing
Discover AI agents for marketing workflows, from SEO and content production to campaign research, outreach, and analytics.
Best AI Agents for Marketing
A curated list of the best AI agents and skills for marketing teams focused on SEO, content systems, outreach, and campaign execution.
SKILL.md Source
plugin: instantly
updated: 2026-01-20
# Campaign Metrics
## Core KPIs
### Primary Metrics
| Metric | Formula | Benchmark (Cold Email) |
|--------|---------|------------------------|
| Open Rate | (Opened / Sent) * 100 | 40-50% (good), 25-40% (average) |
| Reply Rate | (Replied / Sent) * 100 | 5-10% (good), 2-5% (average) |
| Positive Reply Rate | (Positive / Replied) * 100 | 25-40% (good) |
| Bounce Rate | (Bounced / Sent) * 100 | <2% (healthy) |
| Unsubscribe Rate | (Unsubscribed / Sent) * 100 | <0.5% (healthy) |
### Secondary Metrics
| Metric | Formula | Use Case |
|--------|---------|----------|
| Emails per Lead | Total Sent / Unique Leads | Sequence effectiveness |
| Reply by Step | Replies per step / Sent per step | Identify best-performing emails |
| Time to Reply | Avg time between send and reply | Timing optimization |
## Benchmark Reference
### Industry Benchmarks by Vertical
| Vertical | Open Rate | Reply Rate | Notes |
|----------|-----------|------------|-------|
| SaaS | 45-55% | 5-12% | Higher engagement |
| Agency | 35-45% | 3-7% | Competitive space |
| E-commerce | 30-40% | 2-5% | Volume-focused |
| Financial Services | 25-35% | 2-4% | Compliance-heavy |
### Performance Tiers
```
EXCELLENT (Top 10%)
Open Rate: >50%
Reply Rate: >10%
Bounce Rate: <1%
GOOD (Top 25%)
Open Rate: 40-50%
Reply Rate: 5-10%
Bounce Rate: 1-2%
AVERAGE (Middle 50%)
Open Rate: 25-40%
Reply Rate: 2-5%
Bounce Rate: 2-5%
POOR (Bottom 25%)
Open Rate: 15-25%
Reply Rate: 1-2%
Bounce Rate: 5-10%
CRITICAL (Bottom 10%)
Open Rate: <15%
Reply Rate: <1%
Bounce Rate: >10%
```
## Diagnostic Patterns
### Pattern Matrix
| Open Rate | Reply Rate | Diagnosis | Action |
|-----------|------------|-----------|--------|
| Low (<25%) | Any | Subject line issue | A/B test subjects |
| High (>40%) | Low (<2%) | Body copy issue | Rewrite email body |
| High | High | Winning combo | Scale and replicate |
| Declining | Stable | Fatigue setting in | Refresh creative |
| Any | Any + High Bounce | List quality issue | Verify emails |
### Time-Based Analysis
| Pattern | Meaning | Action |
|---------|---------|--------|
| Monday spike | Inbox cleared over weekend | Send Sun night or Mon early |
| Friday drop | Weekend mindset | Avoid Fri afternoon sends |
| Steady decline | Audience exhaustion | Rotate lists or refresh copy |
| Random spikes | External event correlation | Analyze and replicate |
## Score Calculation
### Campaign Health Score (0-100)
```
health_score = (
open_score * 0.25 +
reply_score * 0.35 +
deliverability_score * 0.25 +
trend_score * 0.15
)
```
**Component Calculations:**
```
open_score = normalize(open_rate, min=0, max=60)
60%+ open = 100 points
40% open = 67 points
20% open = 33 points
0% open = 0 points
reply_score = normalize(reply_rate, min=0, max=15)
15%+ reply = 100 points
10% reply = 67 points
5% reply = 33 points
0% reply = 0 points
deliverability_score = 100 - (bounce_rate * 10)
0% bounce = 100 points
5% bounce = 50 points
10% bounce = 0 points
trend_score = based on week-over-week change
+10% improvement = 100 points
Stable = 50 points
-10% decline = 0 points
```
### Score Interpretation
| Score | Rating | Action Required |
|-------|--------|-----------------|
| 90-100 | Excellent | Maintain, scale if possible |
| 75-89 | Good | Minor optimizations |
| 60-74 | Average | Address weak areas |
| 40-59 | Poor | Major revision needed |
| 0-39 | Critical | Pause and fix immediately |Related Skills
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