Sequence Performance

Goes beyond vanity metrics. Most campaign reports tell you open rate and reply rate. This skill reads the actual emails you sent, reads every reply you received, classifies the responses, evaluates your copy, evaluates your lead quality, and tells you specifically what's working, what's not, and what to do about it.

Three layers of analysis:

1. Quantitative: The numbers — sends, opens, replies, bounces, conversions, by touch and by variant
2. Qualitative (Copy): Are the subject lines, email bodies, CTAs, and personalization actually good?
3. Qualitative (Replies): What are people actually saying? What objections keep coming up?

When to Use

Use this skill when:

• User says "how's my campaign doing", "sequence performance", "campaign review", "email analytics"
• User says "analyze my outreach", "why isn't my campaign working", "review my email results"
• A campaign has been running for 7+ days and has meaningful data

Phase 0: Intake

Outreach Tool

1. What outreach tool do you use? (Smartlead / Instantly / Outreach.io / Lemlist / Apollo / Other)
2. How do we access campaign data? (MCP tools / API / CSV export / paste metrics)

Campaign Selection

3. Which campaign? (name or ID)
4. Date range? (or "all data")

Your Company Context (for copy evaluation)

5. What does your company do? (one-liner)
6. Who is your ICP? (titles, industries, company size)
7. What problem do you solve?
8. What's your CTA goal? (book meeting, get reply, drive to page)

Benchmark Context

9. Is this cold outreach or warm/nurture?
10. What segment are you selling to? (SMB, mid-market, enterprise)

Step 1: Pull Campaign Data

Pull three categories of data from the user's outreach tool:

A) Campaign Metrics

How to pull by tool:

mcp__smartlead__get_campaign_stats

mcp__smartlead__get_campaign_sequence_analytics

mcp__smartlead__get_campaign_variant_statistics

B) Email Copy (Sequence Content)

Pull the actual templates for every touch:

mcp__smartlead__get_campaign_sequences

C) Reply Content

Pull the actual text of every reply:

mcp__smartlead__get_campaign_leads_history

mcp__smartlead__fetch_master_inbox_replies

Human Checkpoint

Campaign: [name]
Status: [active/paused/completed]
Sent: X emails to Y recipients
Replies: Z (full text pulled for analysis)
Touches: N touches, M variants

Data looks complete? (Y/n)

Step 2: Quantitative Analysis

Benchmarks

Calculate

Overall metrics: open rate, reply rate, positive reply rate, bounce rate, unsubscribe rate, deliverability rate. Compare each to the benchmark.

Per-touch breakdown:

• Touch-level open/reply rates
• Marginal reply rate (replies from THIS touch / people who received this touch but hadn't replied yet)
• Touch contribution (what % of total replies came from each touch)

Variant analysis (if A/B testing):

• Open rate and reply rate per variant
• Statistical confidence: <50 sends = "insufficient data", 50-100 = "directional", 100-250 = "likely winner", 250+ = "statistically significant"
• Winner recommendation: scale, keep testing, or kill

Step 3: Reply Analysis

Read every reply, classify it, and extract patterns.

Reply Categories

Objection Patterns

• Which objection appears most? (reveals systemic issues)
• Do objections cluster at Touch 1 (bad targeting) vs. Touch 3 (fatigue)?
• Which are handleable (timing, authority) vs. terminal (relevance)?
• What exact language do people use?

Positive Signal Patterns

• Which touch/variant generated positive replies?
• What do positive responders have in common? (title, industry, company size)
• What questions do warm leads ask? (reveals what's missing from the email)

Reply Quality Score

Step 4: Copy Quality Assessment

Evaluate the actual email copy against best practices and reply data.

Subject Lines

Email Body

{first_name}

Grades

Grade each touch A through F on: hook quality, value prop clarity, proof usage, personalization level, CTA quality.

Step 5: Lead Quality Assessment

Evaluate whether we're sending to the right people.

Targeting Check

• Do lead titles match ICP buyer/champion/user personas?
• Are leads in target industries?
• Right seniority level for the ask?
• Company size in target range?

Signal Quality (from replies)

Step 6: Generate Report

Report Structure

# Sequence Performance Review: [Campaign Name]
**Period:** [date range] | **Status:** [active/paused/completed]

---

## Executive Summary

**Overall verdict:** [One sentence]

| Dimension | Grade | Assessment |
|-----------|-------|-----------|
| Metrics | [A-F] | [one-liner] |
| Copy Quality | [A-F] | [one-liner] |
| Lead Quality | [A-F] | [one-liner] |
| Reply Quality | [Strong/Mixed/Weak/Toxic] | [one-liner] |

### What's Working (Double Down)
- [Specific thing with data]

### What's Not Working (Fix or Kill)
- [Specific thing with data]

### Top 3 Actions
1. [Highest-impact action]
2. [Second]
3. [Third]

---

## Detailed Metrics

### Overall Performance
| Metric | Actual | Benchmark | Status |
|--------|--------|-----------|--------|
| Open rate | X% | Y% | [above/below] |
| Reply rate | X% | Y% | [above/below] |
| Bounce rate | X% | <3% | [flag] |
| ... | ... | ... | ... |

### Performance by Touch
| Touch | Sent | Open Rate | Reply Rate | Marginal Reply Rate | % of Total Replies |
|-------|------|-----------|------------|--------------------|--------------------|
| 1 | X | Y% | Z% | Z% | W% |

### Variant Performance (if A/B testing)
| Touch | Variant | Subject | Sent | Open Rate | Reply Rate | Confidence | Action |
|-------|---------|---------|------|-----------|------------|------------|--------|

---

## Reply Deep Dive

### Reply Classification
| Category | Count | % of Replies |
|----------|-------|-------------|

### Top Objections
| Objection | Count | Handleable? | Suggested Response |
|-----------|-------|------------|-------------------|

### Notable Replies
[5-10 most instructive replies with quotes]

---

## Copy Assessment
[Subject line verdicts, body grades, sequence architecture assessment]

---

## Lead Quality
[Targeting assessment, actual vs intended ICP]

---

## Recommendations (Prioritized)

### High Priority (Do This Week)
1. **[Action]** — [data point] → [expected impact]

### Medium Priority (Do This Month)
2. **[Action]** — [data point] → [expected impact]

### Kill List
- [Anything that should be stopped]

Recommendation Logic

Human Checkpoint

Present the executive summary, then ask:

Full detailed report available. Want to see the full breakdown, or act on a specific recommendation?

Adapting to Data Availability

Minimum viable data: Emails sent + reply count + email copy text.

Cost

Free. Pure reasoning + data from user's outreach tool.

Tips

• Run at Day 7 and Day 14. Day 7 catches deliverability and subject line problems. Day 14 gives enough replies for objection analysis.
• Reply analysis is where the gold is. Metrics tell you WHAT. Replies tell you WHY.
• High open + low reply = copy problem. The subject gets them to open but the email doesn't deliver.
• Low open + decent reply rate = subject line problem. The email works, people just aren't seeing it.
• "Not relevant" is the most important objection. If >20% say "this isn't for me," it's targeting, not copy.
• Don't kill a variant too early. Need 100+ sends per variant for directional data.
• Touch 2/3 should contribute 30-40% of replies. If Touch 1 is 90%+, your follow-ups aren't adding value.