sequence-performance

name	sequence-performance
version	1.0.0
description	Email campaign/sequence performance review composite. Pulls campaign data (sends, opens, replies, bounces), reads actual email copy and subject lines, analyzes reply content (objections, positive interest, questions), and produces a diagnostic report covering quantitative metrics, copy quality, lead quality, and actionable recommendations. Tool-agnostic — works with Smartlead (MCP), Instantly, Outreach, Lemlist, Apollo, or CSV data.
tags	["research"]

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

Data Point	What We Need
Total emails sent	By touch (Touch 1, Touch 2, Touch 3, etc.)
Total unique recipients	Deduplicated count
Opens	By touch, unique opens vs. total opens
Replies	By touch, total reply count
Bounces	Hard bounces + soft bounces
Unsubscribes	Count
Clicks	If link tracking is on
Positive replies	If categorized in the tool
Meetings booked	If tracked

How to pull by tool:

Tool	Method
Smartlead (MCP)	mcp__smartlead__get_campaign_stats, mcp__smartlead__get_campaign_sequence_analytics, mcp__smartlead__get_campaign_variant_statistics
Instantly / Outreach / Lemlist / Apollo	Ask user for CSV export or paste metrics
Other	User provides CSV with columns: email, status, opened, replied, bounced

B) Email Copy (Sequence Content)

Pull the actual templates for every touch:

Tool	Method
Smartlead (MCP)	mcp__smartlead__get_campaign_sequences
Others	User pastes the copy or provides CSV export

C) Reply Content

Pull the actual text of every reply:

Tool	Method
Smartlead (MCP)	mcp__smartlead__get_campaign_leads_history, mcp__smartlead__fetch_master_inbox_replies
Others	User provides reply dump or CSV export

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

Metric	Cold (SMB)	Cold (Mid-Market)	Cold (Enterprise)	Warm/Nurture
Open rate	40-60%	30-50%	25-40%	50-70%
Reply rate	3-8%	2-5%	1-3%	10-20%
Positive reply rate	1-3%	0.5-2%	0.3-1%	5-10%
Bounce rate	<3%	<3%	<2%	<1%
Unsubscribe rate	<1%	<1%	<0.5%	<0.5%

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

Category	Definition
Positive interest	Wants to learn more, open to a conversation
Meeting request	Explicitly asks to meet or provides availability
Warm / Curious	Interested but non-committal, asks questions
Objection — Timing	Not now, but potentially later
Objection — Budget	Can't afford or not a priority
Objection — Competitor	Already using a competing solution
Objection — Relevance	Doesn't see the fit
Objection — Authority	Not the right person
Not interested	Flat no
Auto-reply / OOO	Automated response
Referral	Redirects to someone else
Question	Asks about product/offering

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

Score	Criteria
Strong	>50% positive/warm. Objections are handleable.
Mixed	30-50% positive. Mix of handleable and terminal.
Weak	<30% positive. Dominated by "not interested" and "not relevant."
Toxic	High unsubscribe + angry replies. Something is fundamentally wrong.

Step 4: Copy Quality Assessment

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

Subject Lines

Criterion	Red Flags
Length	>60 chars gets truncated on mobile
Specificity	Generic "Quick question" or "Checking in"
Spam triggers	"Free", "Limited time", ALL CAPS
Open rate correlation	Low open rate = subject line problem

Email Body

Criterion	Red Flags
Hook (first line)	"I'm reaching out because..." or "We are a company that..."
Length	Over 150 words
Value prop clarity	Jargon, vague language, buzzwords
Proof points	No proof = no credibility
Personalization	Only {first_name} merge field
CTA	Multiple CTAs, high-friction asks, or no CTA
Filler language	"Hope this finds you well", "just checking in"
Sequence progression	Touch 2 is just a "bump" of Touch 1

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)

Pattern	What It Tells You
High "not relevant" replies	Sending to people who don't have the problem
High "wrong person" replies	Right companies, wrong roles
High "already have a solution"	Right problem, late to the party
High "timing" objections	Right people, right problem, wrong moment — not a targeting issue
Low reply + high open rate	People open but don't find it relevant — copy/targeting mismatch
High bounce rate	List quality issue — bad emails, old data

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

Finding	Recommendation
Open rate below benchmark	Subject line rewrite — suggest 3 alternatives
Reply rate below + open rate fine	Body copy issue — focus on hook, proof, CTA
Both below benchmark	Full sequence rewrite
High "not relevant" objections	Targeting issue — tighten ICP filters
High "wrong person" referrals	Title targeting issue — shift to referred titles
High "already have solution"	Add competitive differentiation to copy
High "timing" objections	Not a problem — set up 90-day re-engagement
One variant clearly winning	Scale winner, test new idea in losing slot
Touch 2/3 near-zero marginal replies	Cut sequence short or rewrite with new angles
High bounce rate	List hygiene — verify emails, check data source
Deliverability <95%	Infrastructure — check SPF/DKIM/DMARC, reduce volume

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

Missing Data	What Gets Skipped	Still Useful?
Reply text	Reply classification + objection patterns	Partially — metrics + copy still run
Variant data	Variant analysis	Yes — single-variant analysis still runs
Lead demographics	Targeting assessment	Yes — infers from reply patterns
Open tracking	Open rate analysis	Partially — reply rate + copy still run

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.