campaign_selection: {
  campaign_id: string | null        # Specific campaign ID (if known)
  campaign_name: string | null      # Campaign name to search for
  date_range: {                     # Optional — pull all data if not specified
    start_date: string
    end_date: string
  } | null
  include_replies: boolean          # Default: true — pull actual reply text
}
outreach_tool: string               # From config
access_method: string               # From config

campaign_data: {
  campaign_name: string
  campaign_id: string
  date_range: { start: string, end: string }
  status: "active" | "paused" | "completed"
 
  metrics: {
    total_sent: integer
    unique_recipients: integer
    total_opens: integer
    unique_opens: integer
    total_replies: integer
    bounces: integer
    unsubscribes: integer
    clicks: integer | null
    meetings_booked: integer | null
  }
 
  sequence: [
    {
      touch_number: integer
      delay_days: integer               # Days after previous touch
      variants: [
        {
          variant_id: string            # "A", "B", etc.
          subject: string
          body: string
          personalization_fields: string[]
          metrics: {
            sent: integer
            opens: integer
            replies: integer
            bounces: integer
          }
        }
      ]
    }
  ]
 
  replies: [
    {
      sender_name: string
      sender_title: string | null
      sender_company: string | null
      sender_email: string
      reply_text: string
      reply_date: string
      triggered_by_touch: integer
      triggered_by_variant: string | null
      existing_category: string | null  # Tool's categorization if any
    }
  ]
 
  lead_list_summary: {
    total_leads: integer
    by_title: [ { title: string, count: integer } ] | null
    by_industry: [ { industry: string, count: integer } ] | null
    by_source: [ { source: string, count: integer } ] | null
  } | null
}

## Campaign Data Pulled
 
Campaign: [name]
Status: [active/paused/completed]
Date range: [start] to [end]
Sent: X emails to Y recipients
Replies received: Z (full text pulled for analysis)
Touches: N touches, M variants
Lead data available: [yes/partial/no]
 
Data looks complete? (Y/n)

campaign_data: { ... }                # From Step 1
outreach_type: string                 # From config ("cold" or "warm")
target_segment: string                # From config ("smb", "mid-market", "enterprise")

quantitative_analysis: {
  overall: {
    open_rate: percentage
    reply_rate: percentage
    positive_reply_rate: percentage
    bounce_rate: percentage
    unsubscribe_rate: percentage
    click_rate: percentage | null
    meeting_conversion: percentage | null
    deliverability_rate: percentage
  }
  vs_benchmark: {
    open_rate: "above" | "at" | "below"
    reply_rate: "above" | "at" | "below"
    bounce_rate: "healthy" | "concerning" | "critical"
    assessment: string                 # One-line summary
  }
  by_touch: [
    {
      touch_number: integer
      sent: integer
      open_rate: percentage
      reply_rate: percentage
      marginal_reply_rate: percentage
      reply_contribution: percentage   # What % of total replies came from this touch
    }
  ]
  by_variant: [
    {
      touch_number: integer
      variant_id: string
      subject: string                  # For quick reference
      sent: integer
      open_rate: percentage
      reply_rate: percentage
      confidence: string               # "insufficient", "directional", "likely", "significant"
      recommendation: string           # "scale", "keep testing", "kill"
    }
  ] | null
  temporal: {
    best_day: string
    best_time: string
    avg_open_to_reply_hours: float
    velocity_trend: "accelerating" | "steady" | "decelerating"
  } | null
}

replies: [...]                        # From Step 1 campaign_data.replies
your_company: {
  description: string
  pain_point: string
  icp_definition: string
}

reply_analysis: {
  total_replies: integer
  classification: [
    { category: string, count: integer, percentage: percentage }
  ]
  reply_quality_score: "strong" | "mixed" | "weak" | "toxic"
  reply_quality_reasoning: string
 
  objection_patterns: {
    top_objection: { type: string, count: integer, example_quotes: string[] }
    objections_by_touch: [ { touch: integer, objection_type: string, count: integer } ]
    handleable_objections: [ { type: string, count: integer, suggested_handle: string } ]
    terminal_objections: [ { type: string, count: integer, implication: string } ]
  }
 
  positive_signals: {
    interest_triggers: [ { touch: integer, variant: string | null, pattern: string } ]
    common_responder_traits: string     # What positive responders have in common
    resonating_phrases: string[]        # Exact quotes from positive replies
    common_questions: string[]          # Questions warm leads are asking
  }
 
  notable_replies: [                    # 5-10 most instructive replies
    {
      category: string
      sender_info: string               # Title @ Company
      quote: string                     # Key excerpt
      insight: string                   # What this tells us
    }
  ]
}

sequence: [...]                       # From Step 1 campaign_data.sequence (the actual emails)
quantitative_analysis: { ... }        # From Step 2 (which touches/variants perform)
reply_analysis: { ... }               # From Step 3 (what replies say about the copy)
your_company: {
  description: string
  pain_point: string
  proof_points: string[]
  cta_goal: string
}

copy_assessment: {
  overall_grade: "A" | "B" | "C" | "D" | "F"
  overall_summary: string              # 2-3 sentence assessment
 
  subject_lines: [
    {
      touch: integer
      variant: string
      subject: string
      open_rate: percentage
      verdict: "working" | "underperforming" | "spam_risk"
      issues: string[]
      suggested_alternative: string | null
    }
  ]
 
  email_bodies: [
    {
      touch: integer
      variant: string
      word_count: integer
      hook_quality: "strong" | "adequate" | "weak"
      value_prop_clarity: "clear" | "muddled" | "missing"
      proof_usage: "strong" | "weak" | "none"
      personalization_level: "tier_1" | "tier_2" | "tier_3" | "none"
      cta_quality: "clear_low_friction" | "clear_high_friction" | "unclear" | "missing"
      issues: string[]
      rewrite_suggestions: string[]
    }
  ]
 
  sequence_architecture: {
    touch_count_verdict: string
    timing_verdict: string
    angle_diversity_verdict: string
    overall_flow: string
  }
 
  strongest_element: string            # What's working best in the copy
  weakest_element: string              # What needs the most work
}

lead_list_summary: { ... } | null     # From Step 1 (if available)
reply_analysis: { ... }               # From Step 3
quantitative_analysis: { ... }        # From Step 2
icp_definition: string                # From config

lead_quality: {
  overall_grade: "A" | "B" | "C" | "D" | "F"
  overall_summary: string
 
  targeting_assessment: {
    title_fit: "aligned" | "partially_aligned" | "misaligned"
    industry_fit: "aligned" | "partially_aligned" | "misaligned"
    issues: string[]
  }
 
  signal_quality: {
    primary_signal: string              # What the reply patterns suggest about targeting
    evidence: string[]                  # Specific data points
  }
 
  responder_profile: {
    most_responsive_titles: string[]
    most_responsive_industries: string[]
    actual_vs_intended_icp: string      # How the responding audience differs from targeting
  } | null
 
  recommendations: string[]
}

quantitative_analysis: { ... }        # From Step 2
reply_analysis: { ... }               # From Step 3
copy_assessment: { ... }              # From Step 4
lead_quality: { ... }                 # From Step 5
benchmarks: { ... }                   # Calibrated in Step 2

# Sequence Performance Review: [Campaign Name]
**Period:** [date range] | **Status:** [active/paused/completed]
 
---
 
## Executive Summary
 
**Overall verdict:** [One sentence: "This campaign is [performing well / underperforming / in trouble] because [key reason]."]
 
| Dimension | Grade | One-Line Assessment |
|-----------|-------|-------------------|
| Metrics | [A-F] | [e.g., "Reply rate 2x benchmark — strong performance"] |
| Copy Quality | [A-F] | [e.g., "Touch 1 is strong, Touch 2-3 need new angles"] |
| Lead Quality | [A-F] | [e.g., "Targeting is tight but volume is too low"] |
| Reply Quality | [Strong/Mixed/Weak/Toxic] | [e.g., "60% positive — objections are handleable"] |
 
### What's Working (Double Down)
- [Specific thing that's working, with data]
- [Another]
 
### What's Not Working (Fix or Kill)
- [Specific thing that's failing, with data]
- [Another]
 
### Top 3 Actions
1. [Highest-impact action with expected result]
2. [Second]
3. [Third]
 
---
 
## Detailed Metrics
 
### Overall Performance
| Metric | Actual | Benchmark | Status |
|--------|--------|-----------|--------|
| Emails sent | X | — | — |
| Deliverability | X% | >97% | [flag] |
| Open rate | X% | Y% | [above/below] |
| Reply rate | X% | Y% | [above/below] |
| Positive reply rate | X% | Y% | [above/below] |
| Bounce rate | X% | <3% | [flag] |
| Meeting conversion | X% | — | — |
 
### Performance by Touch
| Touch | Sent | Open Rate | Reply Rate | Marginal Reply Rate | % of Total Replies |
|-------|------|-----------|------------|--------------------|--------------------|
| 1 | X | Y% | Z% | Z% | W% |
| 2 | X | Y% | Z% | Z% | W% |
| 3 | X | Y% | Z% | Z% | W% |
 
[Commentary: which touches are carrying the campaign? Is Touch 2+ adding value or wasting sends?]
 
### Variant Performance (if A/B testing)
| Touch | Variant | Subject | Sent | Open Rate | Reply Rate | Confidence | Action |
|-------|---------|---------|------|-----------|------------|------------|--------|
| 1 | A | "..." | X | Y% | Z% | Likely | Scale |
| 1 | B | "..." | X | Y% | Z% | Likely | Kill |
 
---
 
## Reply Deep Dive
 
### Reply Classification
| Category | Count | % of Replies | Trend |
|----------|-------|-------------|-------|
| Positive interest | X | Y% | — |
| Meeting request | X | Y% | — |
| Warm / Curious | X | Y% | — |
| Objection — Timing | X | Y% | — |
| Objection — Relevance | X | Y% | [flag if high] |
| Not interested | X | Y% | — |
| Auto-reply / OOO | X | Y% | — |
 
### Top Objections
| Objection | Count | Handleable? | Suggested Response |
|-----------|-------|------------|-------------------|
| [objection] | X | Yes/No | [how to handle or what it implies] |
 
### Notable Replies
[5-10 most instructive replies with quotes and what they teach us]
 
---
 
## Copy Assessment
 
### Subject Lines
| Touch | Subject | Open Rate | Verdict | Issue |
|-------|---------|-----------|---------|-------|
| 1 | "..." | X% | Working | — |
| 2 | "..." | X% | Underperforming | Too generic |
 
### Email Body
| Touch | Grade | Hook | Value Prop | Proof | Personalization | CTA |
|-------|-------|------|-----------|-------|----------------|-----|
| 1 | B+ | Strong | Clear | One proof point | Tier 2 | Clear, low-friction |
| 2 | C | Weak ("just following up") | Repeat of T1 | None new | Tier 1 | Same CTA as T1 |
 
[Specific rewrite suggestions for underperforming touches]
 
### Sequence Architecture
- Touch count: [verdict]
- Timing: [verdict]
- Angle diversity: [verdict]
- Overall flow: [verdict]
 
---
 
## Lead Quality
 
### Targeting
| Dimension | Intended | Actual (from responders) | Fit |
|-----------|---------|------------------------|-----|
| Titles | VP Sales, CRO | Sales Managers (mostly) | Partial |
| Industries | SaaS, FinTech | SaaS (90%) | Aligned |
| Company size | 50-500 | 20-200 | Shift down |
 
[Commentary on targeting gaps]
 
---
 
## Recommendations (Prioritized)
 
### High Priority (Do This Week)
1. **[Action]** — [Data point] → [Expected impact]
2. **[Action]** — [Data point] → [Expected impact]
 
### Medium Priority (Do This Month)
3. **[Action]** — [Data point] → [Expected impact]
4. **[Action]** — [Data point] → [Expected impact]
 
### Low Priority (Backlog)
5. **[Action]** — [Data point]
 
### Kill List
- [Anything that should be stopped — a variant, a touch, a segment]

report: {
  executive_summary: string           # Markdown formatted
  detailed_report: string             # Markdown formatted
  recommendations: [
    {
      priority: "high" | "medium" | "low" | "kill"
      category: "subject_line" | "body_copy" | "targeting" | "sequence" | "variant" | "infrastructure" | "list_hygiene"
      action: string
      data_point: string
      expected_impact: string
    }
  ]
  rewrite_suggestions: [              # Ready-to-use copy alternatives
    {
      touch: integer
      element: "subject" | "body" | "cta"
      current: string
      suggested: string
      rationale: string
    }
  ] | null
}

[Executive Summary rendered]
 
---
 
Full detailed report available with:
- Touch-by-touch breakdown with open/reply rates
- Every reply classified with quotes
- Copy scored on 6 dimensions per touch
- Lead quality assessment with actual vs intended ICP
- Prioritized recommendations with rewrite suggestions
 
Want to see the full report? Or act on a specific recommendation?