Sequence Analyzer

Evaluates the performance of email outreach and nurture sequences. Ingests per-email metrics (opens, clicks, replies, conversions, unsubscribes), identifies drop-off points and underperforming steps, benchmarks against industry averages for B2B services, and outputs actionable optimization recommendations.

Prerequisites

• agency.config.json populated (sequences, benchmarks)
• Sequence performance data: per-email metrics for at least one complete sequence run
• Data source: CRM export, email tool analytics, or manual input
• Optional: previous analyzer reports for trend comparison

Capabilities Used

1. crm-writer -- pull sequence engagement data from CRM
2. reply-analyzer -- analyze reply quality and sentiment
3. ab-test-analyzer -- evaluate A/B test results within sequences
4. email-copywriter -- rewrite underperforming emails

Phase 0: Intake

Read agency.config.json:

• sequences[] -- defined sequences with expected benchmarks
• outreach.daily_caps -- volume context
• benchmarks -- agency-specific benchmark targets if defined:

  {
    "benchmarks": {
      "cold_outreach": {
        "open_rate": 55,
        "click_rate": 5,
        "reply_rate": 8,
        "positive_reply_rate": 3,
        "meeting_rate": 2,
        "unsubscribe_rate": 0.5
      },
      "nurture": {
        "open_rate": 40,
        "click_rate": 4,
        "reply_rate": 5,
        "conversion_rate": 12,
        "unsubscribe_rate": 0.3
      }
    }
  }
  

Accept parameters:

• sequence_name -- (required) name of the sequence to analyze
• data_source -- crm | csv | manual. Default: crm
• date_range -- start and end dates for analysis period
• comparison_period -- previous period for trend analysis. Default: previous_equivalent
• include_rewrites -- boolean, generate improved copy for underperformers. Default: true
• granularity -- per_email | per_step | aggregate. Default: per_email

Phase 1: Data Collection

From CRM

Query CRM for sequence performance data:

For each email in the sequence:
  - Sent count
  - Delivered count (sent - bounced)
  - Open count (unique opens)
  - Click count (unique clicks)
  - Reply count (unique replies)
  - Positive reply count (interested / meeting booked)
  - Negative reply count (not interested / unsubscribe request)
  - Neutral reply count (OOO, wrong person, info request)
  - Unsubscribe count
  - Bounce count (hard + soft)
  - Conversion count (meeting booked / demo scheduled / proposal requested)

From CSV

Accept a CSV with columns:

step, subject, sent, delivered, opened, clicked, replied, positive_replies, negative_replies, unsubscribed, bounced, converted

Manual Input

Prompt for per-step data:

For sequence "[name]", provide metrics for each step:
Step 1: [subject line]
  Sent: ___  Opens: ___  Clicks: ___  Replies: ___  Meetings: ___  Unsubs: ___
Step 2: ...

Phase 2: Per-Email Analysis

For each email in the sequence, calculate:

Core Metrics

Step [N]: "[Subject Line]"
---
Sent: [count]
Delivered: [count] ([delivery_rate]%)
Opened: [count] ([open_rate]%)
Clicked: [count] ([click_rate]%)
Replied: [count] ([reply_rate]%)
  - Positive: [count] ([positive_rate]%)
  - Negative: [count] ([negative_rate]%)
  - Neutral: [count] ([neutral_rate]%)
Converted: [count] ([conversion_rate]%)
Unsubscribed: [count] ([unsubscribe_rate]%)
Bounced: [count] ([bounce_rate]%)

Step-Over-Step Decay

Calculate the drop-off between each step:

Step 1 -> Step 2: [X]% drop in opens, [Y]% drop in clicks
Step 2 -> Step 3: [X]% drop in opens, [Y]% drop in clicks
...

Benchmark Comparison

Compare each email against industry benchmarks:

Step [N] vs Benchmark:
  Open rate: [actual]% vs [benchmark]% -- [ABOVE/BELOW/AT benchmark]
  Click rate: [actual]% vs [benchmark]% -- [ABOVE/BELOW/AT benchmark]
  Reply rate: [actual]% vs [benchmark]% -- [ABOVE/BELOW/AT benchmark]

Flag emails performing >20% below benchmark as "underperforming." Flag emails performing >20% above benchmark as "outperforming."

Phase 3: Funnel Analysis

Sequence Funnel

SEQUENCE FUNNEL: [name]
===
             Sent: [count] (100%)
        Delivered: [count] ([rate]%)
           Opened: [count] ([rate]%)  -- at least one email opened
          Clicked: [count] ([rate]%)
          Replied: [count] ([rate]%)
Positive replies: [count] ([rate]%)
       Converted: [count] ([rate]%)

Drop-Off Identification

Identify the biggest drop-off points:

CRITICAL DROP-OFFS:
1. Step [N] -> Step [N+1]: [X]% drop -- [diagnosis]
2. Step [N] -> Step [N+1]: [X]% drop -- [diagnosis]

Drop-off diagnoses:

• Large open drop: subject line fatigue, send timing, list quality degradation
• Large click drop: CTA weak, content not compelling, link placement poor
• Large reply drop: ask too big, value prop unclear, timing wrong
• Spike in unsubs: content mismatch, frequency too high, tone wrong

Cumulative Performance

Total across all steps:
  Unique contacts reached: [count]
  Total emails sent: [count]
  Total opens: [count] (avg [rate]% per email)
  Total clicks: [count] (avg [rate]% per email)
  Total replies: [count] (total reply rate: [rate]%)
  Total conversions: [count] (sequence conversion rate: [rate]%)
  Total unsubscribes: [count] (sequence unsub rate: [rate]%)
  Cost per conversion: [if spend data available]

Phase 4: Content Analysis

Subject Line Performance

Rank all subject lines by open rate:

SUBJECT LINE RANKING:
1. "[subject]" -- [open_rate]% -- Style: [question/number/curiosity/direct]
2. "[subject]" -- [open_rate]% -- Style: [type]
3. "[subject]" -- [open_rate]% -- Style: [type]
...

Identify patterns:

• Which subject line styles perform best?
• What length performs best?
• Does personalization (first name, company) improve opens?

CTA Performance

Rank CTAs by click and reply rate:

CTA RANKING:
1. "[CTA text]" in Step [N] -- [click_rate]% clicks, [reply_rate]% replies
2. "[CTA text]" in Step [N] -- [click_rate]% clicks, [reply_rate]% replies
...

Identify patterns:

• Direct CTAs ("Book a call") vs soft CTAs ("Reply with thoughts")
• Link-based CTAs vs reply-based CTAs
• CTA placement (end of email vs inline vs PS)

Reply Quality

If reply-analyzer data available:

REPLY ANALYSIS:
  Total replies: [count]
  Positive (interested): [count] ([rate]%)
  Negative (not interested): [count] ([rate]%)
  Neutral (OOO, wrong person): [count] ([rate]%)
  Common positive signals: [list]
  Common objections in negative replies: [list]
  Steps generating most positive replies: [step numbers]

Phase 5: Trend Analysis

If comparison period data is available:

PERIOD-OVER-PERIOD COMPARISON
===
Metric          | Current | Previous | Change | Trend
Open rate       | [%]     | [%]      | [+/-]  | [up/down/flat]
Click rate      | [%]     | [%]      | [+/-]  | [up/down/flat]
Reply rate      | [%]     | [%]      | [+/-]  | [up/down/flat]
Conversion rate | [%]     | [%]      | [+/-]  | [up/down/flat]
Unsub rate      | [%]     | [%]      | [+/-]  | [up/down/flat]

Flag significant changes (>10% shift in either direction).

Phase 6: Optimization Recommendations

Based on analysis, generate specific, actionable recommendations:

Underperforming Emails

For each email flagged as underperforming:

STEP [N] OPTIMIZATION
---
Issue: [specific metric below benchmark]
Diagnosis: [likely cause based on patterns]
Recommendation: [specific change to make]
Priority: [HIGH/MEDIUM/LOW]

Rewrite (if include_rewrites = true):
  Original subject: "[old subject]"
  Recommended subject: "[new subject]" -- [reasoning]

  Original CTA: "[old CTA]"
  Recommended CTA: "[new CTA]" -- [reasoning]

  Body changes: [specific suggestions]

Sequence-Level Recommendations

SEQUENCE OPTIMIZATIONS
===

1. TIMING:
   - [Recommendation about send days/times based on open patterns]
   - [Recommendation about spacing between emails]

2. CONTENT:
   - [Recommendation about content types that work best]
   - [Recommendation about email length based on performance]
   - [Recommendation about personalization approach]

3. STRUCTURE:
   - [Should emails be added/removed?]
   - [Should the sequence be shortened/extended?]
   - [Should the CTA progression change?]

4. TARGETING:
   - [Are certain segments performing differently?]
   - [Should the sequence be split by segment?]

5. A/B TEST SUGGESTIONS:
   - Test 1: [subject line variant] -- Expected impact: [%]
   - Test 2: [CTA variant] -- Expected impact: [%]
   - Test 3: [timing variant] -- Expected impact: [%]

Phase 7: Output

Return structured JSON:

{
  "sequence_name": "cold_outreach_d2c_india",
  "analysis_date": "2026-03-07",
  "date_range": {"start": "2026-02-01", "end": "2026-02-28"},
  "summary": {
    "total_contacts": 200,
    "total_emails_sent": 950,
    "sequence_open_rate": 52.3,
    "sequence_click_rate": 4.8,
    "sequence_reply_rate": 7.2,
    "sequence_conversion_rate": 2.5,
    "sequence_unsub_rate": 0.4,
    "total_conversions": 5,
    "health": "GOOD",
    "vs_benchmark": "ABOVE_AVERAGE"
  },
  "per_email": [
    {
      "step": 1,
      "subject": "Quick question about your Shopify store",
      "sent": 200,
      "open_rate": 58.5,
      "click_rate": 6.2,
      "reply_rate": 4.5,
      "conversion_rate": 1.0,
      "unsub_rate": 0.5,
      "vs_benchmark": "ABOVE",
      "status": "OUTPERFORMING"
    }
  ],
  "drop_offs": [
    {
      "from_step": 3,
      "to_step": 4,
      "open_rate_drop": 18.5,
      "diagnosis": "Subject line fatigue -- Step 4 uses similar style to Step 3",
      "recommendation": "Change Step 4 subject line style from question to curiosity gap"
    }
  ],
  "top_performers": [
    {"step": 1, "metric": "open_rate", "value": 58.5, "reason": "Strong curiosity-based subject line"}
  ],
  "underperformers": [
    {"step": 4, "metric": "open_rate", "value": 38.2, "reason": "Subject line fatigue, poor timing"}
  ],
  "recommendations": [
    {
      "type": "rewrite",
      "step": 4,
      "priority": "HIGH",
      "change": "Replace subject line and lead with fresh angle",
      "expected_impact": "+8-12% open rate",
      "rewrite": {
        "original_subject": "Following up on my last email",
        "new_subject": "This cost [industry] brands 23% in lost revenue last quarter",
        "reasoning": "Pattern interrupt instead of follow-up framing"
      }
    }
  ],
  "ab_test_suggestions": [
    {
      "test": "Step 1 subject line: question vs statement",
      "hypothesis": "Direct statement may outperform question for enterprise segment",
      "sample_size_needed": 100,
      "duration": "2 weeks"
    }
  ],
  "trend": {
    "vs_previous_period": {
      "open_rate_change": "+3.2%",
      "reply_rate_change": "+1.1%",
      "conversion_change": "+0.5%"
    }
  },
  "generated_at": "2026-03-07T10:00:00Z"
}

Phase 8: Review

Present the analysis report.

APPROVAL GATE: "Sequence analysis complete. [N] optimization recommendations. Apply the rewrites?"

If approved:

• Update sequence emails via crm-writer
• Set up A/B tests for recommended variants
• Schedule next analysis for 2-4 weeks out
• Log baseline metrics for comparison

Example Usage

Trigger phrases:

• "Analyze the cold outreach sequence performance"
• "How is our nurture sequence doing?"
• "Sequence analytics for [sequence name]"
• "Which emails in the sequence are underperforming?"
• "Optimize our outreach sequence based on the numbers"
• "Run sequence analysis for February"
• "Compare this month's sequence performance to last month"