# Sequence Analyzer > Analyzes email sequence performance metrics. Evaluates open rates, click rates, reply rates, and conversion by step. Identifies drop-off points, benchmarks against industry averages, and recommends optimizations. - Skill: `ekatasingh1107/sequence-analyzer` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add ekatasingh1107/sequence-analyzer` - Raw SKILL.md: https://api.skillmd.com/api/skills/ekatasingh1107/sequence-analyzer/raw - Safety review: PASS (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Productivity - Author: ekatasingh1107 (https://skillmd.com/u/ekatasingh1107) - Updated: 2026-08-19 - Page: https://skillmd.com/skills/ekatasingh1107/sequence-analyzer --- # 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: ```json { "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: ```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"