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sequence-architecture
Design multi-step outbound sequences that convert. Timing, channel mix, variant strategy, and escalation logic for cold email campaigns.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Design multi-step outbound sequences that convert. Timing, channel mix, variant strategy, and escalation logic for cold email campaigns.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Benchmark your outbound campaigns against internal baselines, industry standards, and cross-portfolio performance data. Know if your campaigns are actually good — not just whether they feel good.
Analyze outbound campaign performance across every meaningful metric. Diagnose what's working, what's broken, and exactly where to optimize for higher conversion.
Extract, analyze, and replicate the patterns behind your highest-performing email copy. Turn individual campaign wins into repeatable copy frameworks your team can deploy across every campaign.
Battle-tested cold email copy frameworks that earn replies. Structures, formulas, and templates for writing emails that convert strangers into conversations.
Turn raw prospect data into compelling personalized email elements. Signal detection, personalization layers, and variable frameworks that make cold emails feel warm.
Systematically evaluate whether a target account is worth pursuing. Scoring frameworks, qualification criteria, and prioritization models that prevent wasted outreach on bad-fit prospects.
| name | sequence-architecture |
| description | Design multi-step outbound sequences that convert. Timing, channel mix, variant strategy, and escalation logic for cold email campaigns. |
This is the baseline architecture. Adapt timing and angles to your ICP.
| Step | Day | Channel | Objective | Angle Strategy |
|---|---|---|---|---|
| 1 | 0 | Open + Reply | Lead with strongest signal or observation | |
| 2 | 3-4 | Reply | New value: resource, benchmark, or case study | |
| 3 | 7-8 | Reply or Referral | Social proof or creative ideas angle | |
| 4 | 14 | Close or Redirect | Breakup ("should I close this out?") or referral ask |
Total campaign duration: 14 days Total touches: 4 emails
For high-value accounts or enterprise targets, layer in LinkedIn and phone:
| Step | Day | Channel | Objective |
|---|---|---|---|
| 1 | 0 | Signal-based opening | |
| 2 | 1 | Connection request with personalized note | |
| 3 | 3 | New value angle | |
| 4 | 5 | Engage with their content (like/comment) | |
| 5 | 7 | Case study or social proof | |
| 6 | 8 | Phone | "Following up on the email I sent" |
| 7 | 10 | Direct message with resource | |
| 8 | 14 | Breakup or referral |
Total campaign duration: 14 days Total touches: 8 (4 email + 3 LinkedIn + 1 phone)
Every email step should have 2-3 variants to enable testing:
| Variant Type | What You're Testing | Example |
|---|---|---|
| Subject variants | Which framing gets opens | A: {{company}} outbound vs B: quick question |
| Opening variants | Which personalization resonates | A: Signal-based vs B: Problem-based |
| CTA variants | Which ask gets replies | A: "Worth a look?" vs B: "Want me to send the playbook?" |
| Angle variants | Which value prop converts | A: ROI-focused vs B: Time-savings-focused |
Testing rules:
| Factor | Recommendation | Why |
|---|---|---|
| Send days | Tuesday-Thursday | Monday = inbox overload. Friday = checked out. |
| Send window | 8:00 AM - 11:00 AM prospect's timezone | Caught during morning email triage |
| Step gaps | 3-4 days between emails | Enough to not feel pushy, close enough to maintain context |
| Reply window | Check within 30 min of sends | Fast replies to "interested" responses 3x close rate |
| Match lead ESP | Enabled | Send from Gmail to Gmail, Outlook to Outlook — improves deliverability |
Configure these before launching any campaign:
| Setting | Value | Reason |
|---|---|---|
| Stop on reply | ON | Never email someone who already responded |
| Stop on auto-reply | OFF | Auto-replies (OOO) shouldn't kill the sequence |
| Text only | ON | HTML emails trigger spam filters more often |
| Link tracking | OFF | Tracking pixels and links hurt deliverability |
| Open tracking | OFF | Same — open tracking = invisible pixel = spam risk |
| Daily send limit | Per account limits | Respect your email provider's sending limits |
| Email gap | 10-20 minutes | Time between individual sends — avoids burst patterns |
| Random delay | 5-10 minutes | Adds randomness to look human |
What happens when the standard sequence doesn't convert:
Standard Sequence (14 days)
↓ No reply
Wait 30 days
↓
Re-engagement Sequence (different angle, 3 steps)
↓ No reply
Wait 60 days
↓
Trigger-based Re-entry
(Only re-enter if new signal detected: funding, hiring, tech change)
Never:
Use a consistent naming structure so performance data is analyzable:
{Client} - {Segment} - {Angle} - {Version}
Examples:
Acme - Enterprise VP Sales - Hiring Signal - v1Acme - SMB Founders - Referral Ceiling - v2Acme - Mid-Market Growth - Tech Stack Change - v1This makes it trivial to filter analytics by client, segment, or angle.
Is the target account high-value (>$50K ACV)?
├── YES → Extended Multi-Channel Sequence (8 touches, 14 days)
│ Layer email + LinkedIn + phone
│ Personalize every touch individually
│
└── NO → Standard 4-Step Email Sequence (4 touches, 14 days)
Use signal-based personalization at scale
Rely on variants for optimization
Is this a new persona/segment you haven't tested?
├── YES → Run 3 variants of Step 1 with 200+ sends each
│ Wait for data before building Steps 2-4
│
└── NO → Clone your best-performing sequence
Swap personalization for new segment
Keep winning structure intact
Pre-Launch:
- [ ] ICP and persona defined
- [ ] Lead list built and verified (bounce rate < 3%)
- [ ] Email accounts warmed (2+ weeks, 30+ emails/day)
- [ ] Sequence written (2-3 variants per step)
- [ ] Settings configured (tracking off, text-only, stop on reply)
- [ ] Naming convention applied
- [ ] Test send to yourself and team for QA
Post-Launch (Day 3):
- [ ] Check open rates (target: 60%+)
- [ ] Check bounce rate (must be < 3%)
- [ ] Check spam complaints (must be 0)
- [ ] Review any replies for pattern recognition
Post-Launch (Day 14):
- [ ] Calculate positive reply rate per variant
- [ ] Identify winning variants
- [ ] Document learnings
- [ ] Plan next iteration
Progressive disclosure: load channel-specific templates and industry playbooks only when building sequences for a specific campaign.