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David Ibia
ea570af409
- Move the vendored skills from .config/opencode/skills to .config/omo/skills, with provenance and local changes in its README - Rewrite Impeccable's script paths to ~/.omo/agent/skills/impeccable so its detector and live-server verbs resolve under omo - frontend-workflow hands screenshots to a visual-engineering task instead of opencode run - Move the notes commands and /impeccable to .config/omo/prompts as omo prompt templates; setup-mcp stays behind because it writes opencode.json - Archive DEVELOPMENT-WORKFLOW.md and UI-WORKFLOW.md under .config/omo/archive; nothing loads them - link-ai-configs.sh links the skills into omo and Claude Code only and stops linking OpenCode's config, TUI, plugins and workflow docs - setup-mac.sh and Brewfile.apps stop installing OpenCode; README and .withcontextconfig.jsonc follow the new layout ~/.config/opencode/AGENTS.md stays linked: omo's rules engine reads it before ~/.claude/CLAUDE.md and stops at the first hit, so it carries the shared preferences and keeps OMC's instructions out of omo.
88 lines
6.3 KiB
Markdown
88 lines
6.3 KiB
Markdown
# Benchmarks, Data & Expert Methods
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## Core Performance Metrics
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Almost every public cold email benchmark comes from a vendor that sells sending, data, or agency services, and each measures differently (some count auto-replies as replies, some don't). Use these as ranges, and judge yourself against your own baseline.
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| Metric | Average | Good | Excellent | Source |
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|--------|---------|------|-----------|--------|
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| Reply rate | ~3.4% | 5.5%+ (top quartile) | 10%+ (top decile) | Instantly, 2025 sends (published 2026) |
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| Reply rate (signal-based, small segments) | — | 2–3× your static-list rate | — | Inferred from several 2025–26 vendor datasets; claims of 15–25% are unverified |
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| Share of replies from email 1 | ~58% | — | — | Instantly 2026 |
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| Meetings booked | 0.5–1% of contacts | 1–2% | 2.5%+ | Reachoutly, Cleverly |
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| Bounce rate | — | under 2% | under 1% | Mailbox-provider guidance; see deliverability.md |
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| Spam complaints | — | under 0.1% | — | Google sender guidelines |
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**Don't use open rate.** Apple Mail Privacy Protection opens tracked emails automatically, many filters treat the tracking pixel as a spam signal, and one large sender dataset found reply rates roughly doubled with open tracking off. Measure replies, positive replies, and meetings.
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## Realistic Funnel Model
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1,000 contacts → ~34 replies (3.4%) → ~10–15 positive (30–45% of replies) → ~5–8 meetings → 1–2 customers. Roughly **100–200 contacts per meeting** on a static list; signal-based segments often need 30–60 (an estimate from the ranges above, not a measured benchmark).
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## Performance Levers (ranked by impact)
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1. **Hook type** — Timeline hooks outperform problem hooks by 3.4x in meetings
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2. **Personalization depth** — Up to 250% more replies
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3. **Brevity** — 25–75 words optimal, 83% more replies under 75 words
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4. **Targeting precision** — ≤50 contacts per campaign = 2.76x higher reply rates
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5. **Follow-up strategy** — First follow-up adds 49% more replies
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6. **Reading level** — 3rd–5th grade = 67% more replies
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7. **Send timing** — Thursday peaks at 6.87% reply rate
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## Declining Effectiveness Trend
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Reply rates fell from 7–8% (2020–2022) to roughly 3.5–5.8% (2024–2025 datasets). Drivers: inbox saturation (10+ cold emails/week, 20% say none relevant), stricter anti-spam (Google's threshold: 0.1% complaints), AI email flood (more volume, less quality signal). Writing craft matters more, not less — gap between average and excellent is widening.
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## Response Rates by Seniority
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- **Entry-level:** Highest engagement at 8% reply, 50% open
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- **C-level:** 23% more likely to respond than non-C-suite when they engage (6.4% vs 5.2%)
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- **CTOs/VP Tech:** 7.68% reply
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- **CEOs/Founders:** 7.63% reply
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- **Heads of Sales:** 6.60% (most targeted role, highest saturation)
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## Industry Variation
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**Highest responding:** Nonprofits (16.5%+), legal (10%), EdTech (7.8%), chemical (7.3%), manufacturing (6.1%).
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**Lowest responding:** SaaS (3.5%), financial services (3.4%), IT services (3.5%).
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## Top 15 Mistakes (ranked by impact)
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1. **Too long** — 70% of emails above 10th-grade level. Under 75 words = 83% more replies
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2. **Too self-focused** — "We are a leading..." signals sales pitch. Count I/We sentences
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3. **No clear value prop** — 71% of decision-makers ignore irrelevant emails
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4. **Generic templates** — {{FirstName}} isn't personalization. Recipients detect instantly
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5. **Feature dumping** — "Great reps lead with problems" (Lavender). One proof point beats ten features
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6. **False personalization** — "Loved your post!" without specifics is transparent
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7. **Asking too much too soon** — 30-min call in first email = "proposing on first date"
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8. **Pushy language** — "Act Now" stacking increases spam flagging by 67%
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9. **No CTA** — Without a clear next step, momentum dies
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10. **"Just checking in" follow-ups** — "I never heard back" = 12% drop in bookings
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11. **Wrong tone for audience** — Founder ≠ RevOps lead ≠ sales leader
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12. **Jargon/buzzwords** — "Leverage synergistic platform" → "We help you book more meetings"
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13. **Unsubstantiated claims** — "300% more leads" without proof triggers skepticism
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14. **Too many contacts per company** — 1–2 people = 7.8% reply; 10+ = 3.8%
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15. **Fake urgency** — Fake "Re:" / "Fwd:" / countdown timers destroy trust
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## Cultural Calibration
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| Factor | US | UK | Germany/DACH | Scandinavia |
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| ------------ | --------------- | ------------------------ | -------------------- | ----------------------- |
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| Tone | Direct, casual | Polite, professional | Precise, data-driven | Fact-based, egalitarian |
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| Length | Shorter, blunt | Longer, insight-led | Detail-oriented | Concise but substantive |
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| Social proof | Outcome numbers | Research-led credibility | Technical precision | Shared values |
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North America: 4.1% response. Europe: 3.1%. Asia-Pacific: 2.8%. Shorter, more direct sequences work better in US. UK needs more insight/personality. GDPR affects European tone.
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## Expert Quick Reference
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| Expert | Core Method | Best For |
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| -------------- | --------------------------------------------------------------- | ----------------------------------------------- |
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| Alex Berman | 3C's: Compliment → Case Study → CTA | High-ticket B2B services, agencies |
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| Josh Braun | "Poke the Bear" — neutral questions exposing invisible problems | Empathy-driven consultative selling |
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| Kyle Coleman | Systematic research + AI personalization at scale | Bridging mass outreach and deep personalization |
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| Becc Holland | Psychographic personalization, Premise Buckets | Combining personalization with relevance |
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| Will Allred | Data-driven coaching, Mouse Trap, Vanilla Ice Cream | Any context; universal frameworks |
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| Justin Michael | 1–3 sentence hyper-brevity, quote their own words | High-velocity SDR teams at scale |
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| Sam Nelson | Agoge Sequence — Triple on Day 1 (email + LinkedIn + call) | Multi-channel, tiered personalization |
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