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Get Started Free →Creates demand generation campaigns, optimizes paid ad spend across LinkedIn, Google, and Meta, develops SEO strategies, and structures partnership programs. Use when planning demand gen strategy, growth marketing, advertising campaigns, PPC optimization, lead generation, pipeline generation, or marketing budgets. Covers multi-channel acquisition (Google Ads, LinkedIn Ads, Meta Ads), CAC analysis, MQL/SQL workflows, attribution modeling, technical SEO, and co-marketing partnerships. Default cali
.claude/skills/alirezarezvani-marketing-demand-acquisition/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | 44% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 55% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 47% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 44% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 38% | 0% |
Acquisition playbook for Series A+ startups scaling internationally (EU/US/Canada) with hybrid PLG/Sales-Led motion.
Demand Gen: MQL/SQL volume, cost per opportunity, marketing-sourced pipeline $, MQL→SQL rate
Paid Media: CAC, ROAS, CPL, CPA, channel efficiency ratio
SEO: Organic sessions, non-brand traffic %, keyword rankings, technical health score
Partnerships: Partner-sourced pipeline $, partner CAC, co-marketing ROI
| Stage | Tactics | Target | |-------|---------|--------| | TOFU | Paid social, display, content syndication, SEO | Brand awareness, traffic | | MOFU | Paid search, retargeting, gated content, email nurture | MQLs, demo requests | | BOFU | Brand search, direct outreach, case studies, trials | SQLs, pipeline $ |
utm_source={channel} // linkedin, google, meta
utm_medium={type} // cpc, display, email
utm_campaign={campaign-id} // {qN-yyyy}-linkedin-enterprise
utm_content={variant} // ad-a, email-1
utm_term={keyword} // [paid search only]| Channel | Best For | CAC Range | Series A Priority | |---------|----------|-----------|-------------------| | LinkedIn Ads | B2B, Enterprise, ABM | $150-400 | High | | Google Search | High-intent, BOFU | $80-250 | High | | Google Display | Retargeting | $50-150 | Medium | | Meta Ads | SMB, visual products | $60-200 | Medium |
| Channel | Budget | Expected SQLs | |---------|--------|---------------| | LinkedIn | $15k | 10 | | Google Search | $12k | 20 | | Google Display | $5k | 5 | | Meta | $5k | 8 | | Partnerships | $3k | 5 |
See campaign-templates.md for detailed structures.
| Tier | Type | Volume | Priority | |------|------|--------|----------| | 1 | High-intent BOFU | 100-1k | First | | 2 | Solution-aware MOFU | 500-5k | Second | | 3 | Problem-aware TOFU | 1k-10k | Third |
| Tier | Type | Effort | ROI | |------|------|--------|-----| | 1 | Strategic integrations | High | Very high | | 2 | Affiliate partners | Medium | Medium-high | | 3 | Customer referrals | Low | Medium | | 4 | Marketplace listings | Medium | Low-medium |
See international-playbooks.md for regional tactics.
| Model | Use Case | |-------|----------| | First-Touch | Awareness campaigns | | Last-Touch | Direct response | | W-Shaped (40-20-40) | Hybrid PLG/Sales (recommended) |
| Metric | Target | |--------|--------| | MQLs | Weekly target | | SQLs | Weekly target | | MQL→SQL Rate | >15% | | Blended CAC | <$300 | | Pipeline Velocity | <60 days |
See attribution-guide.md for detailed setup.
| Script | Purpose | Usage | |--------|---------|-------| | calculate_cac.py | Calculate blended and channel CAC | python scripts/calculate_cac.py (no args — edit the example_data channel table in main() with your spend/customer numbers first) |
See hubspot-workflows.md for workflow templates.
| File | Content | |------|---------| | hubspot-workflows.md | Lead scoring, nurture, assignment workflows | | campaign-templates.md | LinkedIn, Google, Meta campaign structures | | international-playbooks.md | EU, US, Canada market tactics | | attribution-guide.md | Multi-touch attribution, dashboards, A/B testing |
| Metric | LinkedIn | Google Search | SEO | Email | |--------|----------|---------------|-----|-------| | CTR | 0.4-0.9% | 2-5% | 1-3% | 15-25% | | CVR | 1-3% | 3-7% | 2-5% | 2-5% | | CAC | $150-400 | $80-250 | $50-150 | $20-80 | | MQL→SQL | 10-20% | 15-25% | 12-22% | 8-15% |
Required:
✅ Job title: Director+ or budget authority
✅ Company size: 50-5000 employees
✅ Budget: $10k+ annual
✅ Timeline: Buying within 90 days
✅ Engagement: Demo requested or high-intent action| Handoff | Target | |---------|--------| | SDR responds to MQL | 4 hours | | AE books demo with SQL | 24 hours | | First demo scheduled | 3 business days |
Validation: Test lead through workflow, verify notifications and routing.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 24,134 | 20,061 | -17% | 1 | 1 | 0% | 4,721 | 6,273 | +33% | 0 | 0 | — |
case-02 | fail→fail | 18,570 | 18,046 | -3% | 1 | 1 | 0% | 3,329 | 5,980 | +80% | 0 | 0 | — |
case-03 | fail→pass | 19,666 | 13,515 | -31% | 1 | 1 | 0% | 3,436 | 4,943 | +44% | 0 | 0 | — |
case-04 | fail→fail | 15,975 | 13,566 | -15% | 1 | 1 | 0% | 3,303 | 5,215 | +58% | 0 | 0 | — |
case-05 | fail→fail | 15,988 | 14,472 | -9% | 1 | 1 | 0% | 2,963 | 4,996 | +69% | 0 | 0 | — |
case-06 | fail→pass | 14,586 | 8,796 | -40% | 1 | 1 | 0% | 2,604 | 4,044 | +55% | 0 | 0 | — |
case-07 | fail→pass | 11,924 | 5,832 | -51% | 1 | 1 | 0% | 2,434 | 3,576 | +47% | 0 | 0 | — |
case-08 | fail→pass | 15,059 | 7,751 | -49% | 1 | 1 | 0% | 2,700 | 3,891 | +44% | 0 | 0 | — |
case-09 | pass→pass | 12,730 | 16,409 | +29% | 1 | 1 | 0% | 2,333 | 5,406 | +132% | 0 | 0 | — |
case-10 | fail→pass | 13,665 | 4,956 | -64% | 1 | 1 | 0% | 2,417 | 3,328 | +38% | 0 | 0 | — |
case-11 | fail→pass | 12,432 | 7,601 | -39% | 1 | 1 | 0% | 2,514 | 3,898 | +55% | 0 | 0 | — |
case-12 | pass→pass | 13,436 | 4,118 | -69% | 1 | 1 | 0% | 2,406 | 3,200 | +33% | 0 | 0 | — |
case-13 | fail→pass | 16,174 | 5,735 | -65% | 1 | 1 | 0% | 3,003 | 3,527 | +17% | 0 | 0 | — |
case-14 | pass→pass | 14,209 | 10,301 | -28% | 1 | 1 | 0% | 2,512 | 4,446 | +77% | 0 | 0 | — |
case-15 | fail→pass | 10,973 | 3,640 | -67% | 1 | 1 | 0% | 2,430 | 3,136 | +29% | 0 | 0 | — |
case-16 | fail→fail | 16,170 | 12,185 | -25% | 1 | 1 | 0% | 2,672 | 4,742 | +77% | 0 | 0 | — |
case-17 | pass→pass | 15,066 | 13,454 | -11% | 1 | 1 | 0% | 2,684 | 4,963 | +85% | 0 | 0 | — |
case-18 | pass→pass | 14,138 | 9,181 | -35% | 1 | 1 | 0% | 2,447 | 4,025 | +64% | 0 | 0 | — |
case-19 | fail→pass | 12,459 | 9,770 | -22% | 1 | 1 | 0% | 2,185 | 4,195 | +92% | 0 | 0 | — |
case-20 | pass→fail | 10,344 | 10,262 | -1% | 1 | 1 | 0% | 2,126 | 4,358 | +105% | 0 | 0 | — |
case-21 | pass→pass | 7,247 | 5,925 | -18% | 1 | 1 | 0% | 1,412 | 3,471 | +146% | 0 | 0 | — |
case-22 | pass→pass | 8,313 | 4,710 | -43% | 1 | 1 | 0% | 1,630 | 3,425 | +110% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +36 percentage points is the difference between those two pass rates over the 22 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.