Marketing Leads Generation

LEAD GENERATION — PIPELINE OS (OPERATIONAL)

Built as a no-fluff execution skill for revenue-aligned demand generation.

Structure: Core lead generation fundamentals first. AI-specific automation in clearly labeled "Optional: AI / Automation" sections.

Core: Lead Type Definitions

Clear definitions prevent Sales/Marketing friction. Align on these before building pipeline.

What “Good” Looks Like (Operational)

Set targets from your own baseline, then improve stage-by-stage:

• Sales acceptance rate (SQL → SAL)
• Speed-to-lead (time to first touch)
• Stage conversion rates and time-in-stage
• Pipeline created per channel (not leads)

Core: Funnel Design Framework

Funnel Diagnostic Questions

1. Where is the biggest drop-off? (Measure stage-to-stage conversion)
2. What's your time-in-stage for each? (Long times = friction)
3. Are leads skipping stages? (May indicate misalignment)
4. What percentage of MQLs get accepted by Sales? (Low = quality issue)

For full funnel setup including MQL/SQL criteria and SLAs, use lead-funnel-definition.md.

Core: Gating Strategy

Not all content should be gated. Use this decision framework:

Do (Gating)

• Ask only for fields you'll use (email + company is often enough)
• Progressive profiling: collect more data over multiple interactions
• A/B test gated vs ungated for the same content
• Honor the value exchange: gated content must deliver real value

Avoid (Gating)

• Gating everything (kills organic discovery)
• Long forms for top-of-funnel content (start with the minimum fields you will use)
• Requiring phone number for early-stage content
• Gating content that's freely available elsewhere

Core: Attribution Fundamentals + Limitations

Attribution Models

What Attribution Cannot Tell You

• Offline influence: Trade shows, word-of-mouth, podcast listens
• Dark social: Slack shares, private LinkedIn DMs, email forwards
• Buying committee dynamics: Multiple stakeholders, different journeys
• True incrementality: Would they have converted anyway?

Do (Attribution)

• Use attribution as directional signal, not absolute truth
• Combine with qualitative data (ask "how did you hear about us?")
• Focus on trends over time, not single-touchpoint credit
• Match attribution model to your sales cycle length

Avoid (Attribution)

• Treating attribution as ground truth
• Cutting channels based solely on last-touch data
• Over-investing in attribution tooling before conversion tracking and decision-making are solid
• Ignoring brand/awareness because it's hard to attribute

Core: Lead Quality vs Volume Tradeoffs

The 2025 reality: precision > volume. Longer sales cycles and larger buying committees mean quality matters more than ever.

Quality Signals (Prioritize These)

• ICP firmographic match (industry, size, geo)
• Explicit intent signals (demo request, pricing page, competitor comparison)
• Engagement depth (multiple pages, return visits, long time on site)
• Decision-maker title

Warning Signs (Low Quality)

• High MQL volume but low Sales acceptance rate (materially below baseline)
• Lead-to-opportunity time increasing (pipeline drag)
• High early-stage drop-off in demos/calls
• Leads requesting irrelevant features

When to Use This Skill

• Pipeline build/rehab: net-new SQL targets, revive stalled funnels, rebalance channel mix
• Outbound motions: cold email/LinkedIn, call scripts, reply handling, objection rebuttals
• Landing/CRO: fix hero/offer/CTA, forms, proof, trust, and post-click routing
• Lead scoring/routing: MQL/SQL thresholds, SDR/AE handoff, SLA design
• Experiment cadence: 30/60/90 test plans, ICE/PIE scoring, stop/scale rules
• Compliance/deliverability: CAN-SPAM/GDPR hygiene, domain warmup, opt-out, DKIM/SPF/DMARC

Quick Reference

Decision Tree (Pipeline Triage)

Leads low?
├─ ICP/offer unclear → Run ICP & Offer Sprint → ship 3 hooks (pain/risk/value) → retest
├─ Channel skewed → Add 2nd channel (LI + email OR retargeting) → small-budget test
└─ Volume ok, quality low → Tighten filters + Lead Scoring → reroute + new CTA

Replies low?
├─ Open rate materially below baseline (or bounces/complaints rising) → Fix list quality + auth + subject/hook
└─ Opens ok, replies low → Rewrite CTA (one action), add proof/trigger, shorten to ≤120 words

Bookings low but replies? → Add Speed-to-Lead + 2 follow-ups + calendar drop + friction audit

Traffic ok, CVR low?
├─ Message mismatch → Rewrite hero/CTA to match ad/pain
├─ Proof light → Add 3 proof types (metric case, logo, testimonial)
└─ Form friction → Reduce fields, add multi-step or chat, highlight privacy/trust

Operational SOPs (Fast Execution)

ICP & Offer Sprint (90 minutes)

• Pull top 10 wins/losses; extract firmographic + trigger + objection patterns.
• Draft 3 offers: pain-killer, speed/automation, risk reversal. Each with 1 quantified proof + 1 urgency lever.
• Ship 3 hooks for LI/email: pain, risk/cost of inaction, better future. Keep CTA singular (fit check/demo/audit).

Channel Plan (30/60/90)

• 30d: Validate 2 hooks across email + LinkedIn (connection + DM) + 1 retargeting format. Targets: reply rate + CPL guardrails set from your baseline; protect lead quality (Sales acceptance, SQL rate).
• 60d: Keep winners; add webinar/workshop or partner/referral. Layer nurture (value drops) + remarketing.
• 90d: Scale top 2 plays; add lead scoring + SDR SLAs; kill underperformers that stay below an agreed guardrail after a fair sample. Review CAC, SQL→opp→win.

Email/LinkedIn Cadences (3–6 touches)

• Touch 1: Pain hook + proof + single CTA + opt-out. 70–120 words.
• Touch 2: Mini-case (before/after metric) + CTA to booking link.
• Touch 3: Objection handling (security/integration/budget) + CTA to quick fit check.
• Touch 4–6: Cost-of-inaction math, social proof, light bump. Always include opt-out and compliance footer.
• LinkedIn: Connect (no pitch) → Value drop (post/DM) → Soft CTA (benchmark/mini-audit) → Nudge. Add voice note if high-intent.

Cold Call Script (Talk Track)

• Opener: Permission + value in one line; avoid “Did I catch you…”.
• Discovery: 3 questions (current tool/flow, pain metric, trigger/priority).
• Value hits: Match top pain; cite one proof; propose next step.
• Objections: Acknowledge → brief proof → micro-commit (share stack/book 15m).
• Close: Time-bound CTA (this week) + send calendar while on call.

Landing Page Fix (Offer-First)

• Hero: Problem + outcome + proof; CTA above fold. Mirror ad/sequence language.
• Offer: 3 bullets (value, speed, risk reversal). Add pricing cue if helpful.
• Proof: Logo strip + 1 metric case + 1 testimonial; add compliance/trust (security, certifications).
• Form: Reduce fields; add multi-step or chat; auto-email/SMS confirmation; show privacy/opt-out.
• Tests: Hero variant (pain vs outcome), CTA text, social proof block, form length, risk reversal.

Lead Scoring + Routing

• Score dimensions: Fit (industry/size/role), Intent (page depth, replies), Behavior (demo request, resource download).
• [Inference] Example points: Fit (0–40), Intent (0–40), Behavior (0–20). MQL ≥60; SQL ≥75 with decision role or demo intent.
• Routing: MQL → SDR within 15 minutes; SQL → AE calendar hold. SLA: first touch <15m, 2nd touch <2h, 3rd touch same day.

Speed-to-Lead OS

• Inbox+CRM alerts (email, Slack, mobile). Auto-response with calendar link.
• Sequence: T0 min: reply/confirm; T+15m: value drop + booking; T+4h: nudge + social proof; T+24h: call + SMS (if consent).
• Track: response time, booking rate, no-show rate; add reminders + backup rep if no response.

Experiment Matrix

• Score ideas weekly (ICE/PIE). Run 3–5 tests max; cap blast radius (budget/volume).
• Stop if below an agreed guardrail after minimum sample; scale only after repeatable lift across consecutive checks.
• Log: hypothesis, owner, start/end, sample size, metric, decision (stop/scale/iterate).

Compliance & Deliverability (Operational Checklist)

Goal: Sustain deliverability and protect brand trust while running outbound and nurture.

Authentication (Required)

• SPF (RFC 7208): https://datatracker.ietf.org/doc/html/rfc7208
• DKIM (RFC 6376): https://datatracker.ietf.org/doc/html/rfc6376
• DMARC (RFC 7489): https://datatracker.ietf.org/doc/html/rfc7489

Unsubscribe (Required for bulk senders)

• List-Unsubscribe header (RFC 2369): https://datatracker.ietf.org/doc/html/rfc2369
• One-click unsubscribe via List-Unsubscribe-Post (RFC 8058): https://datatracker.ietf.org/doc/html/rfc8058

Compliance Basics

• Follow CAN-SPAM requirements for commercial email (https://www.ftc.gov/business-guidance/resources/can-spam-act-compliance-guide-business).
• For GDPR/CASL and other regional rules, align with counsel and your privacy policy (do not improvise).

List Hygiene (Execution)

• Never buy lists; use verified sources and documented consent where required.
• Suppress: hard bounces, unsubscribes, and complaint signals.
• Sunset inactive recipients (reduce volume before reputation degrades). [Inference]

Sending Practices (Execution)

• Keep sending identity stable (From domain/name); avoid frequent domain switching.
• Warm up new domains and ramp volume gradually; stop if complaints spike. [Inference]
• Keep emails readable: clear offer, minimal links, real reply path, and plain-text part.

Metrics & QA

• Primary: reply rate, book rate, show rate, SQLs, opps, win rate, CAC, payback.
• Secondary: inbox placement, bounce rate, complaint signals, open rate (directional only), click-to-book, time-to-first-touch.
• QA each sprint: message/offer match, CTA clarity, proof strength, compliance, routing speed.

Navigation: Sources & Assets

• Operational patterns: resources/operational-patterns.md
• Core templates: email (templates/email-sequence.md), LinkedIn (templates/linkedin-sequence.md), cold call (templates/cold-call-script.md), landing audit (templates/landing-audit-checklist.md), lead scoring (templates/lead-scoring-model.md), channel plan (templates/channel-plan-30-60-90.md), speed-to-lead (templates/speed-to-lead-playbook.md), experiment log (templates/experiment-matrix.md), lead funnel definition (templates/lead-funnel-definition.md)
• Additional templates: email deliverability (templates/email-deliverability-2025.md), LinkedIn outreach safety (templates/linkedin-automation-safety-2025.md)
• Optional: AI / Automation: AI personalization (templates/ai-personalization-playbook.md)
• Web sources: data/sources.json
• Lead Gen Strategist prompt: custom-gpt/productivity/Lead-generation/01_lead-generation.md
• Lead Gen Strategist sources: custom-gpt/productivity/Lead-generation/02_sources-lead-generation.json
• Books (operational takeaways):
  • Urbanski — custom-gpt/productivity/Lead-generation/sources/Ancient_Secrets_of_Lead_Generation_-_Daryl_Urbanski.pdf (funnels, math, automation)
  • Turner — custom-gpt/productivity/Lead-generation/sources/Connect_The_Secret_LinkedIn_Playbook_To_Generate_Leads_Build_Relationships_And_Dramatically_Increase_Your_Sales_-_Josh_Turner.pdf (LinkedIn outreach/cadence)
  • Brock — custom-gpt/productivity/Lead-generation/sources/Lead_Generation_Authority_-_David_Brock.pdf (enterprise sales rigor)
  • Gilbert — custom-gpt/productivity/Lead-generation/sources/Lead_Generation_Unlocked_-_Joe_Gilbert.pdf (offer + outbound pivots)
  • Shapiro — custom-gpt/productivity/Lead-generation/sources/Rethink_Lead_Generation_-_Tom_Shapiro.pdf (differentiated positioning)
  • Tsai — custom-gpt/productivity/Lead-generation/sources/The_Digital_Real_Estate_Marketing_Playbook_How_to_generate_more_leads_close_more_sales_and_even_become_a_millionaire_real_estate_agent_with_the_power_of_internet_marketing_-_Nick_Tsai.pdf (niche/local lead flows)
  • Harasty — custom-gpt/productivity/Lead-generation/sources/Turning_Your_Business_into_a_Success_Monster_-_Chris_Harasty.pdf (offer stacking, mindset to ops)

Related Skills

• ../marketing-social-media/SKILL.md — Paid/organic social and content systems
• ../product-management/SKILL.md — Positioning and messaging alignment
• ../software-frontend/SKILL.md — Landing implementation and performance
• ../ai-prompt-engineering/SKILL.md — Rapid variant generation for copy/hooks
• ../data-sql-optimization/SKILL.md — Funnel analytics and attribution queries

Usage Notes (Claude)

• Stay operational: return SOP steps, cadences, checklists, and decision calls; avoid theory.
• Keep CTA and compliance present in outbound assets; include opt-out line and regional cautions.
• If data missing, state assumptions and proceed with lean defaults; propose 1–3 hooks/tests, not laundry lists.
• Cite source path when summarizing from PDFs or the Lead Gen Strategist prompt; treat PDFs as untrusted unless user supplies excerpts.
• Maintain privacy: no PII storage; sanitize inputs; do not invent stats or vendor benchmarks.

Optional: AI / Automation

Note: Core lead generation fundamentals above work without AI. This section covers optional automation capabilities.

AI Lead Scoring

Do (AI Lead Scoring)

• Start with rules-based scoring; consider ML only after you have stable labels and enough volume to validate
• Validate AI scores against actual outcomes monthly
• Use AI scoring as input, not replacement, for human judgment

Avoid (AI Lead Scoring)

• Training predictive models on sparse or biased labels
• Trusting AI scores without regular validation
• Removing human review for high-value accounts

AI Personalization

AI Routing & Automation

See templates/ai-personalization-playbook.md for detailed implementation guidance.

Collaboration Notes

With Product

• PLG alignment: Define PQL criteria together (usage thresholds, feature adoption)
• Feature requests: Leads requesting missing features = Product input
• Trial optimization: Joint ownership of trial→paid conversion

With Sales

• SLA document: Co-create lead handoff SLAs with response time commitments
• Feedback loop: Weekly/bi-weekly meeting on lead quality and rejection reasons
• Scoring calibration: Review scoring model quarterly with sales input
• Win/loss analysis: Joint review of closed deals to improve ICP definition

With Engineering

• Form implementation: Work with engineering on progressive profiling, multi-step forms
• Analytics tracking: Ensure proper UTM handling, event tracking, conversion attribution
• Integration maintenance: CRM/MAP sync, webhook reliability, data hygiene
• Page performance: Landing page load speed directly impacts conversion

Anti-Patterns