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.

| Lead Type | Definition | Qualification Criteria | Owner | |-----------|------------|----------------------|-------| | Lead | Any identified contact | Has email/phone, some interest signal | Marketing | | MQL (Marketing Qualified Lead) | Fits ICP + engaged with marketing | Firmographic fit + behavior threshold | Marketing | | SQL (Sales Qualified Lead) | Ready for sales conversation | MQL + explicit buying signal or demo request | Sales | | PQL (Product Qualified Lead) | Used product, shows upgrade potential | Trial/freemium + usage threshold | Product + Sales | | SAL (Sales Accepted Lead) | SQL accepted by sales rep | Sales confirms qualification after first contact | Sales |

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

| Stage | User State | Content/Action | Goal | |-------|-----------|----------------|------| | Awareness | Problem-aware | Blog, social, SEO, ads | Capture attention | | Interest | Solution-curious | Guides, webinars, comparisons | Capture contact info | | Consideration | Evaluating options | Case studies, demos, free tools | Convert to MQL | | Decision | Ready to buy | Pricing, proposals, trials | Convert to SQL → Opportunity | | Activation | New customer | Onboarding, training, quick wins | Reduce churn, increase expansion |

Funnel Diagnostic Questions

Where is the biggest drop-off? (Measure stage-to-stage conversion)
What's your time-in-stage for each? (Long times = friction)
Are leads skipping stages? (May indicate misalignment)
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:

| Content Type | Gate? | Why | |--------------|-------|-----| | Blog posts, how-to guides | No | Build SEO, trust, awareness | | Comparison guides, buyers guides | Light gate (email only) | High intent, worth capturing | | Industry reports, original research | Gate | High value, worth exchange | | ROI calculators, assessments | Gate | Strong buying signals | | Product demos, pricing | Gate | Direct sales intent | | Case studies | Optional | Gate if detailed; ungate if brief |

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

| Model | How It Works | Best For | Limitation | |-------|--------------|----------|------------| | First-touch | 100% credit to first interaction | Understanding awareness sources | Ignores nurture journey | | Last-touch | 100% credit to final touch | Understanding closing sources | Ignores awareness | | Linear | Equal credit to all touches | Simple multi-touch | Over-credits low-value touches | | Time-decay | More credit to recent touches | Long sales cycles | Complex to implement | | Position-based | 40/20/40 to first/middle/last | Balanced view | Still somewhat arbitrary |

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.

| Strategy | Quality | Volume | Best When | |----------|---------|--------|-----------| | Volume play | Lower | Higher | New market, testing channels, brand building | | Precision play | Higher | Lower | Known ICP, limited SDR capacity, high ACV | | Balanced | Medium | Medium | Most B2B companies |

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

Core: Account-Based Sales (ABS)

ABS is often effective in B2B when targeting high-value accounts with complex buying committees.

When to Use ABS

| Criteria | Threshold | Why | | -------- | --------- | --- | | ACV | >$25K | Worth the research investment | | TAM | <5,000 accounts | Finite, targetable market | | Buying committee | 3+ stakeholders | Multi-threaded approach needed | | Sales cycle | >60 days | Time to nurture relationships |

ABS Execution Framework

| Element | Execution | Resource | | ------- | --------- | -------- | | Target list | 50-200 named accounts, tiered (Tier 1: 20, Tier 2: 50, Tier 3: 130) | assets/channel-plan-30-60-90.md | | Account research | Pain points, tech stack, recent news, org chart | 30 min per Tier 1 account | | Multi-threading | 3-5 contacts per account across roles | Champion + economic buyer + user | | Custom content | Pain-specific messaging per tier | Tier 1: fully custom; Tier 2: semi-custom | | Orchestration | Coordinated email + LinkedIn + ads + events | Sequence all channels | | Measurement | Account engagement score, pipeline per account | Add to assets/lead-scoring-model.md |

Do (ABS)

Start with Tier 1 (highest value) to prove the motion
Coordinate Sales + Marketing on account selection and messaging
Use intent data to prioritize accounts showing buying signals
Track account-level metrics, not just lead-level

Avoid (ABS)

Running ABS on >200 accounts (becomes spray-and-pray)
Treating ABS as "just personalized email" (it's full orchestration)
Skipping account research (generic outreach defeats the purpose)
Single-threading accounts (champion leaves = deal dies)

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
Account-based sales (ABS): named account targeting, multi-threaded outreach, account scoring

When NOT to Use This Skill

Use related skills instead for:

Organic content strategy → marketing-social-media
SEO for landing pages → marketing-seo-complete
AI search optimization → marketing-ai-search-optimization
Product-led growth ownership → product-management
Paid media buying/optimization → marketing-paid-advertising

Quick Reference

| Task | SOP/Template | Location | When to Use | |------|--------------|----------|-------------| | Define ICP + Offer | ICP & Offer Sprint | See Operational SOPs → ICP & Offer | Before messaging, bidding, or list-building | | Channel Plan 30/60/90 | Test Plan Grid | See Operational SOPs → Channel Plan | New market motion or quarterly reset | | Email/LinkedIn Cadence | 5-touch skeleton (CTA-first) | See Operational SOPs → Email/LinkedIn Cadences | Cold/prospecting or nurture | | Cold Call Script | Talk track w/ discovery | See Operational SOPs → Cold Call Script | Live outbound, event follow-up | | Landing Fix | Hero/offer/proof/CTA/form checklist | See Operational SOPs → Landing Page Fix | Low CVR or ad-to-page mismatch | | Lead Scoring & Routing | Points + SLA | See Operational SOPs → Lead Scoring + Routing | SDR/AE handoff, CAC/SQL drift | | Speed-to-Lead OS | Response + reminders | See Operational SOPs → Speed-to-Lead | Reply/no-show issues, inbox speed | | Experiment Matrix | ICE/PIE + stop/scale | See Operational SOPs → Experiment Matrix | Weekly prioritization | | Compliance/Deliverability | Authentication + opt-out | See Operational SOPs → Compliance & Deliverability | Cold email/domain health | | Email Deliverability 2025 | Bulk sender requirements | assets/email-deliverability-2025.md | Bulk sending (5,000+/day to Gmail), new domains | | LinkedIn Outreach Safety | Terms-compliant outreach guardrails | assets/linkedin-automation-safety-2025.md | LinkedIn outreach risk reduction |

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).

Pipeline Health Checklist (Weekly)

[ ] Confirm stage definitions (MQL/SQL/SAL) are unchanged (no silent drift).
[ ] Check SQL → SAL acceptance rate vs baseline; investigate top rejection reasons if down.
[ ] Check speed-to-lead median and p90 vs SLA; fix routing/alerts if breached.
[ ] Review bounce/complaint/unsubscribe trends; pause sends if complaints spike.
[ ] Verify list hygiene: suppress bounces/unsubs/complaints; remove role accounts where required.
[ ] Validate 2 outbound sequences against a control (reply rate and meeting rate), not opens/clicks.
[ ] Review landing page CVR vs baseline by top traffic sources; flag message mismatch.
[ ] Confirm forms capture only fields in use; remove any unused “nice-to-have” fields.
[ ] Audit routing: highest-intent leads go to humans first; bots/automation only assist.
[ ] Confirm attribution model is consistent this week (no reporting changes mid-period).
[ ] Inspect pipeline created per channel (not leads) and reallocate effort to top 2 plays.
[ ] Review show rate and no-show reasons; add reminders or friction fixes if slipping.
[ ] Pull 5 recent wins and 5 losses; update ICP triggers/objections accordingly.
[ ] Align with Sales on next-week target accounts (ABS) and the primary CTA per segment.
[ ] Document one change per channel (email/LI/landing) with a hypothesis and stop/scale rule.

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.

Spam Rate Thresholds (Critical — 2025 Enforcement)

Gmail/Yahoo/Microsoft hard ceiling: 0.3% complaint rate
Recommended target: <0.1% for reliable inbox placement
Gmail (Nov 2025): Non-compliant senders receive permanent 5xx rejections
Microsoft (May 2025): Bulk senders without auth are rejected outright on consumer mailboxes

See assets/email-deliverability-2025.md for full enforcement details.

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/references/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: references/operational-patterns.md
Core templates: email (assets/email-sequence.md), LinkedIn (assets/linkedin-sequence.md), cold call (assets/cold-call-script.md), landing audit (assets/landing-audit-checklist.md), lead scoring (assets/lead-scoring-model.md), channel plan (assets/channel-plan-30-60-90.md), speed-to-lead (assets/speed-to-lead-playbook.md), experiment log (assets/experiment-matrix.md), lead funnel definition (assets/lead-funnel-definition.md)
Additional templates: email deliverability (assets/email-deliverability-2025.md), LinkedIn outreach safety (assets/linkedin-automation-safety-2025.md)
Optional: AI / Automation: AI personalization (assets/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

| Use Case | Approach | Tools | |----------|----------|-------| | Predictive scoring | ML models on historical conversion data | Salesforce Einstein, HubSpot, 6sense | | Intent signals | Track research behavior across web | Bombora, G2, ZoomInfo Intent | | Enrichment | Auto-fill firmographic/technographic data | Clearbit, Apollo, ZoomInfo |

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

| Use Case | Approach | Consideration | |----------|----------|---------------| | Email personalization | LLM-generated variants | Test against control; maintain brand voice | | Dynamic content | Real-time page customization | Requires clean data; test load impact | | Video personalization | AI-generated custom videos | Novel but unproven ROI at scale |

AI Routing & Automation

| Use Case | Tools | Benefit | |----------|-------|---------| | Auto-routing | Chili Piper, Default, Calendly Routing | Faster lead response | | Chatbot qualification | Drift, Intercom, Qualified | 24/7 qualification | | Sequence automation | Outreach, SalesLoft, Apollo | Scale outbound |

See assets/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

International Markets

This skill uses US/UK market defaults. For international lead generation:

| Need | See Skill | |------|-----------| | Regional buying committee dynamics | marketing-geo-localization | | Regional channel preferences | marketing-geo-localization | | Compliance (GDPR, CASL, LGPD) | marketing-geo-localization | | Cultural outreach adaptation | marketing-geo-localization |

If your query involves international compliance or regional outreach norms, also use marketing-geo-localization for region-specific constraints and adaptations.

Anti-Patterns

| Anti-Pattern | Why It Fails | Instead | |--------------|--------------|---------| | MQL volume as success metric | High volume ≠ pipeline | Track MQL → SQL acceptance rate | | Buying lead lists | Poor quality, compliance risk, damages domain | Build organic + outbound to verified contacts | | Ignoring Sales feedback | MQLs rejected, trust erodes | Weekly sync on lead quality | | Over-automation | Generic outreach, low reply rates | Automate mechanics, personalize message | | Single-channel dependency | Algorithm changes kill pipeline | 2-3 channel minimum | | Gating everything | Kills SEO, frustrates prospects | Gate high-value, ungate awareness | | Chasing vanity metrics | Opens/clicks without conversions | Focus on reply rate, book rate, SQL | | No attribution model | Can't optimize spend | Start with simple model, iterate |