Stratégie de consultant en génération de la demande

Built a demand gen engine from scratch. $0 to $4M pipeline in 6 months. Here's the exact playbook: The Starting Point: Company: Series A B2B SaaS, $3M ARR Marketing: 1 person (me, consultant, 20 hours/week) Budget: $30K/month Goal: Generate 60 SQLs/month Timeline: 6 months The Demand Gen Framework: Most companies spray and pray. I built a systematic engine. Layer 1: Demand Capture (40% of budget) Target: People actively searching for solution Tactics: - Google Ads (search, not display): $12K/month - Targeted keywords: "B2B lead qualification," "sales engagement platform" - Landing pages: Specific to each keyword group (not generic homepage) - Offer: Free trial + demo Month 1-2 Results: - 180 trials started - 47 demos booked - 28 SQLs created - Cost per SQL: $214 Layer 2: Demand Creation (30% of budget) Target: People who have the problem but aren't searching yet Tactics: - LinkedIn ads: $9K/month - Targeting: VP Sales, CRO at B2B SaaS 50-500 employees - Offer: "Pipeline Generation Framework" ebook - Nurture: 6-week email sequence + retargeting Month 1-2 Results: - 410 ebook downloads - 89 demo requests (from nurture sequence) - 31 SQLs created - Cost per SQL: $290 Layer 3: Demand Expansion (20% of budget) Target: Existing customers and their networks Tactics: - Customer webinars: $3K/month - Invite customers + their peers - Topic: How [Customer] built predictable pipeline - Offer: See the platform in action Month 1-2 Results: - 340 registrations (customers invited peers) - 180 attendees - 41 demos booked - 19 SQLs created - Cost per SQL: $158 Layer 4: Demand Acceleration (10% of budget) Target: In-market accounts showing intent Tactics: - 6sense intent data: $3K/month - Identify accounts researching competitors - Personalized outreach sequences - SDR + marketing collaboration Month 1-2 Results: - 89 high-intent accounts identified - 47 engaged - 12 SQLs created - Cost per SQL: $250 The Full-Funnel View: Month 1-2 (Foundation): - MQLs: 640 - SQLs: 90 - Pipeline: $720K - Cost per SQL: $233 Month 3-4 (Optimization): - Paused: Bottom 30% of ad spend (low converters) - Added: More budget to top performers - Improved: Landing page conversion (2.1% → 3.8%) - Result: SQLs jumped to 140/month, cost per SQL dropped to $186 Month 5-6 (Scale): - MQLs: 890/month - SQLs: 168/month - Pipeline generated: $1.8M/month - Cost per SQL: $179 6-Month Totals: - Total MQLs: 3,880 - Total SQLs: 648 - Total pipeline: $4.2M - Average deal size: $65K - Total marketing spend: $180K - Cost per SQL: $208 - Pipeline ROI: 23x Ce qui a fait fonctionner cela : ✅ Multi-channel approach: Captured demand everywhere (search, social, community) ✅ Layered strategy: Capture + Create + Expand + Accelerate ✅ Ruthless optimization: Cut what didn't work, doubled down on winners ✅ Sales alignment: Weekly sync on lead quality, adjusted targeting ✅ Full-funnel thinking: Didn't optimize for MQLs, optimized for SQLs Common Mistakes I Avoided: ❌ Focusing only on top of funnel: Generated MQLs without conversion focus ❌ Single-channel dependence: Diversified risk across 4 layers ❌ Set-it-and-forget-it campaigns: Optimized weekly ❌ Vanity metrics: Measured SQLs and pipeline, not clicks and impressions The Demand Gen Lesson: Pipeline doesn't come from one tactic. It comes from a system. Capture + Create + Expand + Accelerate = Predictable pipeline.