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How I drive growth

Demand generation for B2B SaaS: build a revenue channel, not a lead pile

Most demand gen is lead gen in a nicer outfit. It optimizes for MQL volume that sales quietly ignores. I build the other kind: a system that creates and captures demand and answers to pipeline and closed-won revenue.

Best for

B2B SaaS, seed to Series B

Where it fits

Zero-to-one and scaling

Proof

~⅓ of closed-won at Delightree

What most teams get wrong

They call it demand generation, but they are running lead generation. The goal quietly becomes MQL volume: gate every asset, count the form fills, pass the list to sales. Sales works the top slice, ignores the rest, and stops trusting marketing's numbers within a quarter.

Worse, nobody created any new demand. You harvested the people already searching for a tool like yours and called it a strategy. That works until the existing demand runs dry, and then the dashboard is green while the pipeline is empty. Demand generation has two jobs, and most programs only do the second one: create demand, then capture it.

How I think about it

01

Pipeline is the only scoreboard

Sourced and influenced pipeline, then closed-won. The moment you make MQL count the goal, it drifts away from revenue. Measure the thing you actually want.

02

Create demand, then capture it

Capture channels harvest people already looking. Creation channels make people look in the first place. A real engine runs both, and knows which is which.

03

Score and route so sales trusts the handoff

A lead score sales believes is worth more than a hundred they don't. Intent signals and clean routing make the handoff something reps act on, not file away.

04

Build it once, let it compound

An AI-native stack means the system keeps producing without more headcount. A small team ships the output of a large one, and the gains stack quarter over quarter.

How I actually do it

  • Stand up the full funnel in HubSpot: lead scoring, UTM architecture, buyer-intent tracking, and routing, so every touch is attributed and every lead lands in the right place.
  • Separate creation from capture. Capture demand with search, comparison pages, and retargeting. Create it with content, paid social, and category-shaping narrative.
  • Wire a feedback loop from closed-won back to spend, so budget follows the channels that actually produce revenue, not the ones that produce form fills.
  • Automate the grunt work with AI pipelines: SEO pages, case studies built from sales-call transcripts, and reporting, so the team spends its hours on judgment, not assembly.
Proof, not theory
Delightree

Zero to a third of revenue

As the first marketing hire at a Series A B2B SaaS company, I built the demand engine from a blank page. It now sources roughly a third of new closed-won revenue, and the AI-native stack behind it lets a tiny team operate like a large one.

~⅓of new revenue sourced 12xROAS on paid search 4-7xmore inbound qualified leads 5-7xmore self-serve demos
Read the demand engine case study

Questions I get asked

Lead generation captures people already in-market and hands sales a list of form fills. Demand generation creates new awareness and intent, then captures it, and is measured on pipeline and closed-won revenue rather than MQL volume. Most teams run lead gen and call it demand gen.

The instrumentation, lead scoring, and routing can be live in weeks. Sourcing a meaningful share of revenue is a multi-quarter build. At Delightree the function went from zero to roughly a third of new closed-won revenue over time.

Sourced and influenced pipeline, closed-won revenue, and the cost and conversion rate to get there. MQL counts are a proxy that drifts away from revenue the moment you optimize for them.

No. An AI-native stack lets a two-person team produce the output of a much larger one. The constraint is judgment and systems, not headcount.

Related capabilities

Marketing ops, attribution & CRO AI-native marketing

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