Adam Woozeer
I build the infrastructure that runs a revenue motion: enrichment, scoring, signals, routing, and the demand programmes on top of it. At Quantexa that meant $6.7M in influenced pipeline and 40 hours of ops work a week handed to automation. The engine and the programmes, one seat. That's Revenue Engineering.
STATUS: Open to Marketing Revenue Leadership · Founding Marketer roles · Portfolio →Everything below comes from shipped programmes at Quantexa, 2023–2026. Demand outcomes sit on the left, the infrastructure that produced them on the right. I built both.
GTM infrastructure on the left. Demand programmes on the right. The work that compounds lives at the intersection.
GTM Engineering covers the data and automation layer: enrichment pipelines, signal monitoring, ICP scoring, AI outbound. Demand Generation covers the programmes built on top of it: ABM, campaign execution, measurement, content systems. Most practitioners do one or the other. Revenue Engineering is what happens when the same person builds both and understands how each amplifies the other. That's the seat I work from.
"The creative concept and persona-based message, combined with the mixed use of tactics, both online and offline, have been critical to influence key stakeholders within our defined strategic accounts."
Most GTM stacks fail the same way: activation tools bolted onto dirty data. The sequences fire, the ads run, and the pipeline reaches the wrong accounts at the wrong time. I build from the bottom.
CRM hygiene, enrichment pipelines, data architecture. Every play above this layer runs on what it produces, and bad data here multiplies through everything above it.
ICP scoring, intent signals, propensity models. This layer turns raw data into a prioritised answer: which accounts, which contacts, when.
Signal-triggered outbound, ABM plays, inbound routing, CRM-native personalisation. Runs without anyone pressing a button. Humans stay in the loop where judgement matters: Tier 1 accounts and high-intent signals route to a rep, not a sequence.
The most common reason a GTM project underdelivers is a wrong brief. Before touching the stack, I map how revenue moves through the company and interview the CRO, VP Sales, and ops team separately. You get three different accounts of the same broken motion, and the gaps between them tell you where the problem lives. Four patterns come up more than any others:
Intent spikes, job changes, funding rounds: the signals sit in your data, and nothing catches them before the window closes. The motion only works when a rep happens to notice.
The fix: a signal layer that catches the trigger, scores the account, and routes it to a rep in minutes, not weeks.
A trial lands on a rep's desk as a name and a company. The firmographics, intent score, and engagement history exist in the stack; none of it travels. So the rep rebuilds context your systems already had.
The fix: an enrichment pipeline that ships full context with the lead. At one client it cut three hours of research per account to thirty seconds.
Assignment rules built for 50 leads a month break at 300. High-intent accounts land with the wrong rep, and everyone blames lead quality.
The fix: routing rebuilt on fit and signal instead of hand-made rules. One rebuild took accuracy from ~55% to 88%.
You have the target account list, the intent data, and the ad budget, and nothing connects them. No trigger logic, no coordinated sequence across channels, nothing to show the board.
The fix: trigger logic that wires list, intent, and spend into coordinated plays with measurement built in.
Whichever pattern is yours, the first deliverable is the same.
The first thing I deliver is an audit written for the CRO and VP Sales, not the ops team. It names the failure pattern, quantifies the revenue impact, and identifies the first lever to move. It applies whether you have broken infrastructure or none at all.
I work with revenue leaders who know something in the motion is broken but can't pinpoint where. The conversation starts with the business problem, and the diagnosis comes before the build.
I've built both sides of the divide. That's Revenue Engineering. GTM engineering: CRM foundations, enrichment pipelines, AI outbound, signal monitoring. Demand generation: ABM programmes owned end to end, from brief and creative direction through agency management and measurement. The Quantexa ABM work won two Golds and influenced $6.7M in pipeline; the automation behind it ran without me. Demand generation portfolio →
Explore the interactive GTM tools, or read the demand gen guide and ABM playbook.
I'm looking for Marketing Revenue Director or Head of Demand Generation seats at B2B SaaS companies, senior GTM engineering roles where the builder also owns the number, or a founding marketer seat at an earlier-stage company building its GTM engine from scratch. Either way, the fit test is the same: the person building the system has to understand the business problem first.
Marketing Revenue Director, demand gen, or founding marketer roles. CV, references, track record, one call away. Fifteen minutes tells you if it's a fit.
Tell me what's stalling. In 30 minutes I'll tell you which failure pattern it looks like and what the fix involves. Or run the audit first and I'll bring results.
The questions revenue leaders and hiring teams ask most.
The discipline of designing the systems a revenue motion runs on and the demand programmes that run on them. RevOps keeps that engine running day to day; Revenue Engineering designs, builds, and upgrades it. I lead it from the marketing side, which means the engine exists to create pipeline, not just report on it.
A GTM engineer builds the automated infrastructure that runs a revenue motion: enrichment, scoring, routing, and signal triggers. The system does the account research, routing, and follow-up that reps and ops teams would otherwise do by hand, so pipeline grows without adding headcount to match.
RevOps keeps the existing engine running. I design and upgrade it, then hand each system to RevOps to run. I work the design side, with the demand number attached.
Two weeks to a full diagnosis and system spec. Builds ship in stages after that, each with a defined outcome and documentation.
Clay, n8n, HubSpot, Salesforce for data and automation. Claude API for agents. Madison Logic and Influ2 for intent and activation. I pick tools to fit the motion.