About our guest — Laurens Nys

Laurens Nys is the founder of GTM Sigma, a studio that builds AI-led GTM systems. He is a prominent voice in the GTM engineering community, known for his practical job descriptions for the role and his advocacy for dynamic, signal-based TAM lists. Laurens is an expert in N8N a workflow automation tool, and uses it to create powerful and efficient GTM motions.

He is passionate about building systems that help companies acquire, retain, and grow their customer base. Laurens — also a lead instructor at GTM Engineer School cohort 2 — is a a strong proponent of using automation to drive efficiency and growth.

Core takeaways

GTM Engineering from First Principles: A GTM engineer is someone who builds systems to get, keep, or make customers worth more. It’s about applying an engineering mindset to the entire go-to-market process.

The Convergence of Factors: The rise of GTM engineering is the result of several factors, including the end of the “growth at all costs” era, the increasing importance of efficiency, and the advent of AI.

The System is Key: The specific CRM or tools used are less important than the underlying system. As long as the tools have a decent API, a GTM engineer can build an effective system around them.

The Intelligence Layer: The core of a modern GTM stack is the intelligence layer, which for Laurens is N8N coupled with AI. This is where the data is processed, and the decisions are made.

Finding the Constraint: To effectively design a GTM system, it’s crucial to identify the bottleneck in the customer journey. This allows the GTM engineer to focus their efforts on the area that will have the greatest impact.

Top quotes

On GTM engineering: “A GTM engineer is just someone that builds systems to get, keep, or make customers worth more.”

On the importance of systems: “I don’t really care about CRM as long as I can interact with it, meaning it has an API that’s not complete s**t. I’m fine.”

On the modern GTM stack: “You have a database where obviously all the data lives. And then two, you have kind of the intelligence layer or kind of the brain of the operation. And for me, that’s N8N coupled to either workflows or some sort of an AI system.”

On identifying the bottleneck: “The top of the bottle usually is the bottleneck. So outbound is a very common one.”

Referenced tools and resources

CRM: Atio, HubSpot

LLM: OpenAI (ChatGPT), Claude

Enrichment/Scraping: Bright Data, Rapid API

Workflow Automation: N8N

Timestamps

(02:08) Laurens’ definition of GTM engineering

(03:07) The factors that led to the rise of GTM engineering: efficiency and AI

(04:33) Lightning Round: Favorite CRM (Atio, HubSpot)

(05:13) Lightning Round: Top LLM (OpenAI/ChatGPT, Claude)

(05:46) Lightning Round: Top enrichment tool (Bright Data, Rapid API)

(06:54) Laurens’ top overall GTM engineering tool (N8N)

(07:09) The most underrated GTM engineering tool (Bright Data)

(08:22) The building blocks of Laurens’ GTM stack: database, intelligence layer (N8N), and interaction layer

(10:14) Identifying the bottleneck in the customer journey

(12:53) A deep dive into a GTM play for a company with a planning API

(16:19) A walkthrough of the N8N workflow for the planning API use case

(20:27) How to get good at N8N: project-based learning

(21:53) Emerging skills for GTM engineers: GTM knowledge and technical fundamentals

(23:16) The importance of mental models and learning how to think

(26:18) Advice for aspiring GTM engineers: figure out your skill gaps and fill them

(28:42) Why Laurens switched from Clay to N8N

(29:48) How to build maintainable GTM systems in a rapidly changing tool landscape

(31:33) The future of GTM engineering: will we be rebuilding systems every year?

How to connect with Laurens

GTM Sigma

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