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GTM (Go-To-Market) 101 Course | What's the point of making it if you don't make money?

Selling once is luck; selling repeatedly is strategy. Most teams miss out on 'potential buyers' because they fail to query the behavioral data they have already accumulated. This course connects the entire process into a single flow: selecting the market (ICP/Segment), designing value (Positioning/Pricing), and engineering a repeatable revenue structure through data (PQL/Triggers/RevOps). The core objective is not to memorize a "formula that always works." Instead, you will train your hands with transferable skills and theories (applications of management science, statistics, business administration, and marketing) that remain effective even when the company, product, or customer changes. Based on experience designing GTM for a profitable unicorn as a Senior GTM Strategy Manager at Gamma, we will deconstruct real-world PLG cases like Railway—not as "answers to copy," but as "mirrors to reflect theory." By the end of the course, you will be left with a system that actually works, rather than just plausible-looking documents.

4 learners are taking this course

Level Beginner

Course period 1 months

Service Planning
Service Planning
Project Management (PM)
Project Management (PM)
sales
sales
Business Plan
Business Plan
Information Strategy Planning
Information Strategy Planning
Service Planning
Service Planning
Project Management (PM)
Project Management (PM)
sales
sales
Business Plan
Business Plan
Information Strategy Planning
Information Strategy Planning

What you will gain after the course

  • GTM Playbook based on my product/company (ICP·Segment → Value·Pricing → Motion·Funnel → Metrics System)

  • Working PQL Scoring Notebook (Python) — Filtering signals with uplift analysis and implementing a 'readable linear score' manually

  • Event-based Trigger & Routing Workflow (CRM Integration + ICP × Behavioral Dual Conditions)

  • Revenue Measurement Dashboard — MRR Movement, Cohort, NRR, Simple Forecast

  • Practical ability to model behavioral data into account-level signals using SQL and dbt

  • The judgment to detect and correct errors, biases, and label leakage in plausible AI-generated outputs.

  • Pricing Plan Strategy Principles and Theories of Various Companies

  • Deliverables that can be applied directly to a company or used as a portfolio for GTM Engineer/RevOps position applications.

Selling once is luck; selling repeatedly is strategy.

Did your company's product sell last year? If it did — was that luck, or strategy?

It sells through personal connections, it sells by getting lucky with media coverage, and it sells because the CEO is out there pounding the pavement. The problem comes after that. Does the second, third, and hundredth sale happen with the same structure? If not, that's not revenue—it's just an episode.

This lecture teaches the job role that bridges that gap — GTM (Go-To-Market). It consists of 8 core lectures — 4 on design and 4 on engineering — where you will learn to select a market, design value, and directly engineer a recurring revenue structure using data.


Why your product doesn't sell repeatedly

Most companies suffer from two types of illnesses.

Vision Sickness — selling only dreams without revenue. Conference Room Sickness — repeating internal discussions without going out into the market. Both grow in places where "cold market validation" is missing.

And a more decisive truth. It's not that things aren't selling because you lack data. They aren't selling because you aren't asking questions of the data. About 75% of PLG companies already have user behavior data but do not use it for lead scoring. It's like having an oil field but not drilling. This course helps you bridge that connection—between data and questions—with your own hands.


This course does not sell a 'formula'

Lectures that say "Just do this and your sales will increase" are a scam.

Your company and the company next door have different products, different customers, and different markets, timing, and data. When all of these factors are different, can a law that works unconditionally exist? Logically, it is impossible.

That is why this lecture does not teach a formula for copying others' conclusions, but rather transferable skills and theories that allow you to redesign even when the context changes. Academically, GTM stands on four pillars — Management Science (decision-making, optimization, measurement), Statistics (signal filtering, uplift, experimentation, causality), Business Administration (strategy, organization, pricing, finance), and Marketing (positioning, segmentation, channels).

All the corporate case studies in this course (Gamma, Railway, Coupang, Slack, Zoom, Figma, Remember, L-Box, Peloton, Samsung, Watcha) are not 'answers to copy,' but 'mirrors reflecting theory.' Instead of saying "Let's do it like Gamma," we take away the way of thinking Gamma used to apply it to their own context.


Recommended for the following people

  • Founders and PMs who have sold once but are relying on luck because they haven't built a 'structure for repeatable sales'

  • Marketers, sales, and growth professionals who are accumulating white paper downloads (MQLs) but seeing no conversions, yet have never once questioned the behavioral data

  • Teams that miss the launch timing by repeating internal discussions and visions without going out into the market

  • Now that job boundaries are blurring due to AI, PMs who want to transition into GTM/RevOps using market analysis, strategy, and data capabilities

  • Data and analytics professionals who have the data but cannot connect it to 'so, to whom and when should we sell'

Especially for PMs: Now that AI is handling spec documents, prototyping, and user stories, your next moat is market analysis, financials, and strategic switching. The most natural next step for someone with those three skills is GTM. A tech background is not required.


What you will have upon completion

It is not just a document, but a set of 4 functioning systems. You can apply them directly to your company or use them as a portfolio when applying for GTM Engineer or RevOps positions.

  1. GTM Playbook — ICP/Segment → Value/Pricing → Motion/Funnel → Metrics Framework

  2. PQL Scoring Notebook (Python) — Functional code that scores which users are likely to become enterprise customers

  3. Event Trigger & Routing Workflow — Automation that sends automatic messages and routes to sales based on scores

  4. Revenue Measurement Dashboard — Measure recurring revenue with MRR, Cohort, and NRR

You will be able to say "This is the system I built," rather than just "I took the course." If you don't have your own company data, you can proceed in the same way using the sample PLG SaaS for the course, 'OZ Deploy'.


The decisive reason why this lecture is different

① It was designed on the premise of using AI. In an era where AI can spit out a plausible ICP in just 5 minutes, grading the output is merely grading one's 'copy-paste ability.' Therefore, the evaluation axis has been shifted from output → process, judgment, and verification. There is only one core question.

"Did you notice what was wrong with the plausible answer provided by the AI? And why did you correct it that way?"

② We do not ban the use of AI. We ban hiding it. All assignments must be submitted with an 'AI Collaboration Log' (prompts, original responses, corrected parts, and reasons). Submitting AI results uncritically leads to point deductions, while identifying and fixing errors, biases, or label leakage results in high scores. An honest log is your very own learning data.

③ We distinguish real skills through an 'error planting test.' When given a plausible but incorrect GTM output, you must identify what is wrong and why based on theory. AI is not good at catching its own errors — this is where human judgment is revealed.

④ Korea is currently a 'GTM pre-emption market.' While there are hundreds to thousands of GTM positions in California and New York, you can count them on one hand in Seoul. The problems are the same, but the job category doesn't exist yet — if this isn't a pre-emption opportunity, what is? (For reference, the lower bound for global GTM salaries is about $320K, with Head-level positions reaching around $400K.)


Curriculum — 8 Core Lectures, from Design to Engineering

An Orientation is at the beginning, a Synthesis/Capstone is at the end, and 8 core lectures fill the space in between.

Orientation — How to use this course: Luck vs. Strategy, AI evaluation principles, Capstone guide

🧭 PART 1 — GTM Design (Lectures 1–4): Finalizing "What to sell to whom, for how much, and how"

  • Lecture 1. What is GTM? — Roles, Scope, and Organization. GTM is Who, What, How, and When, but not Where and Why. RACI, Vision-itis, and Meeting-room-itis.

  • Lecture 2. Market and Customers — ICP, Customer Layering, JTBD. The 3F Trap, "How was it solved when it didn't exist?"

  • Lecture 3. Value, Positioning, and Pricing — Pricing first, PMF second, estimating WTP, market dominance → pricing freedom

  • Lecture 4. GTM Motion, Funnel, and Metrics — Choosing PLG/SLG/PLS, the trap of MRR·ARR vs MAU·Downloads

🛠️ PART 2 — GTM Engineering & RevOps (Lectures 5–8): Turning Strategy into an Operational System

  • Lecture 5. Data Stack & Modeling — Turning behavior into account-level signals with SQL and dbt. "There is oil, but no drilling."

  • Lecture 6. The Science of Signals: Uplift, Label Leakage, and PQL (The heart of this course) — Why readable linear scores beat complex ML

  • Lecture 7. Triggers, Automation, and Handoff — Drip → Event Triggers, ICP × Behavior Dual Conditions, the Structure of Increasing Open Rates from 27% to 50–70%

  • Lecture 8. Revenue Operations, Growth, and Experiments — MRR Movement, Cohorts, NRR, and A/B Causal Inference

🎯 Comprehensive · Capstone — Validation without a product, global expansion (linear equation Korea vs. N-th degree equation), Capstone presentation + Peer review


What you will learn

Since it's a VOD, the order is flexible. However, please maintain the dependencies — Lecture 6 assumes the data from Lecture 5, and Lecture 7 assumes the scores from Lecture 6. Every lecture is completed as a triple set of Video + Resource Package (Concept Handouts, Templates, Notebooks, Case Studies, Evaluation Sets) + Evaluation. The goal is to bring every student up to Skill Achievement Level 3 (Correction) or higher. Because AI already handles Level 2 (Application).


Instructor — Kwangseop Ahn (Oswarld / Haebom)

I am not someone who only digs into theory, but someone who has personally established products in the Korean market and designed GTM strategies for Silicon Valley companies.

  • Notion Korea Launch & Community Lead (2017~, 5+ years) — Experiencing the essence of GTM in the field to successfully establish products in the market

  • Gamma Senior GTM Strategy Manager — Designing new feature launches, market expansion strategies, and playbooks for Silicon Valley products

  • Kakao Brain (AI), Nexon (Gaming), Hanwha Life (Finance), Reckitt Benckiser (CPG) — Accumulated the 'ability to read the market' while crossing diverse industries

  • Current) Adjunct Professor at Sejong University (Business Analytics · Management Data Management), OBF Senior Lead Consultant

  • Master's in Technology Management & MBA from Korea University / Author of Notion books & AI research paper (HEMA)

In a single sentence: "Create signals, not noise." Picking out the real signals from a sea of data — that is the way I work, and it is what this course (Lecture 6, The Science of Signals) teaches.


Pre-course Information

Prerequisite Knowledge — Spreadsheet basics (Required) / SQL SELECT·JOIN·GROUP BY (Recommended, warm-up provided) / Python basics (Recommended, notebook templates provided) / Statistics as needed within the lecture.

Tool Stack — BigQuery·PostgreSQL / dbt / Python·Hex / PostHog·Amplitude / HubSpot·Salesforce / Customer.io·n8n·Clay. Practice is conducted with the sample dataset 'OZ Deploy', which can be replaced with your own company data.

Not recommended for these people — Those who want a "magic formula that works just by following it." This course does not give you the answer, but the skill to design the answer yourself.


After completing these 8 lectures, you will

You will have a working system, not just a document. And you will stand at the starting line as a first-generation GTM—a position that is still vacant in Korea.

Selling once is luck. Repeating it is the strategy you will learn from now on.


Recommended for
these people

Who is this course right for?

  • Founders and PMs who have sold once but are relying on luck because they haven't built a 'structure for repeat sales'

  • Marketers, sales, and growth professionals who only accumulate white paper downloads (MQLs) without conversions, yet have never once queried their behavioral data.

  • A team that misses the launch timing by repeatedly engaging in internal discussions and vision-setting (meeting-room sickness and vision sickness) instead of going out into the market.

  • A PM seeking to transition into GTM/RevOps using market analysis, strategy, and data capabilities at a time when job boundaries are blurring due to AI.

  • Data and analytics professionals who have the data but fail to connect it to 'who to sell to and when'

  • Builders who have created a product or service but don't know how to sell it

Need to know before starting?

  • Spreadsheet Basics (Pivots & Functions)

  • SQL SELECT/JOIN/GROUP BY experience (warm-up materials provided)

  • Python Basic Syntax

  • This will proceed under the assumption that an AI service (such as ChatGPT, Claude, Gemini, or any other) is being used.

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