There is a disease plaguing the new wave of GTM engineers.
It happens when a smart, capable operator gets access to the modern stack. They open Clay and immediately obsess over building the most impressive table possible. They open n8n and start wiring up nodes to build elaborate workflows. They measure their success by how many columns they can fill, how many API keys they can connect, and how complicated their workflow logic looks.
This is a mistake. It leads to “high-effort spam” emails that are technically personalized, but strategically empty. We will discuss why this happens later in the chapter “how to hire and get hired as a gtm engineer”. Let’s focus on research.
Research is not about data collection. It is about argument formation.
For leaders, the mandate is clear: stop being impressed by the complexity of AI automation. Start measuring the clarity of the insight. For operators, the shift is fundamental: stop thinking like a data entry clerk and start thinking like a detective.
Here is how to flip your process from tool-first to message-first:
The logical workflow: message first, data second
The biggest mistake GTM engineers make is starting with the list. They scrape 1,000 leads and then ask, “What can I say to these people?”
This is backward. You must start with a blank document.
Write the “God Mode” message: the email you would send if you knew everything about the prospect. The message that is so specific, so relevant, that if you sent it to the CEO, they would have no choice but to reply.
Once you have written that message, work backward. Look at the text and identify the dynamic variables. They will fall into two buckets:
1. Definitive Data (The Commodity) This is static information. First name. Job title. Company location.
- Difficulty: Low.
- Value: Low.
- Source: Any database or simple enrichment column.
2. Hypothesis Data (The Gold) This is data that requires synthesis.
- Variable: “Bleeding money on legacy licenses.”
- Variable: “Internal teams drowning in manual work.”
- Variable: “Cannot close the VP of Engineering role.”
This data cannot be bought. It must be engineered. You have to perform activities like running multiple searches, cross-referencing Glassdoor reviews with job posts, and doing the math.
Your job is to design the system that finds the second bucket.
The parrot test
How do you know if your research is actually valuable? You run it through the Parrot Test.
A parrot can repeat what it sees. “Hey, I saw you raised $25M.” “Hey, I saw you use SAP.” “I saw you are hiring.”
This is not research. This is proof of literacy. You are telling the prospect facts they already know about themselves.
Research is about the implication, not the observation.
If you are selling an agentic AI ERP solution, do not write: “I see you use Oracle, we can help you save 30%.” That is lazy. Percentages are vague; they feel like marketing fluff.
- The Parrot: “I see you use Oracle.”
- The Researcher: “Based on your finance headcount of 450 and your current on-premise Olocal stack, you are likely spending ~$6.2M annually on maintenance fees alone. Our model replaces this legacy overhead and puts $2.1M back into your budget in Year 1.”
You have to do the math. You have to estimate the volume, calculate the cost incurred, and present an exact approximation of the dollar amount of savings. That is the difference between a nuisance and a consultant.
Front-loading discovery
The goal of modern GTM research is to make the first 20 minutes of a traditional discovery call obsolete.
In the old world, sales reps used the first call to ask basic questions: “What is your tech stack? Are you happy with it? Are you struggling to hire?”
A GTM Engineer finds these answers before the email goes out.
If you are pitching a modernization project, do not guess. Verify.
- Glassdoor: Are employees complaining about “slow systems” or “manual data entry”? (Symptom of technical debt).
- Customer Reviews: Are users complaining about billing errors or slow support? (Symptom of broken internal ops).
- Job History: Did they repost the same technical role three times in six months? Did they increase the salary on the last post?
If you see a salary hike on a reposted job, you have confirmed two things:
- They cannot close the position.
- The urgency is real and costing them money every day the seat is empty.
Now you know that you shouldn’t just reach out to the CTO; you need to include the Talent Acquisition team in your decision-making unit (DMU) because you are solving their headache too.
Selecting the offer (the sniper approach)
The ultimate payoff of the research mindset is that it kills the “Menu Approach.”
Lazy sales is sending a list of everything you do: “We do X, Y, Z, A, B, and C.”
When you front-load your research, you don’t send a menu. You send the specific solution they need right now.
- If your research shows they are bleeding money on legacy licenses, you pitch the $2.1M cost-savings offer.
- If your research shows their teams are burned out and leaving, you pitch the automation/retention offer.
You use research to segment your list not by who they are, but by what they need.
The future: why research is the new discovery
We are living through a moment where the cost of intelligence has dropped to near zero.
Five years ago, the level of research I just described: calculating exact license waste, analyzing hiring patterns, mapping internal tech stacks was a luxury. It was called Account Based Marketing (ABM). It was expensive, manual, and reserved for your top 100 strategic accounts.
Today, you have phenomenal tools that give you superpowers. You can replicate that “white glove” ABM effect across 500 or 1,000 accounts, not just ten.
This changes the physics of the sales cycle.
For Leaders: You must stop accepting “blind discovery” as a standard operating procedure. If your rep gets on a call and asks, “So, tell me about your current tech stack,” the research has failed them. They have wasted the prospect’s time and your money. Expect your GTM engineers to compress the sales cycle. The technology exists to move the fact-finding mission off the Zoom call and into the pre-work phase.
For Operators: Use the tools to shrink the distance between “hello” and “value.” When you front-load the research, you aren’t just showing off data. You are buying speed. You are allowing the prospect to skip the boring part of explaining their business to you and jump straight to the part where you solve their problem.The era of “spray and pray” is over. But so is the era of “purely manual research.” We are now in the era of Automated Relevance. You have the power to know many of the answers so ask deeper questions. Use it.
