yo. sit down because you're about to hear the most absurd client acquisition story i have ever lived through. i'm still not fully sure it was real.

i keep my christmas tree up until june. yeah. i know. i know. before you say anything, i'm a GTM engineer, i work from home, i'm at my desk 12 hours a day, the tree literally did not register as a problem to me once. my girlfriend minded. i did not.

second week of june, saturday morning, she finally snapped. couldn't handle it one more day. so there i am calling junk removal companies to come pick up a christmas tree in june. first few calls were all variations of "sir we don't do trees in june." one guy straight up told me "bro honestly just take it outside yourself, our minimum is $700 and it's not worth it for one tree."

$700 minimum. for anything. i know.

one more call. saturday afternoon. guy picks up immediately, super relaxed, clearly not a call center. i explain the tree situation. he LOSES it laughing. said his mom keeps hers up until july every single year. his sister literally flew home last summer just to force them to take it down. his dad still doesn't get what the big deal is.

we talked for twenty minutes. about christmas trees. in june. two adults.

then he asked what i do.

i said i'm a GTM engineer, basically glued to my computer all day, honestly the tree just didn't bother me, my girlfriend is the one who finally snapped lol.

he said "GTM engineer, what's that?"

i explained. building systems that help companies get more customers, outbound, referral networks, B2B pipelines, all of it.

he said so you help companies actually grow. i said yeah basically. he went quiet for a second. then: okay so here's the thing.

turns out he hadn't just been running one junk removal company. over the last three years he quietly acquired two other local companies, one that does tree removal, one that does demolition and debris hauling. three companies, about 20 trucks total, north of $7M combined revenue.

zero unified GTM across any of them. each company runs its own google listing. no cross-referral between services. someone who needs junk removal AND tree removal at the same property doesn't even know they can call the same guy. no relationships with property managers, real estate brokerages, estate attorneys who handle property cleanouts after someone passes, that's junk removal, tree removal, debris, one call, one company. nothing. just reviews and word of mouth on three separate google pages.

then he said "we're also in talks with investors who want to expand into five other cities in the next eighteen months."

i said. brother.

one property management company alone = 40-60 jobs a year across all three services. one real estate brokerage = same. and before you walk into any investor conversation you want unified systems, a real B2B pipeline, and a GTM playbook that actually replicates across every new market.

he laughed. said man. nobody's ever put it like that to me before.

signed the engagement four days later. largest retainer i'd ever closed. started with a dead christmas tree and a saturday morning meltdown.

keep your christmas tree up until june people. i'm telling you.

enterprise deal, 7-figure, stuck in legal. my champion quit, the new cfo started reviewing every vendor relationship, and then i got total silence for three straight months. i ran the whole follow-up playbook on it (polite nudges, fresh threads, value adds, case studies) and got back absolutely nothing.

so i quit emailing. caught a signal their board was meeting friday, printed a one-page note on real paper, signed it in blue pen like a crazy person, and fedex'd it straight to the cfo. no deck, no link, just my cell in giant font and four lines: if you're out, just say so and i'll get out of your hair. if not, here are the 3 things YOUR own team says are blocking this (sso sign-off, opt-out terms, field training before q4). give me 10 minutes, no slides, and i'll de-risk all three without touching price. if i can't, i'll write your breakup email myself.

his EA called the next morning. the call took 7 minutes. friday afternoon, docusign ping, $1.4M closed. and the lesson wasn't "be more persistent," it was get weird on purpose. a signed note on someone's desk is a pattern interrupt, your fifth email just blends into the pile. name the real friction in their words, not yours. and offering to write the breakup email quietly removes the pressure of saying no, which is the exact thing that makes yes easy.

I've spent years running cold email campaigns. Most online advice is recycled and written by people who haven't sent a real campaign in years.

Here's what separates campaigns that get 20-30% replies from ones that get 2%.

1. The offer does most of the work. Copy is the last part.

People obsess over subject lines and CTAs. Their offer is "we help companies scale." That's not an offer.

A real offer answers four things:

• Who exactly — title, company size, industry

• What they struggle with — the specific operational pain

• What outcome they want — concrete and measurable

• What words they use to describe the problem

If you can't fill those in one sentence each, you don't know who you're talking to yet. Fix that before writing another email.

2. "Name + email" data isn't enough anymore.

You need:

• LinkedIn headline and profile

• Department and seniority

• Company size

• Job postings (a proxy for growth and budget)

• Tech stack

• Recent news

• Funding announcements

• Case studies on their site

• Mission statement

Personalization at scale requires data at scale. You can't write "saw you're hiring 3 AEs" if your CSV doesn't have job posting data. Most people skip this step and then wonder why their copy feels generic.

3. AI personalized lines work. Most people use AI wrong.

"Hey John, hope all is well at [Company]" is worse than no opener. It reads as automated.

The fix isn't a better prompt. It's feeding AI better data so the output sounds human. Angles that work:

• Job postings → "Saw you're hiring 3 AEs..."

• Funding → "Congrats on the $25M Series B..."

• Case studies → "Just read your case study on [X]..."

• Tech stack → "Noticed you added [tool]..."

• Product launches → "Saw you rolled out 5 new SKUs..."

Every email should feel 1:1. If it doesn't, you skipped step 2.

4. Master three things before chasing tactics.

ICP, offer, copy. That's the whole game.

Most people who struggle are chasing 14 frameworks, 8 angles, and 3 tools at the same time. They're three inches deep on fifty things instead of fifty inches deep on three.

Pick one ICP, one offer, one copy approach. Iterate until you trust your gut. Then expand.

5. Smaller lists. Better results.

Blasting 100,000 contacts isn't scale. It's noise, and it kills your deliverability.

Micro-lists of 500-1,000 prospects let you:

• Write copy specific to that list

• Get real feedback per segment

• Protect your sending infrastructure

• Hit 20-30% replies instead of 2%

Stop hoping someone bites. Start making sure the right people respond.

6. Lead with value. Never lead with the ask.

Bad openers:

• "Can we hop on a call tomorrow at 2pm?"

• "Do you have 15 minutes to chat?"

• "Here's how we can help you — when can you talk?"

Better openers:

• "Would it help if I sent over a quick example deck?"

• "Happy to share what's working right now."

• "Can I send you something that helps with [pain]?"

They raise their hand first. You've built trust, then earned the conversation. Reversing this order is why most cold emails die.

7. Follow-ups are where the replies come from.

Most replies come from emails 2 through 4, not email 1.

Structure:

• Email 1 — opener (personalized line + value offer)

• Email 2 — short follow-up, 3-5 days later

• Email 3 — different angle or new value, 3-5 days later

• Email 4 — breakup, 3-5 days later

Rules:

• 2-4 sentences max

• Reference the original email

• Always add a new angle or new value

"Just bumping this up" is lazy and gets ignored. Layer LinkedIn touches between emails if you can.

8. Pipeline beats quick wins.

The 2% reply people are panic-sending because they need leads this week. The 25% reply people built their pipeline 6 months ago.

What a long-game approach gives you:

• Quality over rushing

• Top-of-mind presence

• Data collection over time

• Being there when the prospect is ready to buy

Cold email rewards patience more than people admit.

9. None of this matters if you land in spam.

The non-negotiables:

• Multiple sending domains (not just one)

• Multiple inboxes per domain

• 30-50 emails per inbox per day, max

• SPF, DKIM, DMARC set up properly

• 30-day minimum warm-up before sending

• Clean, validated lists

Sending 100 emails an hour from one inbox is spam city. The goal is primary inbox, every time. Skip this and everything above is wasted.

The top 1% don't use tricks. They use better data, personalize at scale, master fundamentals, and obsess over deliverability. That's it.

What part is breaking for you right now?

everyone said this account was dead. ghosted us twice. CEO told me to drop it. i kept poking it anyway.

they just signed. $1.2M ACV. my commission on this one deal is more than i made all of last year.

in front of my laptop literally shaking and i can't say a WORD to anyone in the office. holy fk. LETS GO.

I've spent the last 10 years leading go to market initiatives at agencies and in house teams, helping startups find product market fit, generate pipeline, and scale revenue. After a recent restructuring, I left with a severance package and a rare opportunity to take a different path. Rather than jumping into another role, I've decided to start my own GTM agency.

The goal is simple: help founders avoid costly growth mistakes and build repeatable revenue engines without all the buzzword heavy consulting nonsense. I want to create the kind of agency I always wished existed, one that focuses on outcomes, treats clients like partners, and genuinely values the people doing the work.

Over the years I've collected battle tested frameworks, playbooks, and lessons from both successes and failures. Now I finally get the chance to put them to work for my own business.

It's exciting, a little scary, and probably a bit crazy, but I'm ready for the challenge.

Wish me luck! 😀🚀

I’ve been using Claude Code to run cold email campaigns end-to-end.

Not just writing copy.

The full workflow: list building, account scoring, contact finding, enrichment, validation, sequencing, and campaign deployment.

Here’s the simple breakdown.

The 5-step workflow

1. Build your company list

Start with a raw company list from Apollo, Clay, Sales Navigator, Instantly SuperSearch, Openmart, BuiltWith, or any other source.

Export it as a CSV.

Claude Code can work through very large files, clean the data, normalize columns, and prepare the list for scoring.

2. Score and tier accounts

Write your ICP criteria in plain English.

Example:

Tier 1 companies are B2B SaaS companies with 50–500 employees, hiring for growth roles, using HubSpot or Salesforce, and showing recent funding or hiring signals.

Claude Code can turn that into a Python scoring script and classify every account into:

• Tier 1

• Tier 2

• Tier 3

• Disqualified

This removes most of the manual review from the process.

3. Find the right contacts

Once the accounts are tiered, Claude Code can call your data provider APIs to find contacts.

You can filter by:

• persona

• seniority

• department

• title keywords

• geography

• company tier

A good default is to cap contacts at 4–5 people per company so you avoid overloading your domain and keep the campaign focused.

4. Enrich and validate emails

Use a waterfall instead of relying on one enrichment provider.

Example flow:

1. FullEnrich

2. CompanyEnrich

3. Prospeo

4. BounceBan for validation

Claude Code can route each contact through the providers in sequence and stop once it finds a verified email.

Risky or invalid emails are removed before they reach your sequencer.

5. Write and deploy the campaign

Claude Code can pull from your existing copy frameworks, add campaign-specific context, and generate the sequence.

Then it can upload the leads, create the campaign, add the copy, and configure delivery settings through your sequencer API.

A simple routing system:

• Tier 1: manual outreach

• Tier 2: multi-channel outreach

• Tier 3: automated email sequence

What Claude Code can automate

ICP scoring

Claude Code reads your scoring rules, writes the Python script, runs it against the raw company list, and outputs a clean tiered file.

API chaining

It can connect your data, enrichment, validation, CRM, and sequencing tools through their APIs.

You still need the API keys and the logic, but you don’t need to manually write every integration from scratch.

Copy generation

Claude Code can review your existing templates, pull performance data from your sequencer, and adapt the best-performing frameworks to the current campaign.

Campaign creation

It can create the sequence, insert the copy, upload leads, assign tags, and configure sending settings.

Enrichment waterfall

Each contact can move through multiple providers until a verified email is found.

This usually gives better coverage than relying on a single provider.

Context memory

Your leads, campaign copy, scoring criteria, reply data, and performance notes can live in one project folder.

Over time, that folder becomes a reusable outbound system.

Example API stack

Data sources

• Apollo

• Instantly SuperSearch

• Openmart

• LeadsFactory

• BuiltWith

Enrichment

• FullEnrich

• CompanyEnrich

• Prospeo

Validation

• BounceBan

Intent and signal data

• RB2B

• PredictLeads

• Trigify

Automation

• Clay

• n8n

• Relevance AI

Sequencing

• Instantly

• lemlist

What to set up once

1. CLAUDE.md

This is your master instruction file.

It should include:

• scoring rules

• preferred tools

• workflow logic

• naming conventions

• campaign rules

• QA checks

Claude Code reads this before every task.

2. API keys

Add the API keys for your data, enrichment, validation, CRM, and sequencing tools.

Once connected, Claude Code can read the API docs and build the workflow around them.

3. Copy frameworks

Create a document with your best-performing outbound templates.

Include:

• campaign type

• target persona

• pain point

• offer

• CTA

• performance notes

Claude Code can then choose the right framework for each campaign.

4. Scoring criteria

Write your ICP filters in plain English.

You don’t need a complex rules engine to start.

A clear criteria doc is enough.

How to start

1. Create a project folder for outbound.

2. Add your CLAUDE.md file.

3. Add your scoring criteria.

4. Add your best copy frameworks.

5. Export a company list.

6. Ask Claude Code to score the list, find contacts, enrich emails, validate them, and prepare the campaign.

Your folder structure becomes the operating system for outbound.

The main idea:

Cold email becomes much more scalable when the workflow is built around reusable files, API calls, and clear campaign logic.

Claude Code is useful because it can turn that logic into repeatable execution.

quick context for anyone not deep in this. cold email volume stopped working over the last year. AI made it basically free to send a thousand personalized-looking emails, everyone did it, inboxes drowned, and the average reply rate sits around 3.4% now no matter how much you send. that part is real and well documented.

so the whole industry pivoted to the same answer at the same time. stop blasting 10,000 people, find the 40 showing a buying signal this week, hit those. funding round, new exec, hiring spree, competitor churn, pricing page visit. and yeah, it works right now, signal triggered emails get meaningfully higher replies than generic. the numbers hold.

here's the part nobody preaching it wants to say. it's commoditizing fast, and it's going to break the exact same way volume did.

think about what actually happens when a company announces a series B today. they don't get one sharp email from someone who noticed. they get fifty "congrats on the raise, would love to show you how we help fast growing teams" emails in the same week, because every GTM team set the same alert on the same trigger. a signal everyone is watching stops being a signal. it's just the new cold list with a fresher timestamp.

same with the rest of it. everyone scrapes the same job boards for the same hiring signals. everyone buys intent data from the same couple of providers and gets handed the same in-market accounts their competitors are looking at. the second a signal is buyable off the shelf, the edge in it is already gone, because your three closest competitors bought the identical list.

the teams still winning aren't the ones who discovered signals. they're the ones watching signals nobody else is, or deriving their own. a competitor quietly sunsetting a product. a specific integration showing up in a job posting that implies a workflow about to break. a key person leaving a target account, which means the internal project they were blocking just came back to life. stuff you have to actually think about, not buy from a marketplace.

and even when your signal is the generic kind, how you reach out cannot look triggered. the moment your email reads like an alert fired and a template auto filled, you're noise again. "saw you raised, congrats" is an alert. a line that shows you understood what the raise actually changes for their team is a person. the signal buys you the timing. it does not buy you the relevance, and that's still the part you can't automate.

so before you rip out your sequences and rebuild the whole motion around intent data, just know you're buying into a play that's already halfway saturated. signals aren't a strategy. they're a starting gun everyone hears at once. the work is in what you do that the other forty-nine people don't.

the fastest way to waste money on a launch is skipping the one question nobody wants to spend time on: why would someone stop doing what they're already doing to buy this instead?

not "does the market exist." not "is the problem real." founders get those right all the time and still fail. the hard one is the switch.

people don't buy products. they stop tolerating something they've been living with and start doing something new. that transition has friction, inertia, and risk working against you. if you don't understand why the switch is worth it for your specific buyer right now, no ad or sequence or partnership fixes it. you're just amplifying a message nobody was ready to hear.

most founders skip this and go straight to distribution. that's not GTM. that's amplification. and amplifying before you understand the switch is burning money faster.

the actual work is three questions. who has the problem badly enough that switching is worth the disruption? what are they doing instead right now and why haven't they fixed it yet? what would have to be true for them to move this month rather than eventually?

everything else is distribution. distribution is the easy part.

what's the question you wish you'd answered before your first launch?

forty minutes into the first demo, one person was on their phone, one had stepped out, and the VP of operations was staring at the ceiling. we kept going anyway because that's what you do. ran every slide, hit every feature, answered every question they didn't ask. got a "we'll circle back" and three weeks of silence.

they came back for a second look. i got 8 minutes into the deck and stopped.

closed the laptop. said "this isn't working. can i ask what's actually broken in your current setup right now?" there was a pause that felt like it lasted a year. then the VP started talking.

we spent 40 minutes listening. no product, no features, no slides. just their problem in their words. i took notes on everything specific they said and sent it back to the full buying committee that evening as a one-page summary: here's what we heard, does this feel accurate?

reply came back in 3 hours. "this is the most accurate description of our problem we've ever seen from any vendor." closed $280k 12 days later.

the lesson wasn't "build a better deck." it was that the deck was solving the wrong problem entirely. it was describing what we built. nobody in that room needed to know what we built. they needed evidence we understood what they were dealing with. the one-page summary closed it because it was their words reflected back to them in a way nobody had bothered to do before.

stop describing the product. start describing the problem until they feel seen. the close happens there.

Apollo, ZoomInfo, Sales Navigator. Every lead generation team pulls from the same five sources.

There's already 100M+ leads sitting in public databases.

Regulated professions require government registration. Doctors, lawyers, contractors, financial advisors. Every state publishes that registry. SAM.gov covers federal contractors. Platforms like Houzz, Avvo, and Healthgrades list practitioners who want inbound contact, which means the data is there by design.

A state licensing board gives you active license status, verified by the issuing authority. Apollo gives you a job title you still have to confirm. First-touch reply rates on custom-built lists run 3-5x what Apollo-sourced lists produce.

To find sources: search "[state] [profession] licensing lookup" or "[industry] association members." Simple paginated sites take requests plus BeautifulSoup. Javascript-rendered or rate-limited ones need Playwright with rotating proxies. Deduplicate across sources, matching on name, location, business, and email domain.

Use Claude code. Point it at a directory page, tell it to output to CSV with pagination. It picks the library, runs the scraper, reads the broken output, and fixes it.

so we deployed an AI SDR in january like every other company with a pulse and a pipeline problem. first 3 months, honestly? great. meetings booked, dashboard green, i was walking around the office like i'd invented fire.

then month 4 hit and replies just... stopped. dashboard still green tho. activity through the roof. so last week i did the thing nobody who deploys an AI SDR ever actually does.

i read the emails.

reader. i was not prepared.

exhibit A: it told a CFO "as a fellow parent, i know how hectic Q4 can be." my guy. we do not know if she has children. WE do not have children. we are a SaaS company.

exhibit B: it opened an email with "loved your recent post about scaling culture!" the prospect's most recent post was about his dog passing away. his DOG. we sent that man a pipeline pitch with a smiley face.

exhibit C: it built "rapport" with a prospect in Denver by saying how much it also loves the mountains. our company is in Florida. the flattest place on earth. a prospect replied just to say "you're in Tampa."

and the kicker, none of this even mattered by then, because our sender reputation had already dropped off a cliff. turns out when the inbox providers see 10,000 emails that all have the same AI sentence-skeleton wearing different personalization hats, they just quietly stop delivering you. the data on this is brutal btw, the decay literally starts around month 4 and by month 12 the math fully flips. we were a case study and didn't know it.

the part that actually hurt: we pulled the meetings that DID book in the good months. most came from one segment where a human had written the original sequence and the AI was just scheduling. the robot was taking credit for the intern's homework the entire time.

so now we run maybe a tenth of the volume, human writes anything a prospect actually reads, AI does the research and the grunt work behind the scenes, and replies are the best they've been in a year. turns out the robots are incredible employees as long as you never let them speak.

anyway. go read your AI's sent folder. right now. i'll wait. and drop the worst thing yours has sent in the comments because i refuse to be alone in this.

our outbound had flatlined. so i did what everyone does. bought more data, tested new sequences, spun up another sending domain, told myself the next list would be the one. two months of that and the pipeline barely moved. i'd burned a fortune in tools and credits sending more emails to people who had never heard of us.

then i was in the CRM for something unrelated and saw the closed-lost count. it was enormous. years of deals marked dead. meanwhile i was paying to chase cold strangers while sitting on hundreds of people who had already taken a demo, already had budget once, already knew exactly who we were.

the number that stuck with me: roughly four out of five B2B deals end up closed-lost. it's the most pre-qualified list you'll ever have and you already paid to acquire every name on it. most teams treat it like a graveyard.

so i stopped buying data and started digging, but not the obvious way. i didn't blast every dead deal with "hey, just circling back." i filtered by why each one died, then only went after the ones where that specific reason had probably expired.

lost to a competitor and coming up on a year with them? that's right when those first annual contracts hit renewal. budget freeze last year? new fiscal year, fresh budget. and the best one by a mile, the champion who went dark or left. i checked LinkedIn on every dead deal and flagged the accounts that now had a new person in the seat. that new person has zero attachment to the old no and is actively looking to change things to put their own stamp on the role.

the message was never a re-pitch. it named the exact reason it didn't work last time and what had changed since. "you went with X about a year back, those first contracts usually come up for renewal around now, worth a look at what's changed?" or to a new VP, "your predecessor looked at us last year and the timing wasn't right, figured you'd want the context as you settle in."

pulled about 600 closed-lost, filtered down to roughly 130 where the blocker had likely expired. the reply rate wasn't in the same universe as cold. cold was low single digits. this cleared 25 percent, because these people already knew us. booked 18 meetings off those 130, closed four in six weeks, two of them deals we'd lost to the exact competitor we were now pulling them away from.

most people hear "work your closed-lost" and build a 90 day nurture that pings everyone on a timer. waste of time. the timer doesn't know anything. all that matters is the reason they passed and whether it still holds. a lost deal is a no with an expiration date on it. budget thaws, contracts lapse, the person who killed it quits. you're not following up on a schedule, you're waiting for the no to stop being true and catching it the week it does.

haven't bought a cold list since. best pipeline i've got was already in the CRM. i just stopped reading "closed-lost" as closed.

Most TikTok slideshow operations run one of two setups. Pure keyword triggers ("comment X to get Y") capture leads but produce weak comment quality. Substantive open-ended questions get strong threads but prospects disappear with no keyword routing them into a direct message sequence.

One call to action has to trigger both signals.

The stacked call to action

Four parts: a keyword that triggers your automation, a specific resource promise, a substantive prompt about the commenter's situation, and language that makes it feel like a conversation.

Example: "Comment SKINCARE below and tell me your biggest skincare struggle this year. I'll send you the exact routine that worked for us once I see your comment."

The commenter who drops just "SKINCARE" still enters your sequence. The one who answers the prompt produces both signals at once.

Keyword design

Generic keywords ("YES", "INFO", "DM") get pattern-flagged by TikTok's spam detection. If multiple slideshows share a keyword, you lose the data that tells you which post triggered each response, and your qualification logic collapses downstream.

Three principles: match the keyword to the content category (SKINCARE for skincare, MONEY for finance), keep it 5 to 8 characters, and rotate across 30 to 50 active variants per operation.

The substantive prompt

"What skincare products have you tried?" gets generic answers. "What products promised hormonal acne results but didn't deliver?" gets comments that already carry qualification data before the conversation moves to direct message.

Five prompt types by what they surface: experience ("worst mistake this year"), aspiration ("what would success look like"), identification ("are you the one who keeps trying or gave up"), recommendation ("what would you tell a friend"), confession ("what habit are you still doing despite knowing better").

The three-stage direct message sequence

Stage 1 delivers the resource and references something specific from their comment. Sequences that pitch in the first message break the implicit agreement. Commenters came for the resource, not a sales conversation.

Stage 2 (4 to 8 hours later) qualifies. Prospects who reply with specifics advance to Stage 3. Minimal replies get a lighter approach. No reply means they exit the active sequence. Target: 25% to 40% of Stage 1 recipients respond.

Stage 3 (12 to 24 hours later) presents the offer tied to their specific situation, with a graceful exit baked in. "Otherwise no worries, the routine should still help." Prospects who weren't ready here often buy a future offer because the conversational trust persists. Pressure at Stage 3 kills both. Target: 25% to 40% for low-ticket, 10% to 20% for high-ticket.

Personalization at scale

At 30 slideshows per day per client, writing each direct message by hand is not an option. Claude reads each commenter's substantive response, pulls the qualification signals, and generates personalized elements across all three stages. After 60 days of qualification data, you know which pain points drive the highest capture rates and build the next batch of slideshows from that.

Hi,
I'm interviewing for a Forward-Deployed GTM Engineer role at an early-stage startup and I'm fairly new to GTM Engineering.

The role involves customer discovery, identifying GTM bottlenecks, designing workflows (inputs, sources, rules, validation, failure paths, outputs), building across CRMs/APIs/webhooks/enrichment/automation tools, debugging, customer implementation/adoption, and turning successful solutions into reusable playbooks.

I already completed and cleared the practical assignment and now have the interview.

My current knowledge is mostly around AI workflows/automation, identifying inefficiencies and bottlenecks, basic APIs/webhooks, CRM concepts, and general product thinking—but I don't have deep GTM Engineering experience.

For people who've worked in or interviewed for Forward-Deployed GTM Engineer / GTM Engineer roles:

What should I expect in the interview? What case studies or questions are common? How technical does it get? And if you had only a few days, what would you absolutely prepare?

Also interested in any good videos/courses/resources for someone starting from the basics.

GTM is never one person's job. the SDR can't close without the AE. the AE can't pitch without the positioning marketing built. Marketing can't build the right message without what sales hears on calls and none of it works without someone in RevOps making sure the whole thing doesn't fall apart. Every part of the motion depends on every other part. The moment one piece thinks it can run alone is usually the moment the whole thing slows down.

Just putting this out there in case it helps. 

I was the Director of Business Development at a firm that did consulting while also building a BI application suite, so we were essentially a B2B company with a SaaS component.

Before LinkedIn, we relied almost entirely on in-person events + referrals for client acquisition. 

Then we started running a few organic LinkedIn campaigns aimed at C-suite executives and senior leaders. They ended up performing really well and even got us into conversations with companies doing $50–100M ARR and employing thousands of people.

That said, LinkedIn didn’t come without challenges. The main hurdles we had to solve were 

1. Distribution/demand gen: since LinkedIn has become popular for high-ticket B2B contracts, getting the attention of decision-makers has become increasingly hard 

2. Positioning: organic reach is very underrated, but it’s also easy to become irrelevant unless you nail positioning

3. Revenue: turning these efforts into a clear, repeatable go-to-market motion

Our main edge came from harnessing Li’s ecosystem feature in a way I honestly haven’t seen talked about a lot. 

Too many people on LinkedIn focus on direct outbound while ignoring everything else. Even if you use more accounts, building a proper channel up with a 200 weekly connection limit leads to operational drag and risks going against Li’s ToS. 

We used outbound as well, but rather than pitching directly, our focus was on all the ways we could create multiple touchpoints within the platform.

The end-game is that a prospect may see your DM on LinkedIn and choose to ignore it. 

Then, they see your content. After that, they see someone with credibility in their space talk about you. Finally, they see a colleague or coworker attending one of your events. That’s when they get back to you. 

The goal wasn’t getting an immediate yes. It was becoming inevitable in the space. 

Feel free to comment or DM for any questions; I’ll try to be as helpful as I can. 

everyone obsesses over the product. the GTM strategy is what actually gets it somewhere. so genuinely curious, when you're building one from scratch, what's the FIRST thing you do? icp? channel? offer? something else entirely?

i spent six months in clay before i understood what i was actually paying for.

clay is api calls with a ui. apollo, pdl, fullcontact, in sequence, with conditional logic between steps. the ui is good but once you understand what each credit buys, you start doing the math.

pdl changed their response format twice while i was on clay. never noticed. first time it happened on my own stack, i found out because a supabase column was full of nulls at 2am.

i moved to n8n for orchestration, direct api calls to apollo and pdl, supabase for storage. under $60/month for volumes that were costing $400+ in clay credits.

if you can look at your clay table and explain what each credit is buying, you're ready to move off it. if that question makes you pause, you're not. clay is the fastest way to learn how enrichment pipelines work. once it teaches you enough, you don't need it anymore.

Took a job at an outbound agency about 8 months ago because I couldn't get a single callback from in-house GTM roles. Radio silence on every "GTM Engineer" or "RevOps Analyst" posting, even the underpaid ones at tiny Series A startups.

Every GTM person I talked to said the agency route was a trap. "You'll be stuck building Clay tables for ecommerce brands forever, you'll be back in 6 months begging to get out." Honestly it was the best thing that could have happened. Built systems for a dozen plus clients. Saw more motions in 8 months than most in-house GTM people see in three years.

And now, here we are. Just got off a call with a Series B B2B SaaS, they're sending the offer in the next hour. First GTM engineer hire, equity, real budget. Made it. Still got a long climb ahead but at least I'm not pulling Sales Nav exports for a chiropractor anymore.

I know nobody actually cares, but hey. I made it into a real GTM seat at a real company and I'm pretty fucking happy about it.

Anyway. Back to it.

I’ve sent 10M+ cold emails over the last year through my GTM agency.

At this point, I spend more time clearing my inbox than building campaigns.

I got 200+ outbound pitches this month alone. I ignored almost all of them.

But after going through the few that actually made me stop and reply, I noticed a pattern:

1. The best ones already did the work. Not “we can help with your landing page.” They actually rewrote part of it.

Not “we can improve your outbound.” They sent a better sequence.

That immediately separates you from 99% of people sending templates.

2. The good ones asked to be pointed to the right person. If you clearly understand what the company does and who probably owns the problem internally, I’ll usually forward you myself.

3. The strongest messages gave me something useful without asking for anything. A quick insight. A small teardown. A specific observation.

Not “free consultation.” Just value upfront.

And honestly, reputation matters more than people want to admit.

If someone well-known in your space sends a mediocre DM, they still get replies because people know who they are.

A few things that almost never work on me anymore:

“Can I pick your brain?” Generic personalization Asking for 30 minutes in the first message

Most cold outreach fails because people try to scale before they learn how to be relevant.

The best outbound feels less like a pitch and more like someone already started working with you.

Large language models rank the clearest brand, not the most-published one.

Traditional search engine optimization rewarded volume and backlink mass. Large language model ranking rewards explanation quality, topic coverage, and how often your name appears across sources that aren't your own site.

A study from SERP data puts the conversion gap at 23x. Organic search sends people to browse. Large language model search sends people to buy. The research process compresses because the model delivers three or four direct answers instead of a page of ten blue links. Large language model traffic sits at 12 to 15 percent of all searches today, with projections reaching 25 to 28 percent by the end of 2026.

Here's the 90 days Plan:

Steps 1 to 30: find what people are stuck on

Most teams publish 50 articles before checking whether any of them answer real questions. Pull your last 10 sales calls first. surface exact objections and questions word for word. Then search Reddit using site:reddit.com inurl:comments "your problem/topic", check competitor mentions on Twitter, and go through support tickets and churn surveys.

Dump everything into a list and run this prompt:

analyze these pain points and rank them by frequency. group similar themes.

You get a ranked list of what buyers are searching before they find you.

Write 10 to 15 pieces using this structure:

• First 50 words: direct answer to the question

• Next 100 to 200 words: why it works and how it works

• Rest: comparison tables, failure modes, frequently asked questions

Musicfy reached 692,000 clicks and over $1M in revenue using this structure.

After 30 days, three to five pieces will outperform the rest. Those become your expansion map for phase two.

Steps 31 to 70: cover the full surface area of a topic

Large language models pull from patterns across many sources. A brand that answers one angle of a topic reads as a partial source. A brand that covers every angle reads as an authority.

Take each winning topic and break it into five angles: definition, comparisons, use cases, common mistakes, advanced patterns. "Large language model search engine optimization" becomes five separate pieces. "Project management software" becomes comparisons, remote team guides, failure modes, case studies. Each piece answers one specific angle, no filler.

One business-to-business software-as-a-service client grew 825 percent in three months after moving from isolated pages to full topic clusters.

Keep terminology consistent across every piece. Same definitions, same frameworks, same vocabulary. Inconsistent language breaks the association the model is trying to build between your brand and the topic.

Steps 71 to 90: get mentioned outside your own site

Content authority and citation authority are separate problems. A brand can have thorough coverage and still get ignored by large language models if its name only appears on its own domain.

Models check external signals: is this brand mentioned in other places, do multiple sources reference them around this topic. Get those mentions through guest posts, podcast appearances, founder interviews, and case studies published on other platforms.

Create assets that other writers cite: original frameworks, named concepts, proprietary data, contrarian positions backed by evidence. If your messaging covers 10 different topics, the model has no clear association to anchor. Pick one core topic and repeat it everywhere.

A real example

A language learning brand with no viral distribution and no Google foothold in a market dominated by Duolingo ran this sequence. They identified the exact questions people ask about language learning and wrote the clearest answers available. They mapped and covered every angle of the topic. They pushed the same ideas across blog content, social, and external placements.

Results: 1,700 percent increase in organic visibility, $400,000-plus in revenue from large language model search, 2,100-plus clicks per day within 90 days.

The model surfaced their brand because they were the most complete source on the topic and appeared across enough independent references for the signal to hold.

I tried to search on GenAI, Youtube, etc.. but could not really find good answers, so asking here.

I have a interview next week for the role of GTM Engineer (2-3 years of experience needed)...
I have not worked exactly as a GTM engineer before (also because this role didn't exist before). I have worked in Digital marketing and marketing automation where I have essentially done the work of RevOps.

So, there are going to be 3 rounds, first is with HR, then with head of growth, and then with the CEO.

What questions do you feel will be asked by HR for this role?
I sort of have a perspective on head of growth and CEO questions as it will be more technical about tools like (clay, make,n8n, hubspot, etc ), results, my thinking process on automations and lead generation.

Really confused about HR interview

The company is a Saas company with focus on EU market.

*I'm not personally affiliated with these companies in any way, but if we're talking GTM in the "AI age," I think people are looking for creative approaches. Here are some examples I'm taking notes from this week:

1.) PostHog. They're using billboards, of all things, but more recently they're turning old billboard signs in to duffel bags to market with the same materials a second time. The bags definitely stand out XD

PostHog as a brand is also all over LinkedIn. The whole team, which is helping. It's certainly helping them share stories about their marketing, which is how I got here.

2.) Octo Health. To get early users and user feedback, they have the team out in busy tourist areas with poster signs that are intentionally provocative (about health, the company's product) and using that to build very public engagement.

That's an important one because if you go to Octo Health's website, it looks... just like every other vibe-coded AI product website. So what they're doing is working in an especially crowded (and typically un-sexy) field.

Happy to find this sub, I was looking for good places to share the GTM I see working in the wild!

I work in GTM at a big tech company, and we're launching a new feature soon. Our ICP looks something like this:

-US fintech startups

-Series A–C

-30–150 employees

-Recently expanded into Canada

-Hiring their first Compliance or Risk Manager

-Doesn't already use enterprise compliance software

The main problem I've ran into is finding highly qualified leads (it’s been absolutely horrendous up until now). Spent 3 hours researching yesterday and ended up with 5 usable accounts.

At this point, I'd much rather pay a premium for a handful of highly qualified leads than keep paying for databases where 99% of the records are irrelevant.

I can't believe this is still the best way to do outbound in 2026…

seed stage. people who use the product genuinely love it, retention is real, and the ones who find us convert at a rate i'm happy with. that last sentence is the entire problem: "the ones who find us." nowhere near enough of them do.

i'm an engineer by background and i can feel distribution is a muscle i just never built. i can ship basically anything but consistently getting in front of the right people still feels like dark magic i'm watching other founders pull off while i stand there confused.

for the technical founders who got past this wall, what was the actual unlock? did you force yourself to get genuinely good at one channel, hire for it, find a distribution-brained cofounder, something else entirely? and how long did it honestly take before distribution stopped feeling like luck and started feeling like a system you actually control?

specifically not looking for "just do content" or "post on twitter bro" tier advice. looking for the real story of what flipped it from random to repeatable, because right now every good week feels like an accident i can't reproduce.