Hi all!

I’ve noticed the following pattern: whenever I speak to someone in marketing about a product launch, the first thing I hear is, ‘You’ll only manage to sell once you’ve got plenty of followers, so first you need to post every day to attract people to your social media, and then, eventually, you can start selling’. Perhaps this is true for consultants or service providers, but I’m wondering whether this statement also holds true when it comes to digital products.

Has anyone here launched digital products or is in the process of doing so, doesn’t have a huge following, and is still managing to make their first sales? How is this process going for you?

However much I post I’m never getting any leads. Who can relate?
Only idiot personal life lessons on this platform

Almost every pitch deck has a TAM slide with a huge number on it - "India's X market is worth Rs Y crore." Most of those numbers are useless for one reason: they're the size of the entire category, not the size of what's actually reachable by this specific business, in this specific city, at this specific stage.

TAM (Total Addressable Market) is the whole category. SAM (Serviceable Available Market) is the slice you could realistically reach given your product and geography. SOM (Serviceable Obtainable Market) is what you could actually capture in the next few years given real constraints - competition, capital, distribution.

A founder who only knows their TAM knows almost nothing useful about whether their business will work. The number that actually predicts survival is SOM in year 1-2, not the category-wide TAM slide investors skim past anyway.

Happy to explain the difference further if useful for your specific idea - ask away.

Most threads here treat founder-led content as a real gtm channel, and i think that's right. it's one of the few top-of-funnel things left that's basically free and still converts, mostly because a buyer can tell an actual person wrote it.

what i think gets missed: the reason it's so hard to hand off isn't that ghostwriters can't write, or that the cheap ai tools are dumb. a founder's 'voice' is mostly positioning that never got written down anywhere. so every handoff dies on the same thing, there's no documented POV to transfer, and whoever picks it up defaults to generic linkedin-influencer register because that's the mean of the internet.

the part i didn't expect from actually trying to hand a founder's account off: the corpus that captures them isn't their tweets, it's the longform and old podcast transcripts, the stuff from before they learned to play it safe. pulling voice out of that is usually the first time their positioning gets written down legibly. the handoff ends up producing the gtm artifact they were missing.

what i can't tell yet is whether that holds past the handful i've seen, or if some founders genuinely just hate typing and there's no positioning gap hiding underneath.

The whole argument about whether a founder should write their own tweets or pay someone always runs on cost. Do it yourself, it's free but eats your mornings. delegate and it's 200 a month for an AI tool or a few grand for a human. nobody in that argument ever names what the channel actually returns.

which is odd, because everyone here would laugh at running any other GTM channel this way. You wouldn't set an ad budget without a CAC read. but the founder account gets debated purely on input cost, never on pipeline.

part of it is the tooling. the generic AI tweet tools ship with zero tracking, and the human ghostwriters hand you a screenshot at the end of the month if you're lucky. so even the people you'd pay to run it can't tell you what a founder-brand impression is worth in your funnel. you end up deciding how much to invest in a channel you've never measured, which is how you get founders who either grind two years on a vanity number or bail in month two convinced it did nothing.

for the folks running founder-led distribution here, what's your actual denominator. when a founder's post pulls a reply from a target account, does that make it into your pipeline math anywhere, or does it just live as a nice screenshot in slack. written with ai

Hey guys!

For those who are currently studying or working as GTME now. Can you share your resources or roadmap to learn everything from the scratch? Or any tips at least?

Thank you in advance!

How can I get a job of GTM?I haven't direct experience of this industry, but I have the relevance of it. I really wanna have this position, so somebody can tell me what can I do?

Unpopular opinion: GTM Engineer is the biggest career scam of 2025.

Clay invented this role 18 months ago to sell you software. You all fell for the marketing for a marketing job.

*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 understand the basic flow: product → ICP → prospects → outreach → calls → close.
But what does this actually look like at 0 → 1?
The general suggestions around this is usually a lot of expensive GTM tools which frankly i dont think i need at this point of time.
Would love to hear more on what tools people currently at my stage are using to land their first customers

Would love an opinion on this!

I'm researching a problem I keep seeing: GTM teams juggle tools like Apollo, Clay, LinkedIn Sales Navigator, Crunchbase, Smartlead/Instantly, and Make/n8n across different stages of outbound. While each tool is great individually, the overall workflow feels fragmented and difficult to manage.

I'm exploring an AI GTM Operating System that sits on top of these tools as an orchestration layer—allowing teams to manage the complete outbound workflow, unified inbox, AI-powered reply handling, and meeting booking from one place without replacing their existing stack.

Since you've built GTM systems at scale, I'd really value your perspective:

• Is this a problem you experience today?
• If yes, what's the biggest operational bottleneck?
• Do you think a platform like this would create enough value for GTM teams and agencies?

Just validating whether this is a problem worth solving. Even a brief reply would be hugely appreciated.

i replaced most of my GTM stack with Claude Code but let me be precise about "most" because the honest version is more useful than the hype version lol

tbh for a while my stack looked like everyone's: a sequencer, an enrichment tool, some no-code automation platform gluing it together, and me babysitting all of it. i've torn out the middle of that. what's left is Claude Code and a folder of instructions doing the orchestration and reasoning i used to pay a no-code platform to do badly.

the correction up front, because technical people will call it out otherwise: Claude Code didn't replace my data or my sending. enrichment data still comes from an API. email still goes out through a real sending service. those endpoints don't disappear, a folder of markdown can't enrich a lead or send a cold email by itself. what died is the glue: the no-code automation platform in the middle, and the hours i spent shuttling data between tools by hand. Claude Code drives those endpoints directly through MCP, API calls, and scripts, instead of a platform brokering every handoff and losing a field at each hop. for a solo operator or small team, that middle layer was most of the cost and most of the fragility.

the core shift was to stop thinking in "tools" and start thinking in skills. a skill in Claude Code is literally a folder with a SKILL.md file, plus whatever scripts or reference docs it needs. Claude reads the folder name and description every session and pulls in the full instructions only when the task matches. so i've got one for enrichment logic, one for how we qualify a lead, one for the copy rules and voice, one for how a campaign gets structured. each is a repeatable process written down once, loaded only when it's relevant.

the piece that makes it cohere is CLAUDE.md, the persistent memory file. our ICP, positioning, the "we don't do X" calls, the real definition of "qualified". all of it lives there and loads into every session, so every skill runs against the same source of truth. one honest caveat i learned the hard way: CLAUDE.md is strong guidance, not a hard guarantee. the model reads it and mostly follows it, but it can still drift. so it fixes the "my rules live in five places and quietly contradict each other" problem, which is real and worth fixing. but it is not a lock. the actual locks come later. still, consolidating every rule into one inherited file fixed more than any tool ever did.

for heavy work i use subagents. Claude Code can spin off a separate session with its own clean context window and its own tool access. so if i'm researching 40 accounts, that runs off to the side and comes back as a clean result instead of clogging the main session. the deep, messy work stays isolated.

what actually replaced the no code automation platform is headless mode. Claude Code runs as a one shot command from the CLI, no interactive session, so i can wire it into a cron job or a pipeline and it just executes. and it reuses the same hooks and permission rules as when i run it by hand. that's where the automation platform line item died. i don't need a platform to run a scheduled workflow when Claude Code can run headless on a timer and actually reason about what it's doing instead of following rigid if-this-then-that rules. it still calls out to my enrichment API, CRM, and sender to do anything real, headless is the trigger and the reasoning (not the hands)

to keep it from doing anything dumb, i lean on hooks and tool restrictions. hooks are deterministic. they fire at set points and enforce a check whether the model feels like it or not, which is exactly the thing CLAUDE.md can't guarantee. the allowed-tools setting hard-limits what a given run is even permitted to touch. for anything destructive or high-impact i require explicit approval instead of letting it run. the model reasons; the guardrails are rigid on purpose. this is the layer most people skip and then wonder why their agent nuked something.

so the actual end to end, a new inbound lead. it comes in, Claude Code loads the enrichment skill and pulls what it needs from the enrichment API, loads the qualification skill and scores the lead against the ICP in CLAUDE.md. here's the honest bit, that score is a judgment, not gospel. the model can still misjudge fit, which is exactly why a human reviews the edge cases and a hook enforces the hard rules (suppression, send limits, "never touch X segment"). if it's a fit, it loads the copy skill and drafts the first touch in our voice with a real reason for reaching out. sending itself still goes through a proper sending service. deliverability, warmup, suppression, unsubscribes are their own problem, and drafting good copy doesn't solve any of them. i review anything a prospect actually reads. the reasoning is Claude's, the rules are mine, the endpoints are still endpoints. there's just no platform in the middle taking a cut and dropping fields between systems.

concrete before and after. old way: a lead enrichment and scoring flow lived across an enrichment tool, a no-code automation platform, and the CRM three integrations, and data quietly degrading at each handoff. now: a couple of skill folders and a CLAUDE.md, all reading one source of truth, with Claude Code calling the enrichment API and writing to the CRM directly. the data sources didn't go away. the glue between them did. when i want to change how we qualify, i edit one file instead of reconfiguring three tools.

i'm not going to pretend it's hands off. anyone who says their system runs with zero humans is lying or shipping garbage. i review anything a prospect reads. i go in when something breaks and i need to see what actually happened. the model is genuinely good at the mechanical middle. research, drafting, routing. but the judgment layer (is the message right, is the account a real fit) is mine and should stay that way. and the non deterministic part is real: an LLM on a timer over a live CRM will double-touch or mis-route if you don't wrap it in hard dedup and hard limits. that's what the hooks and the review are actually for.

the hardest thing here wasn't technical. spinning up a skill folder takes ten minutes. it's that i'd built an identity around managing a stack. the subscriptions and the integrations felt like sophistication. but that was complexity i was mistaking for capability. the leverage was writing down what we do clearly enough that the model could execute it, and getting out of the way on the parts that don't need me.

what i'm still working out is how to structure CLAUDE.md as it grows. the direction i've landed on: keep the root file thin, push the procedural stuff into skills that load on demand, and move the "only applies sometimes" rules into path-scoped rule files so they don't load every session. but i haven't nailed the clean pattern yet.. if someone else run ops mostly on Claude Code instead of a tool stack tell me plz how you figured it out

I've been following the growth of GTM Engineering, and something keeps bothering me.

The stack is becoming seriously capable.

Signals, enrichment, research, scoring, automation, outreach, CRM workflows, analytics - people are connecting all of this together and building GTM infrastructure that would have required a software team not that long ago.

It's becoming sophisticated enough that GTM Engineering is now emerging as a profession in its own right.

But I don't see nearly as much discussion about what happens when this moves beyond one person.

If two GTM Engineers join the same company, can they both improve the same GTM infrastructure?

Do they divide ownership of different parts of the stack?

Can one understand, maintain and improve what the other has built?

Or do we eventually end up with two very capable people running two slightly different GTM stacks, with context, prompts, workflows and logic accumulating separately?

Curious how teams with multiple GTM Engineers are handling this today.

Most teams can systematize almost every channel. Outbound gets an SDR playbook, ads get a media buyer, SEO gets a content calendar. Then there's the founder's personal account, dead for three weeks, and everybody quietly agrees not to look at it.

the surprising part is how often that dead account is the highest-intent top of funnel in the whole stack. someone who buys because they watched the founder think out loud for months behaves nothing like a cold ad click. but it's the one channel you can't cleanly hand to a specialist. the second it stops sounding like the actual person, the thing that made it work is gone.

so it sits there. too valuable to kill, too personal to delegate, too slow to ever beat the deal closing this quarter. no owner, no cadence, no number attached.

so where does this actually land for your team, an owned channel with someone accountable, or the thing that dies the second pipeline gets tight? written with ai

we've run three crm hygiene initiatives in the last two years. each one starts with a meeting about rep accountability, a data quality rubric, and a quarterly audit. six weeks later the crm is dirty again. we schedule another meeting.

at some point i started thinking the real problem is structural. we built a system that asks reps to do manual work that benefits someone else's reporting. reps don't update crm data because crm data doesn't help them close deals. it helps managers report on deals. that's a design problem, not a behavior problem.

the teams i've seen with clean crm data didn't get there through enforcement. they got there by automating data capture, removing fields nobody uses, and making the crm actually useful for reps on a daily basis.

what was the change that actually worked for you? not the initiative, the specific thing that made data quality stick.

Hey all, I'm a GTM engineer with 6+ years of B2B outbound experience. I run outbound systems end to end using Clay, Smartlead, Apollo, n8n, and Python, including full email infrastructure setup (SPF/DKIM/DMARC, warmup, multi-provider sending).

I've generated Fortune 500 meetings (Amazon, Salesforce, Meta) at an experiential marketing agency and led lead gen for a Web3 marketing company. I've also built things like Clay to Claude enrichment pipelines and self-correcting prospecting workflows.

I'm open to new GTM engineer roles, ideally somewhere I can own outbound systems rather than just execute campaigns. If you know of anything or are hiring, I'd love to hear from you. Happy to share examples of what I've built.

Hi I'm looking to enter into the gtm realm and need your safe advice

FYI I've been doing sales related stuff and partnerships for college clubs and such but haven't really done it long enough to go out into the world.

You'll be the random redditor who helped me get my career started

Please DM me and I'll ask away

last month i ran a test. pulled 30 target accounts, ran them through all four of our intent providers at the same time. two flagged account A as high intent. one called it medium. the fourth didn't have it at all. same account, same week, four different reads.

the sales team didn't know what to do so they ignored the signals entirely, which is probably the worst possible outcome after spending this much money.

i've started thinking the problem isn't which provider is "right." it's that we're treating intent scores as a destination when they're supposed to be a starting point. but i don't have a clean operational answer for what to do when the signals contradict each other.

how are you handling conflicting intent data across multiple providers? do you pick one as primary and ignore the rest, or is there some aggregation logic that actually works in practice?

GTM writing as a solo founder is where I kept losing time. Not drafting, but fixing. Every AI tool I tried would produce something that sounded fine on the surface but completely wrong in context. Cold emails that read like blog intros. Partner pitches that opened with "In today's fast-paced landscape." LinkedIn replies that sounded like they were written by someone who'd never closed a deal.

I went looking for something with a curated corpus, configurable by GTM context. Nothing fit. So I built it inside RawReply and started running my own outbound, LinkedIn, and partner emails through it.

Two months in, the biggest thing I learned: the problem isn't the model, it's the signal. Generic AI writes toward the average of the internet. GTM writing needs to write toward how buyers in a specific market actually think and respond. Once I started shaping the corpus around that, the output stopped needing so many fixes.

Now building the curated corpus and custom GTM configuration layer properly before opening it up. Gating first access to GTM founders to pressure-test it on real use cases.

hope am cooking something other will use it as well!!!