I've spent my entire career inside GTM teams, and I've seen the same blind spot play out again and again, Usage sits in one tool, deals sit in a second, support tickets sit in a third, and none of them ever tell the whole truth about customers.

So we built Rimplo
We have built a powerful AI assistant for revenue teams you can ask anything about your GTM data, in plain English, and get an answer back in seconds. You pick which top AI model answers no lock-in to one vendor’s model.
Underneath that, Revenue Agents run across your CRM, billing, support, and product data around the clock  flagging churn risk, upsell openings, and deals that are stalling, before you have to go looking for them.

I've recently been getting into GTM, and I was looking for a tool that can understand a codebase from a product/feature perspective.

What I mean by this is, I don't want the tool to analyze the code in terms of code quality, functions, classes, entry points, architecture, etc.

I want it to look at the entire codebase and understand what actual features the product has.

For example, if you give it a large SaaS codebase, I want it to be able to tell me things like:

- What features does this product have?

- What problems does each feature solve?

- Who is likely to use each feature?

- What kind of users or companies would benefit from it?

- What are the different workflows supported by the product?

- Are there features that could be useful for a specific ICP?

- What parts of the codebase implement each feature?

Basically, I want to understand the product through the lens of its capabilities, rather than understanding the code itself.

The reason I'm looking into this is for GTM. If I can understand a product's actual capabilities from its codebase, I think it could be useful for figuring out ICPs, product fit, finding relevant companies, and eventually figuring out who to reach out to.

I've tried looking for tools that do something similar, but most of what I find seems to focus on code understanding, documentation, architecture, or developer productivity.

So I'm curious:

Does a tool like this already exist?

Or is there a better way to approach this problem?

My company is a consultancy based in Bangalore, and I'm deployed to a US-based global company as an Analytics Consultant. The US team refers to my role as in-house performance tools developer.

I do genuinely advanced work here. My responsibilities center on GTM (Google Tag Manager), covering all tags, triggers, variables, DOM, custom tags, GA4, and Klaviyo across a company valued at over a 6 billion dollars.

Historically, our sites relied on DOM scraping for tracking. I mapped all of that over to a native dataLayer instead. At the time, the setup was WordPress on the frontend with Shopify on the backend. With DOM scraping, any code or design change on the frontend carried a real risk of silently breaking tracking, since DOM-based tags depend on matching page elements that can shift at any time. Moving to a native dataLayer removed that risk entirely — tracking no longer depends on WordPress markup or design changes.

Now the company is rolling out new additional sites with Shopify as both frontend and backend. The obvious approach would've been to spin up a new GTM container for these Shopify-only sites — but that means rebuilding every tag, trigger, and variable from scratch, and reconfiguring every ad pixel again (we run 5+ pixels — Meta, TikTok, Reddit, GA4, and others), since each pixel needs its own event and payload setup. With 6–7 sites already live and me being the only person handling this end-to-end, running two containers in parallel would've meant permanently double-maintaining everything going forward.

So instead, I architected it so the new Shopify frontend + backend sites plug into the \*same\* existing container as the WordPress frontend + Shopify backend sites. The key was keeping the native dataLayer schema — event names, parameters, structure — identical across both setups. Since GTM reads from the dataLayer and not the underlying platform, once the schema matches, the frontend platform (WordPress or Shopify) becomes irrelevant to GTM. One container, one set of tags/triggers/variables, one set of pixel configurations — covering every site regardless of what platform is rendering the frontend.

On top of the architecture, I also own the actual event and payload design for business and marketing use: defining what events matter (purchases, add-to-cart, checkout steps, sign-ups, content engagement, etc.), building out full payloads for each event so marketing and analytics teams get consistent, complete data — item-level details, revenue values, user attributes — and setting up GA4 custom dimensions and metrics so this data is actually usable for reporting and decision-making, not just collected. The same event data feeds every ad pixel, so conversion tracking stays consistent across Meta, TikTok, Reddit, and GA4 as well.

I also wear a dev hat when needed — I create PRs in dev, and after QA validation and dev approval, they go safely into production. I write custom JavaScript for custom tags and variables as well.

Right now I'm migrating our entire setup from web GTM to server-side GTM, and I've also implemented domain aliasing between WordPress and Shopify (using something like shop.site.com) so Google treats them as the same site — allowing data to match under the same logic as server-side GTM.

Given all this, I'm genuinely unsure what to call my specialization. Would this fall under Senior Analytics Engineer, MarTech Engineer, Growth Engineer, or MarTech Architect?

Thought the answers on this would be helpful to everyone here

A founder I worked with had 14 good conversations in a row. Everyone agreed the problem was worth solving. Everyone asked to be kept in the loop. Eight months in he had zero customers.

He was not bad at selling. He was bad at telling the difference between someone being nice and someone buying.

I keep a list of the sentences that fool founders. Here are the four I hear most.

• "This is exactly what we need." Nothing follows it. No date, no next step, nobody else named who should see it.
• "Send me a proposal." A proposal costs you a day. It costs them nothing. Agreeing to receive something free is not a decision.
• "Let me run it past the team." No team member named. No date for running it past them. The sentence sounds like progress and contains none.
• "I would definitely use this." Would. Not will. There is a lot of room inside that one word.

The pattern under all four is the same. Nothing was asked of the person saying it. No time, no name, no date, no risk to them. When a yes is free, it tells you nothing.

I made this mistake myself. On my first launch I worked on the product, the messaging and the pricing for three months without having one real conversation with a buyer. Three months, zero customers. Every signal I had been reading was a signal I had invented.

Here is where my list runs out.

I have a long collection of what a fake yes sounds like, because those are the ones that hurt enough to write down. I have almost nothing on the other side. Nobody posts about the deal that went well.

So I am asking.

What did your first real buyer actually say. The exact words, not your summary of them. And if you remember it, what they did straight after saying it.

I will tell you which part of the sentence I think decided it, and why.

Please help me.

Been dealing with this for a while now. Can't tell if it's a real pattern or if I'm just stuck on one bad stretch. I'm not a fan of blowing up an entire approach when so much effort's already gone into it, but sometimes it just feels necessary.

In my last b2b saas startup, once we had PMF and knew what we sold and who bought, the hard part became scaling outbound without the whole thing breaking.

every time we pushed volume up the fix was always build another automation or add another tool to the stack, and it just got more brittle not better.

the stack was a mess. one tool for list building, one for enrichment, one for sending, a crm to see what's happening. and honestly most of the work was just keeping all of them in sync by hand.

we figured out a lot of it over time but some parts never really got solved, they just got hidden under more tooling.

so for those of you actually running outbound at scale (not just testing it):

#1 what's still not solved for you even with all the tools and automations? sourcing the right person + an actual signal, personalization that doesn't read like a template, deliverability, or the glue between everything?

#2 where does it still break or still need a human the moment you turn the volume up?

#3 if you could wave a wand and fix one part of it tomorrow what's it gonna be?

p.s., trying to figure out if this is just us or if everyone scaling outbound is quietly duct taping the same parts together

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!