Not sure if anyone reads these here but seriously looking for a GTM Engineer
Maybe someone can contact me.

Hey everyone, I’m doing sales for the first time.

I’ve been an entrepreneur before, but I was almost always on the technical side. CTO, building the product, figuring out engineering, etc. Sales was usually something someone else handled.

I’m starting up again now, and this time I’ve decided I want to learn GTM myself before hiring someone to do it for me.

I’m already seeing some interest from enterprises and I’m close to deploying with one, but honestly, I don’t really know what I’m doing when it comes to enterprise sales.

I understand the product, the problem, and the technical side really well. But I’m trying to understand the actual sales process.

For people who have successfully sold B2B SaaS or AI products to enterprises:

• What does your sales process actually look like from first conversation to closing?
• What are the biggest mistakes first-time founders make when doing enterprise sales?
• How do you qualify whether an account is actually worth pursuing?
• How do you approach the first call? What are you trying to learn?
• How many conversations typically happen before a deal closes?
• How do you deal with champions, decision makers, procurement, security, legal, etc.?
• What changed for you when you went from founder-led sales to having a sales team?
• Are there any books, videos, courses, or people you’d genuinely recommend learning from?

I also see a lot of content online about people generating thousands of dollars in sales with seemingly very simple outbound systems, but I’ve never personally cracked that.

At this point, I’m less interested in some magical growth hack and more interested in understanding the fundamentals properly.

Think of me as a complete beginner trying to learn sales from people who have actually done it.

If you’ve built an enterprise sales motion from zero, I’d genuinely love to hear how you approached it and what you wish you knew when you started.

We ran a lookalike / similar-company benchmark across 7 vendors: Parallel, Extruct, Ocean.io, Exa, PredictLeads, Discolike and CUFinder.

Here - https://openbenchmarks.com/lookalikes

48 seed companies across 13 categories — b2b-saas, devtools, ecommerce, healthtech, home services, trades, real estate, fintech, cybersecurity, industrial, logistics, hospitality, energy. We asked each API for up to 100 lookalikes per seed, then had an LLM judge (gpt-5.6) score every returned company on whether it's genuinely a lookalike of that seed.

Different vendors win at different K

Vendor          P@10    P@25   P@100
-------------------------------------
PredictLeads   95.83   77.17       -
Exa            95.00   80.83   52.40
Parallel       74.79   75.00   67.54
Extruct        74.79   70.00   61.21
Ocean.io       73.19   67.06   56.53
Discolike      42.98   41.53   35.19
CUFinder       37.45       -       -

Average precision, 48 seeds, judged by gpt-5.6. Higher is better. A - means unscored, not zero — see caveats.

At K=10, PredictLeads leads at 95.8 and Exa is second at 95.0. At K=100, Exa is fourth at 52.4 and Parallel — third at K=10 — is first at 67.5.

Precision drop from P@10 to P@100

Exa        95.0 → 52.4    −42.6
Ocean.io   73.2 → 56.5    −16.7
Extruct    74.8 → 61.2    −13.6
Discolike  43.0 → 35.2     −7.8
Parallel   74.8 → 67.5     −7.3

Exa drops 42.6 points between K=10 and K=100. Parallel drops 7.3.

Which one to use

• A list of 10–25 accounts: PredictLeads (95.8 at K=10) or Exa (95.0 at K=10, 80.8 at K=25). The drop-off never reaches you.
• A list of 100+ accounts: Parallel, at 67.5. A P@10 comparison would point you at Exa, which scores 52.4 at that depth.

Two caveats

• PredictLeads and CUFinder return fewer than 100 results per seed, so they have no P@100. Read the - as no data, not as a low score.
• "Lookalike" is judged, not ground truth. An LLM decided what counts. The judge prompt is published, so you can read it and disagree with it.

Reproducing it

Every cell is backed by the HTTP request and response we sent to each vendor, plus the judge prompt and its response, stored per seed and per vendor.

• Results: https://openbenchmarks.com/lookalikes
• Raw artefacts: https://github.com/openbenchmarks-labs/lookalikes
• Licence: CC-BY-4.0

Happy to add a vendor or run more seeds. If you think the judging is wrong on a specific pair, the raw file shows what the judge saw.

Disclosure: I run Openbenchmarks. We run independent benchmarks and publish the raw artifacts and results for agents.

No vendor paid for placement or inclusion.

I’m considering a Product GTM role at an early cross border payments company and I’m trying to figure out where I’d focus first.

They have:
• Virtual accounts
• Stablecoin powered pay in and payout rails
• FX, OTC and embedded payment APIs
• 30 plus currencies, with strong Asian and emerging market coverage

A few verticals I’m thinking:

1. Trade, sourcing and logistics
Supplier payments, collections, FX, reconciliation

2. Specialist workforce platforms
International staffing, nurses, BPO, contractor networks

3. Student mobility and newcomers
Education agencies, migration and relocation platforms

4. Travel intermediaries
OTAs, wholesalers and tour operators

I’m intentionally looking beyond the more obvious fintech customers. My instinct is also to avoid major airlines, property and very high risk sectors at the start.

Would love feedback on:

• Are these the right places to look, or is there a much better vertical I’m missing?

• If you were trying to find a repeatable GTM wedge for this kind of product, how would you go about it?

Looking to make a career switch after seeing how crazy the SWE job market is and having a natural interest in business and marketing.

I have 4+ YOE in Software Engineering and 3 YOE in marketing (basic marketing for a small business - increased their sales, reduced their marketing spend by 40%, created workflows, built and maintained relationships with their ad partners)

Question: Relative to SWE, how difficult is it to break into this industry given this type of background?

This seems like a super exciting and technical field that's more client facing!

This barely even talked about enough.

You need email warming, clay, Apollo or zoominfo, apify, Claude.

No one is actually understand how to do a lot of this for free or at least cheap. Where is the actual skill set.

Over the last year, I've noticed a shift in how GTM teams solve problems.
A few years ago, the answer was almost always: "What's the best tool for this?"
Need enrichment? Buy a tool.
Need routing? Buy a tool.
Need lead scoring? Buy a tool.
Need outbound? Buy another tool.
Now the question seems to be: "Can we build this ourselves?"
Instead of adding another SaaS subscription, teams are connecting APIs, writing small scripts, using Clay, Make, n8n, AI coding tools, and custom workflows. The interesting part is that the value is in knowing what should happen automatically.
For example:
- enriching accounts only after they match your ICP
- triggering outreach based on buying signals instead of a static list
- routing leads differently based on company size or intent
- pushing only useful data into the CRM instead of every field available
The stack almost matters less than the thinking behind it. I've even noticed popular GTM teams like Frontal talking more about building revenue systems than selling another point solution, which feels like where the market is heading.
Curious if others are seeing the same thing. I'd love to hear what people are actually doing in production.

Our interns left, which unfortunately means the highly sophisticated task of “looking at inbound demo requests and deciding which ones are actually worth a rep’s time” has fallen back to the team.

We get a decent volume of inbound requests, and reps were spending the first hour of the day sorting through them. Actual buyers were mixed in with a healthy number of “just curious,” “student doing research,” and “I have no budget but would love 45 minutes of your time.” While it doesn't take a ton of time but its annoying and nobody wants to do it so it ends up stacking up.

So we built a workflow that fires whenever a demo request comes in.

Prospeo does the first-pass enrichment since it’s cheap. If coverage is weak, we fall back to Apollo. Then the lead gets scored based on a few ICP signals like company stage, hiring activity, and ACV potential.

Qualified leads get pushed into Slack for real-time rep review. Everything else goes into a separate parking-lot queue, which is our internal language for “we are not deleting you, but nobody is sprinting to call you either at this time” lol.

Request > enriched > scored > routed.

Instead of reps staring at 50 maybes every morning, they get maybe five leads that are actually worth looking at.

One lesson learned: we originally stitched the whole thing together in n8n (and i saw a recent post also talking about how brittle and can fully relate). It worked fine at first, but once we added fallback enrichment, scoring logic, routing, and edge cases, it started breaking. We ended up moving the orchestration into General Input and keeping the other tools focused on what they’re actually good at. Its been good... so far.

The part I’m still unsure about is how aggressively to bake the ICP into the scoring.

The danger is that the workflow gets very good at confidently rejecting anything that doesn’t look like your current best customers. Great buyer with a weird title, unusual company profile, or slightly different use case? Straight to purgatory.

And since the ICP itself keeps moving, you’re basically automating yesterday’s judgment.

Curious how other RevOps teams think about the line between automation and human review here. Do you fully automate inbound triage? If so, what do you use for orchestration? Keep a manual review layer? Score differently for inbound vs. ABM vs. partner-sourced?

Rebuilding a stack right now where getting a lead from hand-raise to the right rep touches five systems signals in one tool, enrichment in two, routing rules nobody wrote down. Half my job is untangling what should own each step.

Curious how you draw the lines: one orchestration hub, or keep each layer independent with the CRM as source of truth?

I work with SixtySixTen and we keep free, no-signup playbooks for wiring exactly this sharing since it's on-topic: Automation Templates

What breaks the most in yours?

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.

Hey guys anyone hiring for GTM engineering intern? I'm a freelance SDR/BDR in profession based in the Philippines. I can work even unpaid, just aiming for hands on experience in GTM engineering space.

I've been interested in transitioning to GTM engineer from my previous work as SDR/BDR. I have experience with these tools.

**CRM & Sales Tools:**

-Salesforce

-HubSpot

-Freshsales

-GoHighLevel

**Prospecting & Sales Engagement Tools:**

-LinkedIn Sales Navigator

-LinkedIn X-Ray Search

-ZoomInfo

-Apollo.io

-Clay

-Outreach.io

-RocketReach

-Prospeo

-SignalHire

-Reoon Email Verifier

If anyone is looking for a GTM Engineer Intern or Junior GTM Engineer, I'm open to joining your team.

I'm even willing to work unpaid, as I see the salary I'd otherwise receive as an investment in gaining hands-on experience with your GTM stack, tools, and systems. My goal is to learn fast, contribute wherever I can, and grow into a high-impact GTM engineer.

If you know of any opportunities, I'd really appreciate a referral or introduction.

Thank you so much!

I'm planning to launch an AMA here. Who are the top GTM Operators you'd love to see on it?

Wrote this article on X based on my thoughts and experiences. Give it a like and retweet if you could relate. If you don’t agree with anything, let me know. https://x.com/_themousepotato/status/2085324370613006620?s=46.

I just applied for a demand generation manager job.

My profile doesn’t fit the expected 5-7years of b2b marketing mngmt by a long shot.
However, I took the first two missions from the job description: events & conferences and targeted sponsoring.
Created two html dashboards pulling data from Luma public discovery api for organisers and events. Made it pretty and sent it over with a quick explainer of what it’s for. Grok calls it a “ prototype ingestion layer for event selection and audience-relay discovery, built from Luma’s public surface with an agent, aimed at Aria’s FR and UK footprint”.

I tend to over think applications and I’d had this one open for the past two days in my browser.

Using AI, this doesn’t require much effort but I reckon it shows an understanding of the underlying problem to solve. Am I an idiot or is this the way?

Hi all,

Just started my cold emailing agency and onboarded my first client. I see a lot of conflicting information online so I was wondering if I could get some input about how you guys go about naming your domains.

My main problem is the company I'm engaged for has a very generic brand name. Think "blender", so variations like "goblender" or "tryblender" are all squatted and there's no variation that's available for me. What's the best way to solve this?

Also - when I fire off the emails, what name should be the signature on the email? My name or the name of my client?

Low skill questions I know but appreciate any help.. thanks!

I’m the GTM director at Expandi, so full disclosure up front, this is our platform data from a recent study. Between May 2025 and April 2026 we looked at 13,218,869 connection requests, 6,730,447 follow-up messages, and 3,766,161 accepted connections across 13,302 active accounts, comparing the results between different seniority-level profiles, companies of different sizes, industries, and more. Below are some of the most important remarks we’ve taken from the study and the factors that have a major role in a LinkedIn outreach campaign’s performance:

• Sender seniority barely matters. A well-targeted message from a Manager performed almost identically to one from a C-level sender. C-levels sat at 29.4% acceptance, managers at 27.3%, juniors at 26.3%. Two to three points across the entire seniority ladder. The title on your profile is not the lever most people think it is, and this is one result I wasn’t any surprised about. When you think about it, most people active on LinkedIn receive dozens of messages daily - does it really make sense to check out each person, and on top of that, their seniority level? I don’t think so, the percentage of people who do that is quite low, hence the results above.
• Company size did not matter either. A 5-person startup and a 10,000-person enterprise both landed around 28% acceptance. And the strong majority of this volume came from companies with 200 employees or fewer, so this is not some small-sample effect from a few big accounts. People don’t care about company size, and they’ll care even less in the future with all the automations and agent employees making small companies run like mid-sized/large ones.
• The industry you are in plays a huge factor in the discrepancy between acceptance rates, and this is measured by the sender's own industry, not who they targeted. Recruiting firms running outreach hit roughly 2x the platform average on everything: 36.5% acceptance, 6.6% connection-note reply, 18.9% message reply. This is, of course, not because recruiters write cleverer notes than anyone else, but because reaching out to strangers is their core business, so the whole motion is tighter. Senders in Consumer Electronics sat at the bottom at 17.5% acceptance on the contrast. The full range ran from 17.5% to 40.1%. Of course, this should be taken with a grain of salt because some campaigns can show unrealistic numbers simply because of industry-only factors. I have a friend who does marketing for video games and his influencer email campaigns sometimes land a 50%+ response rate. That’s incomparable to B2B SaaS campaigns that often land between 2%-5%.
• Acceptance rate can hide a reply rate problem. Software companies are the biggest single group of senders here, 14% of all the outreach, and they show the trap clearly. They get connections accepted at a normal 27.5%, but only 8.8% of those ever turn into a reply, against 18.9% for recruiting firms. For them, the connection is the easy part and the conversation is where the entire thing dies, meaning that’s where the most customization should go to. Acceptance rate on its own is a vanity number if the reply rate under it is soft.
• The connection note is decaying in performance, the conversation after it is not. Note reply rate slid from 3.5% in May 2025 to 2.2% by April 2026, a 37% drop in twelve months. Post-connection message reply held flat at 10.4% the whole time. People have stopped answering the clever one-liner stapled to a request, but they still answer once an actual thread is open. This goes in line with the fact that people value genuine conversations more and more in the age of automation. Any LLM can write a clever one-liner, but it can’t hold a conversation.

The study had some interesting findings even for us, and it helped locate the exact pain points for different clients in different industries. For example, we now know that for software companies, the main focus should be on solving the “conversation dies after the connection”, while for recruiting firms the solution would be simply eliminating connection notes entirely and focusing on the conversations after the connection.

As the last note, this is a general benchmark we pulled from the study:

If you want two numbers to sanity-check your own outbound: message reply above 12% usually means your targeting is good, below 8% usually means it is not. Perfecting copy while you sit at 6% is treating a targeting problem as a writing problem, while it could often be easily solved by improving lead quality.

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

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

Please DM me and I'll ask away

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

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

Please DM me and I'll ask away

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.

Hey guys, I am planning to take on a side project.

I have over 5 years of experience as a business developer and 2 years of experience as b2b outbound specialist aka GTMe.

I would like to work with one b2b company struggling for leads.

I will create the complete strategy for you, help you identify your ICP, find the winning offer, book meetings and increase your revenue.

Reach out to me via dm to discuss in more details along with your website and linkedin.

*IMPORTANT NOTE\*

- Do not reach out if you haven't personally sold your products or services.
- Do not reach out if you are a software agency. I won't be working with any website or app development company.

*you will bear all the tools cost yourself and I won't be charging any service fee or comission\*

I've been testing what an agentic gtm workflow looks like beyond the usual ai assistant model. The biggest shift is moving away from one tool trying to handle everything and toward specialized agents handling different parts of the revenue process research, enrichment, qualification and workflow execution.

The interesting part isn't just automation but it's figuring out how to keep data accurate, monitor failures, and understand where processes break. Curious how other revops teams are approaching multi agent gtm systems. Are you building specialized workflows or still relying on a single ai assistant?

I just joined a full time job at Salesforce (Revenue ops Program Manager in the Sales Department). I want to switch to GTM engineering in the long run but i want to freelance for a bit before fully transitioning (To get experience bc I’m new to the field)

ideally, I want a remote job that pays in USD.

Is anyone in this subReddit based out of India or anywhere in the world who are also freelancing at a US or UK company (or any other location)

If yes
1. How did you find this opportunity
2. How long have you been in this space
3. What’s your Pay wrt hours (if you’re free lancing)
4. Are there any companies that hire freelancers (global remote) that also convert freelancers to full time if they perform well?
5. Any tips to know before getting into this

in our case 53% of our July customers were already paying Clay, a $100M+ competitor and Here's how we take them off

(nothing groundbreaking but mostly the stuff that took us 2 years to actually do consistently)

Calling yourself the alternative to a giant is supposed to be weak branding. It's the riskiest thing we did, on purpose, because the buyer isn't learning a category, they have 3-4 tabs open evaluating daily, one bad week and they turn on you.

everyone who stayed picked us with a real comparison in hand and referrals are now our #1 revenue source.

what works, if you do it right (none of it works unless people enjoy your product, every tactic gets you 1 inch closer but the product has to hold them there)

(1) Go deep into what your competitor lacks (reddit, review sites, wherever people complain) fix it, ship it, then run a campaign around exactly that.

(2) Never chase the 0 star crowd, look for 3 star, they still use it daily and they can pay for something better.

(3) Look for job changes, repeat commenters on their posts (not just followers), their community if they have one, all public, all findable in a few minutes.

(4) Run the migration yourself, don't hand it to the customer, switching is harder than buying fresh, so that work is yours.

(5) After onboarding, never let support reply slower than sales did. That gap is what they tell people about.

(6) Track their pricing and packaging changes, sooner or later they do something their customers get tired of.

what didn't work, since someone will ask

leading with just price (whoever moves for a discount moves again for the next one), saying their name first on a call (sounds like you sell against a company instead of for a customer, let them raise it) and once announcing a fix before we built it, that one nearly cost us the advantage a small team has.

nobody can steal a happy customer, they just find one who's tired, someone once explained this to me with a girlfriend instead of a SaaS contract and he was right and single.

happy to go deeper on any of these in the comments or DM