🦾Eric Nowoslawski

Founder Growth Engine X | Clay Enterprise Partner

I am a direct response outbound lead generation expert. And by that jumble of words, I mean that I leverage cold email to build predictable revenue pipelines for my clients. 

I leverage over 20 databases to merge Firmographic, Technographic, and Persona data to build highly targeted playbooks with my clients. 

I believe we need to arm sales teams with the knowledge of their total Target Addressable Market and teach them how to score that Market for which companies have the most pain and need their solution. 

The best way to do this is to look at public indicators such as open jobs data, employee growth/attrition, social media growth, web traffic, new product launches, employee sentiment, individual employee tech investment, company tech stacks, etc.

With this data, not only are you only messaging with people that you know have a problem that you can solve, you also are armed  with powerful messaging that cuts through the noise. 

I teach sales teams to embrace technology and multiply what is possible per employee.

Founder

There is actually a limit to cold email reply rates.

Knowing this can hopefully free you from testing things that won't matter.

Let me explain.

I have worked with customers in the past that are obsessed with getting the reply rate as high as possible. They think if they send a good enough email, every single person who could possibly see it is going to respond.

We've run tests that show this just isn't the case.

We had the opportunity to work on behalf of Forbes at Web Summit. We had the list of speakers. We had all their emails. We cold emailed them from infrastructure that had warmed up for four months. The pitch was simple: "This is our booth. We want to interview you for Forbes. Book a meeting." And we sent them a link to Forbes.com.

That only got a 30% reply rate. Basically all positive.

When I say "only" 30% — if I were a speaker at an event and Forbes wanted to interview me, I would think the reply rate would be even higher than that.

We also helped a customer email their own customers. We found data that showed they needed a new product, and we could fix the problem for free.

Still only a 10% reply rate.

I say all this because these two examples are pretty undeniably good offers. And they still did not get 40, 50, 60% reply rates.

When you're sending emails, there are so many reasons why somebody doesn't respond. They didn't even see it. It got mixed up with everything else in their inbox. It's just not the right time for them.

When you've run a couple of experiments and the reply rate is staying pretty steady, I would think more about experiments around lists and experiments around offers versus trying to change the copy to see if an AI personalization or a fancier way of saying something is going to move the needle.

List segmentation and changing your offer matter way more.

What experiments have you run that changed your thinking on reply rates?

Founder @ TrueAdvertize | B2B Outbound partner & Nice Guy | High Performing Swimming Athlete

Hey! This is Samuel. 

My day starts swimming as a high-performing athlete and it ends helping to scale ambitious B2B Companies 

I am a founder of a narrow-but-refined Claygency with expertise in B2B outbound strategies and mechanisms that drive business growth.

What I do, it's simple: 

I help B2B Companies grow by generating more qualified prospects through outbound strategies at scale using Clay!

And what I mean by "growth" is:

→ Generate a predictable flow of high-quality prospects consistently.
→ Close more sales opportunities, therefore increasing top-line revenue.
→ Fill your pipeline with dream prospects.
→ Embrace AI, automation, and streamlined client acquisition channels.
→ Don't stress about lead generation again.

That's what I do.

If you are interested in seeing how this can work for you, don't hesitate to message me and I'd be more than happy to help you.

P.S. I am quite interested in pushing myself beyond my limits too. For that reason, I became World Junior Champion 2021 in Finswimming.

If you'd like to talk about growth, apply to work with me (or chat sports), feel free to shoot me a message, and let's connect. 

Cheers 😘

Samuel Roa Cárdenas

how I'm feeling lately. 


Claude Remotion Control is gonna help me deploy GTM systems for our clients while I'm playing golf hehe.

If you don't know what is the thing I'm talking about here. Claude what? 

Check this: https://lnkd.in/eCekMUZ6

Founder @ TrueAdvertize | B2B GTM partner & Nice Guy | High Performing Athlete | I use Clay for a living

Stop looking for the perfect signal. It doesn't exist.

Every GTM team I work with wants to find the ONE data point that predicts buying intent, the signal that tells you exactly which company is ready to buy.

That's not how it works.

Some of the most useful data is private. 

Revenue is a good example, it's estimated from employee count, it's often wrong, and you can't verify it, but that doesn't mean you ignore it. 

It means you build a system around it that gets closer to truth.

You take what's public. 

Job postings. 
Org changes. 
Technology adoption. 
Content they're publishing. 
Vendor shifts.

Real-world behavioral data that anyone can see.

Then you match those patterns against what you already know about companies that bought from you and then you can answer the following questions: 

What were they going through when they signed? 
What was happening publicly before the deal closed?

That's where Clay and Claude Code come in, to basically orchestrate dozens of signals together with the context of what actually made similar companies buy.

The signal doesn't close the deal, the context you give the system to interpret that signal does.

Any data point can be useful. What makes it powerful is knowing what to point it at.

There's this voice in my head every time I hand something off to my team.

"You could do it faster yourself."

And honestly? It's probably true. 

I could jump into Clay and build worklflows from the ground.

I could open Claude Code and set up the systems to generate programatic account research myself.

But that's the trap.

I'm still building my agency right now and not as you may think, like me being in the woods or somethig like that. No. 

I'm working on an amazing project that later down the line I'm going to talk about here and it requires 90% of my attention every single day.

So the hardest lesson isn't technical. 

It's not about Claude Code or Clay or which system to build next. It's my own ego getting in the way.

The goal isn't to do more work. It's to build systems the team can scale without me.

I build the new thing. The team runs it. I build the next new thing. They run that too.

Last week I shipped a content pipeline that integrates a scraping system, and a scoring workflow across 5 clients aside the current GTM work, each one with its own taste, and the hardest part wasn't building any of it because they were new to me.

It was walking away after.

Every time I step back, there's this pull. "Just let me make sure about that one thing." But if I get into that one thing, I'm the bottleneck again. And the whole point was to stop being the bottleneck.

The founders who scale in 2026 aren't the ones who work the hardest. 

They're the ones who build systems that run without them in the room.

I'm still learning how to be that founder. Every single day.

I keep watching founders demo new tools on LinkedIn like it's a personality trait.

Meanwhile, the ones actually closing deals aren't talking about models.

They're too busy building systems around what they already know about their customers.

I run Claude Code + Clay (no, I don't replace Claude code with Clay, I enhance the power of Clay using claude code) across every client project. It's fast. It's excellent. 

But Claude Code or any other tool has no idea why my clients' customers buy. 

It doesn't know which CRM signals are real and which ones are noise. 

That part is mine. That part is the whole game.

The competitive edge in 2026 isn't the tool.

 It's the operator. 

Your understanding of the problem. Your context. Your ability to structure what you actually want before you touch anything.

AI without context is just expensive autocomplete. 

Systems thinkers are going to run circles around tool collectors this year and most people won't notice until it is too late.

Now when we onboard a new client, a system triggers automatically to build the context that we need to start working systematically on the client.

No guessing, just pure structure. 

Here's what we actually build:

⤷ Claude Code pulls their CRM closed-won data and finds the real buying triggers in the web.
⤷ Clay enriches the next target list using those triggers as filters and we push the list automatically to Clay.
⤷ Claude Code generates three message variations matched to what actually made similar companies sign - we use PVP and PQS depending on the data that is available in the closed won deals that sits on the CRM.
⤷ First 200 sends go out, we usually test small and then scale if it makes sense.
⤷ Reply data feeds back into Claude Code
⤷ Next round is tighter. Every time.

The big difference here is that the old way treats outbound like a broadcast. This new system that we built treats it like a conversation that gets smarter with every send.

That's compounding context... a concept that I'm sure you're gonna hear a lot from now on.

So the key is step one. If you skip the CRM analysis, the rest of the loop runs on assumptions. And assumptions don't compound.

We're testing this version of this right now where the feedback loop runs weekly instead of after each campaign. That makes more sense now, I'll share the results here once we have done enough testing.

I deleted a database because I told Claude Code "just delete it."

No way to recover it.

Claude Code is the most powerful software tool I've ever used and I'm designing my entire business around it + Clay and Sales/GTM thinking, but the context can get away from you, and that can't happen.

You give claude code a prompt and it goes 1,000 miles a minute, and then you have to go track down everything it did to understand if it's right, the dependecies, the QA checks.

It's quite exhausting and it's worth to a certain point.

I had a CRM ingestion workflow running perfectly, clean data, correct formatting, everything mapped to the right fields. 

Then I kept building on top of it, and because Claude Code kept going, it lost that context and overwrote the whole thing.

That's the nature of it, you can do a lot, but when it makes a mistake, it can make a really big one, like deleting your entire project database because you got sloppy with one sentence.

Now Clay, Clay forces you to think differently.

What format do I want? 
What happens in this exact situation? 

If you're writing data to 200,000 CRM rows, you can't vibe code that easily* 

You need to see every error and correct it yourself or set up great underlying processes that catches those things for you to review, again making sure it has the correct context. 

At the end of the day, setting things like that depends on your OWN situation, not on what people tell you to do without your context.

But what I can tell you right now is that voice isn't precise, Clicking is precise.

So the way I think about it now: Claude Code is for building, Clay is for running.

One is creative, the other is reliable, and your business probably needs both, because the moment you confuse them, you can lose something you can't get back.

Your CRM already knows why people buy. You're just not reading it correctly.

I'm running Claude Code across three client CRMs while my team pulls data through Clay on their closed-won deals. 

Not deal size or sales cycle length. The actual transactional trigger. 

What made someone go from "interested" to "yes, let's do this."

The reasons people buy almost never match what the sales team thinks. 

In the last CRM we pulled apart, marketing said the product demo closed the deal. 

The data said something else: 6 out of 8 closed-won deals had a failed vendor switch in the 90 days before signing. 

That wasn't in any sales deck.

Once you find that pattern, outbound changes completely.

You stop writing messages based on what you think prospects care about.

You start writing based on what actually made similar companies sign. 

That's not personalization. That's pattern recognition.

The data is already sitting in your CRM. Nobody's connecting it to outbound.

This is the unsexy work. Period. 

No Claude Code screenshots. 
No Clay workflow GIFs. 
No viral automation thread. 

Just reading your own data so every new campaign starts from proof, not guesses. That's real revenue engineering.

The pattern I keep seeing with early-stage founders looks something like this.

They hire before they have a playbook.

Series closes or something happen, so the board or just them say grow the team, so they hire SDRs, a marketing lead, and maybe a VP of Sales.

Six months later, pipeline hasn't moved.

Not because the people are bad, most of the time they are great professionals, but because nobody built the system they were supposed to run.

The SDRs don't know which accounts to target because nobody analyzed the closed-won data. 

The marketing manager is producing content with no feedback loop. 

The VP of Sales is managing a process that doesn't exist yet.

I keep seeing this, more often than I'd like, including struggles in the leadership when it comes to decide how the GTM team will be deploy. 

(tools & people)

So the fix isn't hiring less, It's sequencing it differently.

Before you start hiring a lot to increase your revenue based on more headcount, you need to know actually who actually buys. 

Why they buy
What signals show they're ready. 

That's a system problem --> not a headcount problem.

Build the system first with the data you already have to back it up, then hire someone to operate it.

Maybe you'll need to hire someone who understand what you're going through and get your GTM systems in place.

Or maybe not, it all depends on how well structured your data is and what hwo you're using it to increase revenue

So what I know is that the founders who will scale faster and cleaner this 2026 aren't the ones with the biggest team. 

They're usually the ones who built the system before they built the team.

There's one question I ask on every first client call and I keep asking after to our clients

The answer tells me everything I need to know about whether their GTM can be deployed.

"With perfect information, unlimited resources, and unlimited time - what data would you try to know about a company you want to sell to?"

Most clients freeze, and they should of course.

Not because they don't know, because they've never been asked to think about their market that way. 

They've been so busy running GTM motions that they never stopped to define what perfect targeting actually looks like.

When they answer, three things become clear.

1. Whether they have real product-market fit, you can hear it in how specific their answer is. 

 Clients who know their buyer describe exact scenarios, clients who don't describe firmographics.

2. Which data they're missing. 

If they say:

"I'd want to know which companies are actively evaluating competitors"  

That's a signal we can build a Clay enrichment around right now. 

If they say:

"I'd want more leads" 

We have deeper work to do first.

3. Whether their CRM is actually tracking the right things. 

Usually it's not, the data they need is sitting in their sales director's head, even in their sales team's head, not in their CRM.

That one question turns a vague "we need more pipeline" conversation into a buildable system that allow us deploy GTM teams correctly.

I've been running enterprise ABM for about 5 months now.

The single hardest thing to shift, everything I knew about SMB go to market.

SMB is greenfield, you start from zero if the client doesn't know very well their target market.

We build the list, write the messages based on the data, set up Clay, hit send and then we run multiple tests, shipping fast, until something sticks. 

(Again, this is only if we don't know what we want to know, the companies that actually would buy)

The whole system is yours.

Enterprise is the opposite.

They already have sales reps, marketing teams, content teams, CRM workflows, a decade of institutional knowledge sitting in people's heads that never made it into any system.

Our job isn't to replace any of that, it's to enhance what they already have.

When we walk in with Clay and Claude Code, the goal isn't to build something new.

It's to take what the sales team already knows and make those signals scalable.

Which accounts are investing in adjacent technology.
Which ones just restructured their sales org. 
Which ones are buying based on patterns the CRM was never set up to track.

Enterprise clients are comfortable spending $10K on a single discovery call, and it is a reality.

So the sales cycle isn't the problem, the problem is knowing which accounts are worth that call in the first place.

That answer doesn't come from a better tool, It comes from listening to the people who've been closing deals there for 15 years and turning what they know into a system that runs at scale.

The difference between a company that scales and a company that just gets bigger is one thing.

What they build before they hire.

Companies that scale build systems. 

Repeatable systems that produce consistent results no matter who operates them.

Companies that just get bigger add headcount. 

More salespeople doing the same unscalable thing, more managers managing more people doing more of the same.

I see it in almost every GTM team I work with, the instinct is always to hire. 

The board expects headcount growth. "We need 5 more SDRs" sounds like progress. 

But 5 SDRs running the same manual process doesn't give you 5x the output, you just get the same playbook at a higher burn rate.

One Fantastic SDR with the right systems in place can become a GTM lead that produces 10x better outputs compared to the team of 5. 

I've seen it, I've done it.

So the companies that actually compound their results are the ones that before hiring the next person, let's make sure that the right GTM infrastructure is in place, not just a simple workflow someone has to babysit.

Systems that learn from their own output and get better every cycle. That's the model. That's what we're building as well.

More people is a linear equation. Better systems is a compounding one.

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🦾Eric Nowoslawski

Samuel Roa Cárdenas

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