🦾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 | B2B GTM | Scale without headcount by mapping your playbooks into an AI revenue engine

Be honest - how many Linkedin posts have you seen this year saying that XYZ tech/playbook/widget is absolutely essential to your GTM this year?

Probably a lot

We now have thousands of sales and marketing technologies on the market

And all of them are pitching their solution as best in class

With playbooks, agencies, influencers to sell them

And yes, we have preferred tech vendors

Here's the difference

We don't throw you in canned playbook, we actually look at your Go to Market model before making recommendations

Let me tell you a funny story

I talked with an SDR team that was booking 80 meetings a month with parallel dialers and 10k emails per SDR per month

But 60% of their meetings got disqualified

Why?

They didn't qualify target accounts before doing outreach

Tech and playbooks don't save you deploy the wrong one at the wrong time

So what do we do different?

When you work with us, we start with an assessment

We look at your funnel metrics, your best customers, your messaging/copy, and get input from your team on detailed attributes that make up a great customer

That you CAN'T find in an off the shelf tool

After that we use our knowledge of best practices and tooling to identify the biggest opportunities to unlock more revenue per headcount in your team

This could look like

> Customer analysis
> Serviceable Addressable Market Mapping (because TAM is where you lose deals not win them)
> Target Account Lists
> Automated Contact Prospecting & Enrichment
> Pipeline Validation (we build out a script, test it to book meetings for you, help you take the meetings through completed discovery with next steps, then give you a completed call framework, email sequence, and discovery framework)
> Outbound Sales Team Coaching (Phone first)
> Sales Process Optimization
> Managed Cold Emails & Cold Calling
> Inbound Lead Flow Optimization
> AI Business Case Building (which we used to open a $300k+ opp at UPS)
> Implementation of Nooks, Clay, Evergrowth, and other sales tooling

Yes, we do that under one roof

Yes, it was very hard to find a team good enough to do all of this in one place

If you want to learn more, book a time here: https://calendly.com/b2bcatalyst/your-gtm-system-audit-linkedin
> Cold Email Infrastructure

Scott Martinis

GTM engineers & AI platforms are trying to figure out GTM context engineering by reinventing product marketing from first principles. Most will fail.

Context - I am seeing a ton of really smart people like Nico Druelle, Charlie Saunders, Alex Fine🌲, 🏍benyamin ‎ and others start to work on the problem of how to create centralized context libraries to iterate on messaging and targeting.

I am also seeing many platforms try to do the same thing.

The operators I included above will probably succeed. 

The platforms and GTM engineers that follow in their footsteps will largely fail.

Why?

Simply put - GTM context engineering is effectively reinventing a complete and fleshed out disciple (product marketing) from scratch.

Given INSANE amount of training volume, ML modeling (Karpathy released a new library to help automate this) CAN effectively invent the necessary cognitive architecture to help generalize those patterns.

Outreach could probably do this if they really wanted to. Gong as well.

But that's a waste of time, and anyone with less training data will probably fail horribly.

Think about what ACTUALLY happened to optimize messaging in high performing GTM orgs years back:

Product marketers would run meeting analysis, win/loss interviews, customer interviews, and talk with analysts.

They would then consolidate this into strategic narratives, positioning frameworks, competitive battlecards, ICP/segment/persona definitions, and value frameworks for major audiences.

If you want to do context engineering, you could try to reinvent this discipline from first principles.

But you're going to demonstrably slower than people who just... 

Interview good product marketers 

And figure out how to automate the product marketing analysis pipeline.

The marketers I've been most recently impressed with on this front are Emily Pick, Julia Hugg, Trinity Nguyen 💎

Octave, of course, is way, way ahead of the pack here.

Instead of trying to figure this out all by themselves, GTM engineers and revops leaders should take the shortcut and take the time to learn product marketing from elite practitioners instead

This was why all the AI SDRs failed by the way. 

It's not the lack of data foundations that broke them as many revops people supposed (though that doesn't help)

It was actually the lack of understanding the GTM strategy is quite complex, and guessing and checking without proper PMM foundations is not fast enough to get ROI on automated outbound.

What does this mean?

Well, the actual foundation of your GTM strategy is not revops OR GTM engineering, it's REALLY good product marketing

Both of those functions rely on good PMM to build good data models and automated workflows... though they both relying to some degree on well thought out enablement playbooks to know what to include in the data model and what to automate as well.

TLDR: model your context engineering on PMM. Will save 6-12 months.

I make AI work for complex B2B GTM through pipeline impact, not data projects | CEO, B2B Catalyst

So it turns out you can CSV dump from every GTM tool into your GitHub repo then Claude Code can write you data lake scripts in like... 20 minutes?

And if you dump in cold call transcripts from Gong or Chorus then you can basically rewrite any messaging playbook your org has in a few hours

Working next on ways to extract use cases from sales calls at scale, map in depth what messaging wins and loses

Then take the use cases and scrape online presence of won deals to extract custom signals

Tag won and lost deals with them

And build custom signal models predicting win rate and ACV

Future is pretty wild

Next up:

- Automated coaching briefs across the org
- Down funnel marketing campaign attribution
- Automated and targeting iterations for marketing campaigns and sequences
- Weekly or monthly auto playbook updates broke. down by segment
- Weekly or monthly value framework updates, battle cards, competitor battle cards (possibly automated)

Of course the more normal use cases like 

TAM scraping, signal sourcing, account prioritization, contact sourcing, qualification, classification 

TitanX phone intent

AI outbound personalization on emails calls LinkedIn

AI content generation and amplification 

AEO

AI inbound and outbound SDRs

Personalized ABM content

Personalized meeting prep

ETC

Candidly those feel a bit more pedestrian and table stakes to me though in 2026

Still... very exciting possibilities for GTM orgs

The key I think is knowing where to start

Which is usually your biggest bottleneck 

(DM me "context lake" if you want this in your org)

Every GTM intelligence tool is racing to answer the same question: who should we target?

Intent data. Buying signals. De-anonymization. Account scoring.

The targeting layer is getting genuinely good. 

And it's about to be commoditized. 

The same way signal aggregation became table stakes after Clay and 6sense turned it into infrastructure.

There's a deeper problem.

These tools are optimized for finding people who want to buy tools like them. They surface intent signals and firmographic matches.

They don't find the prospects where YOU have a differentiated advantage at closing.

That's a different ICP layer. Not "who looks like our customers" but "who has a problem we solve better than anyone else."

Most teams build messaging off the same shallow foundation. Company value props plus signals. "They just raised a Series B, mention growth." "They posted a sales role, mention scaling."

Relevant first line. Generic pitch.

The signal tells you WHEN to reach out. Your value prop tells the buyer what YOU think matters. 

Neither one tells you what the BUYER thinks matters.

The thing that converts is explaining why your solution beats the buyer's current state in a way they actually agree with.

That's differentiation. And it's missing from almost every discussion on signals & ABM I've seen. 

A VP of Ops at a 200-person logistics company. What's her job-to-be-done? What objection does she raise first?

That's not in your intent data. It's in your call recordings. It's in the patterns your best reps know but nobody's formalized.

Pull your last 30 closed-won deals. Listen to the first calls. What did the buyer say they cared about? Group them.

You'll find 3 to 4 patterns. Those are your real use cases.

Each one should shape both WHO you target and WHAT you say. Not a template with a different first line. A different reason to meet, built on why your solution changes their current state.

The targeting will keep getting smarter. The message won't get better until someone studies the buyer.

If you want help building this layer, DM me.

I rebuilt my company's website through an MCP inside Claude Code. Told my AI co-worker what to change. It read the Webflow data directly, made the edits, and published.

Mostly. I still had to open Webflow for some formatting changes the MCP couldn't handle. But 80% of the work happened without touching the UI.

That alone changed how I think about platforms.

MCP is Model Context Protocol. It lets AI talk directly to a platform's data and take actions.

Every legacy platform is walking the same path right now.

Step 1: Platform. You log in, click buttons, build reports, configure sequences. Standard SaaS.

Step 2: The AI can READ the platform's data. Pull reports, see pipeline, check analytics. You still log in to do anything.

Step 3: The AI can ACT. Create records, update fields, trigger workflows. You log in less.

Step 4: The AI owns the domain. It decides what to do, does it, reports back. You make decisions. You rarely touch the platform.

I'm somewhere between step 3 and 4 with Webflow. It's not perfect. But the direction is clear.

Now think about Salesforce, HubSpot, Outreach, Salesloft.

Once an AI can read your CRM, update records, pull analytics, manage sequences, and trigger workflows.. what's the UI for?

Salesforce has Einstein. HubSpot has Breeze. Outreach has "AI everywhere."

Bolting a chat window onto a legacy platform is step 2.5 at best. And the companies building step 4 aren't starting from step 1. They're starting fresh.

Pick the GTM platform you use most. Map it against the 4 steps. Where the AI feature actually sits vs. what the marketing claims will tell you everything you need to know about that vendor's roadmap.

taplio

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