Building sales teams and delighting customers at early stage hyper growth startups.

Mike Gallardo

We're hiring AEs at Deel. 🥳

Here's why you should apply:

1. All remote. Forever and always. Work from anywhere in the USA or Canada.

2. The earning potential is real. I have reps hitting 200%+ consistently. More than 50% of the team hitting quota. The comp plan is built to reward top performers.

3. We promote fast. Promotion cycles every 3 months. I have already promoted 10+ people this year. And we're not slowing down.

4. You'll learn from the best. We listen to calls, coach every week, and genuinely care about your career growth.

5. We're still growing super fast. New products. New markets. New opportunities. The opportunities are endless.

If you're a hungry AE who wants to build something and get paid well doing it, this one's for you.

👉 Interested? Apply here: https://lnkd.in/gkhGnZdA

If you have any questions drop them in the comments!

Best of luck!
- Mike G

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Certified Public Accountant

Sales Director at Deel

The Best Sales Course

An AE on my team closed a deal that was dead for 45 days.

No responses. Ghosted. Done.

Here's what he did:

He didn't send a breakup email. 
He didn't drop the price. 
He didn't chase.

He sent one short message:

"Hey [Name] — I know the timing may not be right. No worries at all. In the meantime, here's one thing that changed since we last spoke: [specific update]. Is [priority] still something that's top of mind?"

That was it.

The prospect responded within 2 hours. 
Deal closed that Friday.

Most reps lose deals in the follow-up.
Not the pitch.

Because they either disappear or become annoying.

The best ones stay present without being desperate.
And they have a system for valuable follow ups.

That's the difference.

- Mike G

👉 Get The Best Sales Newsletter delivered straight to your inbox for FREE: https://lnkd.in/g-dHTj7s

The best AE I've ever managed didn't have the best pitch.

Wasn't the most polished on calls.

Wasn't even the loudest in the room.

Here's what she had:

She treated every lost deal like a lesson and every won deal like a system.

When she lost: 

She reviewed the call, found the moment it went sideways, fixed it.

When she won: 

She documented exactly what happened and repeated it. 

Even looked for ways to make the process and experience better next time.

Most reps do the opposite.

They move on from losses fast and treat wins like they were meant to happen.

So the gap between average and elite isn't talent.

It's the work someone puts in after the deal is done (win or lose) that matters most imo.

That's where careers are built.

- Mike G

👉 Get The Best Sales Newsletter delivered straight to your inbox for FREE: https://lnkd.in/g-dHTj7s

One of my best AEs has a 39% win rate 🤯

Her secret is one question she asks on every single discovery call:

"If you could change one thing about how your team handles [process] right now, what would it be?"

That's it.

Here's why it works:

It's specific enough to get a real answer. 

Not "what are your challenges?" which is pretty vague, and tends to get vague answers (unless you're lucky).

This questions forces the prospect to prioritize. 

Suddenly they're telling you exactly what matters most.

The answer tells you everything. 

And gives you exactly what you need to position your product as how to best solve what's most important to them.

Most discovery calls fail because reps ask broad questions and get broad answers.

They leave without knowing the #1 thing the buyer actually needs to fix.

Ask the one question. 
Listen hard. 
Build everything else around the answer.

That's how she closes 4 out of 10 deals.

- Mike G

👉 Get The Best Sales Newsletter delivered straight to your inbox for FREE: https://lnkd.in/g-dHTj7s

I use AI every single day.

And in my opinion, Claude is the strongest AI tool for sales right now.

Here's one of the easiest ways to start using it:

Track your closed won deals every month. 
That's it.

Just log how many deals you close each month. 
One number per month.

Then drop that data into Claude and ask it to visualize it.

What comes out the other side? (See image below 👇)

A clean chart. 
Month over month trends. 
Automatic callouts on what's working and what's not.

This took me < 1min to make.

Now take that into your next 1-on-1 with your manager.

I promise, nobody else on your team is doing this.
And that's exactly the point.

AI isn't going away.

The reps who figure it out now are going to make more money, get promoted faster, and be a lot harder to replace.

The best part?

You don't need to be a tech person to start.
You just need to start.

And this is one of the simplest first steps you can take.

What are you waiting for?

- Mike G

👉 Get The Best Sales Newsletter delivered straight to your inbox for FREE: https://lnkd.in/g-dHTj7s

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.

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Mike Gallardo

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The Battle

Mike Gallardo

Scott Martinis

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Engagements

Likes

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