Scaling GTM Ops for B2B SaaS Startups ⚙️🛠️

As a Marketing and Revenue Operations Consultant with over 10+ years of experience, I help the top startups & Fortune 500 Companies grow their revenue through operational excellence 🚀💸

My Background & Experience: 
★ Scaled operations at 10+ Companies that were growing at 100% Year over Year 📈
★ Over 30+ certifications in Marketing & Revenue Sales Technology 🏆
★ Launched 10+ PLG & Freemium Efforts 🚀
★ Scaled 100+ Salesforce Instances 🛠️ ☁️
★ Scaled 100+ Hubspot Instances ⚙️
★ Optimized 75+ Marketing & Revenue Analytics & Attribution Tools 🛠️
★ Managed Millions of Dollars in MarTech Spend
★ Sales/Marketer Turned Ops Engineer  💸

My Portfolio: 
★Samsung / Samsung NEXT
★Connectwise
★AT&T
★WPP
★Madkudu
★15Five
★Lever
★Aerolase
★Madison Square Garden
★ControlCase
★Auvik
★Ramp
★Productiv
★John Varvatos
★ and many more.....

My Beliefs: 
★Ops Teams need more bandwidth via On-Demand Technical Talent #ExtraBandwidth
★Business Teams need solutions that make technical things easy
★Repeatable & Scalable Lead Generation is achievable
★Marketing & Revenue Operations can be strategic but requires a holistic approach to the business 
★Work can be and should be Fun! 

Outside of work, I am: 
★ Most importantly, a Family Man 👨‍👩‍👦
★ Diehard Knicks & Giants fan🏀🏈
★ Foodie 🍕🍔
★ Lover of nature 🌴
★ Sneakerhead 👟
★ Health Fanatic 💪🏽
★ Real Estate Investor 🏡

For more information on how my expertise can help your company grow, please visit www.JayOh.io.

Joe LaGrutta, MBA

MQL handoffs should not feel like a liability transfer.
Yet in most orgs, they do.

Marketing optimizes for lead volume.
Sales optimizes for revenue.

The definition sits in the middle, fuzzy.

If your MQL is based on surface level activity, you are not qualifying intent.
You are logging engagement.

Real alignment happens when:
-ICP is enforced in scoring
-Behavioral signals are weighted by buying stage
-Disqualifiers are codified
-Sales feedback loops into scoring
-SLA follow up is tracked and audited
-Recycle logic is intentional, not manual

An MQL should represent:

Fit + Context + Behavior + Timing.

Not just clicks.

If marketing and sales argue about lead quality, the issue is rarely effort.
It is math.

Loose definitions create political tension.

Tight definitions create velocity.

If your MQL to SQL conversion is under pressure, do not blame effort.

Refine the model.

Fractional RevOps & GTM Teams (and Memes) ⚙️🛠️

Many marketing teams do not have a real funnel strategy.

Most teams create content.
Run campaigns.
Drive traffic.
Score engagement.

But there is no intentional progression.

No structured path from:
-Problem unaware
-Problem aware
-Solution exploring
-Vendor comparing
-Ready to buy

If you do not design content and programs tied to specific funnel stages, you are not moving buyers.

You are collecting activity.

Full funnel means:
-Top of funnel content that reframes the problem.
-Mid funnel programs that deepen urgency and qualify fit.
-Bottom funnel assets that reduce risk and accelerate decision.

And those pieces should be sequenced.
Retargeted.
Scored differently.
Measured differently.
Owned differently.

If your blog, webinars, paid ads, nurture streams and SDR outreach are not mapped to buying stage, you are just spraying content into the void.

Then sales sits there saying the leads are cold.

Of course they are.

You never intentionally moved them.

Hope is not a funnel strategy.

Design is.

Sales and marketing arguing about MQLs is one of the biggest red flags in a GTM org.

When I see this happening, I already know the real problem.

It’s not the leads.
It’s the funnel design.

We see this pattern all the time across RevOps work at JayOh - the GTM Engineering Company  :

Marketing says:
“We’re hitting our MQL targets.”

Sales says:
“These leads are garbage.”

Leadership says:
“Why isn’t pipeline growing?”

Here’s the uncomfortable truth:

The MQL itself is usually the problem.

Most companies define MQLs around activity, not buying intent.

So you get things like:

• Someone downloaded a guide → MQL
• Someone attended a webinar → MQL
• Someone clicked 3 emails → MQL

Sales gets the lead…
and the buyer isn’t remotely ready to talk.

Now trust is broken.

Sales stops following up.
Marketing sends more leads.
The argument gets louder.

The teams think they have a lead quality problem.

But it’s really a funnel definition problem.

One of the quiet fixes we push with clients at JayOh - the GTM Engineering Company  

Stop optimizing for MQL volume.

Start defining lifecycle stages around actual buying signals.

Funny thing happens when you do this:

Sales stops complaining about leads.
Marketing stops defending their numbers.
And pipeline actually starts to grow.

The best GTM teams don’t argue about MQLs.

They barely use them.

No alternative text description for this image

If your CRM only works when reps manually update everything…
you didn’t build a system.

You built homework.

This is something I see a lot when talking with CRM admins and RevOps teams.

The system is perfectly structured.
The fields are all there.
The process is documented.

But sales hates using it.

Why?

Because reps aren’t system admins.

They’re sellers.

And the real job of RevOps isn’t to force compliance with the system.

It’s to design the system so sellers barely have to think about it.

Across a lot of the work we see at , the highest-performing GTM systems follow one rule:

Make the right behavior the easiest behavior.

That means things like:

• Automating lifecycle updates
• Auto-logging activities wherever possible
• Minimizing required fields
• Pre-filling data from enrichment or integrations

Every extra field you require is friction.

Every manual update steals time from selling.

The best CRM setups don’t ask reps to maintain the system.

They quietly maintain the system for them.

Because if your sellers spend more time updating CRM than talking to customers…

The system isn’t enabling revenue.

It’s slowing it down.

Good RevOps removes friction.

Great RevOps makes the system feel almost invisible.

Everyone wants to add AI to their GTM tech stack right now.

But almost nobody wants to fix the thing AI actually depends on.

Data.

We see this constantly when talking with teams we work with at JayOh - the GTM Engineering Company .

The conversation usually starts like this:

“Which AI tools should we add?”
“Can AI help sales prospect faster?”
“Can we automate more of our funnel?”

All good questions.

But here’s the reality:

If your CRM data is messy, AI just helps you scale bad decisions faster.

The same problems show up every time:

• Duplicate accounts everywhere
• Incomplete fields across lifecycle stages
• Activities not logged consistently
• Lead sources that no one trusts

Then teams plug AI into the system and expect magic.

But AI doesn’t fix bad data.

It amplifies it.

A lot of GTM leaders are treating AI like a growth lever.

In reality, it’s more like a multiplier.

If your foundation is strong, it makes your team faster and smarter.

If your data is a mess… it just makes the mess happen at scale.

One quiet trend we’re seeing across clients :

The teams getting the most out of AI didn’t start with tools.

They started by fixing their data model and lifecycle structure.

Same lesson RevOps has always known:

Garbage in. Garbage out.

RevOps teams aren’t overwhelmed because they’re bad at their jobs.

They’re overwhelmed because their systems are broken.

Walk into almost any GTM org and you’ll see the same pattern.

The RevOps team is drowning in requests:

“Can you fix this report?”
“Why didn’t this lead route?”
“Salesforce is broken again.”
“Can we change this workflow?”

All day long it’s tickets, pings, and fire drills.

And leadership assumes the problem is capacity.

But when we look at these environments with clients, the real issue usually shows up fast:

RevOps isn’t operating strategically.

They’re operating like the GTM IT helpdesk.

Here’s what typically caused it:

• No clear ownership of GTM systems
• Poor lifecycle and funnel design
• Automations built reactively over time
• Leadership asking for fixes instead of root-cause solutions

So RevOps ends up fixing symptoms all day.

Instead of fixing the system that creates the fires.

One of the biggest shifts we help teams make is simple:

RevOps has to move from ticket taker → system architect.

That means fewer one-off fixes and more time designing how the machine actually works.

Because the truth is:

Most RevOps burnout isn’t caused by workload.

It’s caused by preventable chaos.

Every company says they need “strategic thinkers.”

But when budgets tighten… they fire Dave.

Dave ships.
Dave fixes.
Dave builds.
Dave closes the loop.
Dave figures it out without a 12-slide alignment deck.

Here’s the uncomfortable pattern I see in B2B SaaS:

Headcount grows.
Titles multiply.
Documentation increases.
Alignment meetings expand.

Revenue does not.

There is a difference between:

Strategic clarity
And intellectual theater

Between:
Real operating leverage
And people who just narrate what should happen

Early stage companies win with doers.

Growth stage companies stall when talkers outnumber builders.

Late stage companies drown in governance.

If your org chart has more people reviewing work than producing it, you have a velocity problem.

Operators create cash flow.
Observers create commentary.

When I evaluate a GTM org I ask one simple question:

Who actually moves revenue forward without needing a committee?

Protect those people.

Hire more of them.

And stop firing Dave.

Incognito testing is not paranoia.

It’s respecting the fact that attribution, routing, and scoring are fragile systems.

If your pipeline depends on forms, your forms deserve production-level QA.

Most teams ship them like landing page copy.

Different game.

Test like revenue depends on it.

Because it does.

AI will agree with you all day.
That is the default setting.

It is optimized to be helpful.

Helpful often sounds like agreeable.
Agreeable feels like validation.
Validation feels like intelligence.
Dangerous combo.

If your prompts are vague, your thinking is lazy, or your assumptions are wrong, AI will still nod and say you are absolutely right.

That does not make you a super user.
That makes you amplified.

The difference between average users and killers with AI is control.

Super users:
-Frame the context precisely
-Define constraints
-Ask for opposing views
-Force tradeoff analysis
-Challenge outputs
-Iterate on structure
-Pressure test logic
-Ask what they are missing

They do not ask AI to confirm their opinion.
They use it to stress test their thinking.

If your AI never disagrees with you, that is not a flex.
That is a mirror.
And mirrors do not make you smarter.

They just reflect what you already believe.

The skill is not using AI.
The skill is steering it.

Control the inputs.
Challenge the outputs.
Exploit the iteration loop.

That is where the edge is.

Humans don’t really create from nothing either.

We remix ideas.
We learn patterns.
We build on things we’ve seen before.

That’s how innovation has always worked.

That is what I love about Ai. 

I use it more than anything as a thinking partner that helps me:

• explore ideas faster
• iterate on drafts
• test different approaches
• pressure-test thinking

AI isn’t replacing creativity.

It’s accelerating the iteration process behind creativity.

The people who win with AI aren’t the ones expecting it to do the work for them.

They’re the ones who know how to collaborate with it.

The operators who learn how to use AI become dramatically more productive than everyone else.

That gap shows up fast.

One RevOps leader uses AI to:

• analyze pipeline in minutes
• generate SQL queries
• draft documentation
• troubleshoot workflows faster

Another does everything manually.

Same job title.

Very different output.

AI isn’t the tractor replacing the horses.

It’s the tool that makes the smartest operators even more effective.

The real risk isn’t AI.

It’s someone in your same role learning how to use it better than you.

Most sales teams do not have a forecasting problem.
They have a qualification problem.

When there are no hard rules for what it means to move a deal forward, reps default to one thing:
Push the close date.

No defined entry criteria.
No required discovery fields.
No enforced MEDDICC.
No mandatory next step.
No mutual action plan.
No aging limits.

So what happens?

Deal does not close.
Rep does not want to kill it.
Close date moves 30 days.
Stage stays the same.
Pipeline looks “healthy.”

This is how Stage 2 becomes a retirement home.

If you do not force qualification standards, human nature wins.

Hope feels better than disqualification.
Optimism feels better than starting over.

Strong sales orgs bake discipline into the system:
-Cannot move to next stage without required fields.
-Cannot forecast commit without defined buying process.
-Auto flags deals aging beyond threshold.
-Clear rules for when to close lost.

Qualification should be mechanical, not emotional.

If reps can advance deals based on vibes, they will.

If the CRM enforces standards, behavior changes.

Most teams do not kick the can because they are lazy.
They kick the can because no one defined what “qualified” actually means.

High growth covers a lot of sins.
Until it doesn’t.

When the market is hot, you can outrun:
-A leaky funnel
-High CAC
-Weak qualification
-Forecasting based on vibes
-No attribution discipline
-Random GTM experiments

Revenue growth hides operational fragility.

Then a PE firm shows up.

And suddenly the questions change:
-Why is CAC creeping?
-Why is conversion inconsistent by segment?
-Why does forecast miss every quarter?
-Why are top reps carrying the whole number?
-Why does pipeline not translate to bookings?
-Why does marketing not tie cleanly to revenue?

Growth excuses inefficiency.
Private equity exposes it.

The companies that look good post acquisition are not the ones that grew the fastest.

They are the ones that built:
-Stage discipline
-Clear ownership
-Clean data models
-Real attribution
-Repeatable sales motions
-Forecast accuracy within a tight band

If your growth depends on chaos, you are not scaling.

You are just riding momentum.

Momentum fades.

Systems compound.

“$2.5M in Stage 1.”
Translation: we have hope.

Zero deal stage rules is how pipelines turn into fiction.

I see this constantly:
-No exit criteria.
-No required fields.
-No MEDDICC enforcement.
-No defined qualification standard.
-No aging limits.
-No auto-close logic.
-No probability governance.

But somehow… massive early stage pipeline.

If everything can sit in Stage 1 forever, nothing is real.

Pipeline without rules is just CRM storage.

And here is the part Sales leaders hate hearing:

Inflated early stage pipeline feels good.
It makes the board deck look healthy.
It buys time.

But it destroys forecast accuracy.
It hides rep performance issues.
It masks qualification gaps.
It delays the hard conversations.

Revenue is not created by how much is in Stage 1.

Revenue is created by how cleanly deals move forward.

Strong orgs obsess over:
-Entry criteria
-Exit criteria
-Time in stage
-Disqualification discipline
-Close lost hygiene
-Probability tied to behavior, not vibes

If your Stage 1 is doing all the heavy lifting in your forecast, you do not have pipeline.

You have optimism.

And optimism does not compound.

Process does.

Everyone talks about “intent data” like it’s some secret weapon.
It’s not.

Using third party intent signals is table stakes now.

If you are not tracking category searches, review site spikes, competitor comparisons, pricing page visits, you are behind.

But here is where it actually gets interesting.

The real edge is not detecting buyers who know they want to buy.

The real edge is manufacturing intent before the buyer can name the problem.


It is understanding the company behind the behavior.

Knowing:
-What stage they are in
-How their org is structured
-Whether they are founder led or operator led
-If they are PE backed or bootstrapped
-What their current GTM motion looks like
-Where they are constrained
-What they are optimizing for

That is intent.

If a 15 person founder led SaaS company is consuming content about outbound process, that means something very different than a 200 person PE backed company doing the same.

If a company just raised and starts engaging with infrastructure and reporting content, that signals scale pressure.

If their org chart shows three SDRs and no RevOps, and they are reading about lead routing and attribution, that signals a systems gap.

If they are hiring demand gen but consuming pipeline forecasting content, that signals a misalignment internally.

Intent is contextual.

Downloads alone are noise.

Engagement alone is vanity.

The advantage comes from combining:
-Company stage
-Org makeup
-Strategic posture
-Hiring patterns
-Funding events
-GTM structure

With behavioral data.

That is knowing your customer.

Not just what they clicked.

But where they are.

What they are trying to become.

And what friction they are about to hit next.

That is how you move from reacting to activity to predicting need.

Let me just add one more exclamation point so they know I’m friendly and not yelling!

"𝐓𝐡𝐞 𝐨𝐧𝐥𝐲 𝐰𝐚𝐲 𝐭𝐨 𝐬𝐞𝐞 𝐭𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐢𝐬 𝐛𝐮𝐢𝐥𝐝 𝐢𝐭."

Investing in amazing people and building tech since 2011.

Recovering Investment Banker turned Data Engineer, Software Developer and Tech Investor. After co-founding a hedge fund utilizing automated financial statement analysis, I pivoted into technology to build the systems I wish I had on Wall Street and as a business owner.

Today I build systems used by Fortune 500 companies, hedge funds, professional services firms with thousands of employees and executives who want to maximize return on time.

What I do:

𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: run McDonagh Technologies, we engineer technical solutions and bring them to the market
𝐈𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭𝐬: manage a single-family office, layering capital with operational support
𝐑𝐞𝐯𝐎𝐩𝐬: optimize revenue engines by combining strategy, analysis, and engineering

Technically hands-on and obsessed with leverage, I develop AI agents and use context engineering for everything. Started in backend but I love building full stack. Writing Python since 2009, JS since 2011 and building systems as a Salesforce Administrator for over a decade. Whether it is reading the latest ML research papers or architecting a new GTM strategy, I am obsessed with applying data to generate real-world value.

Never stop learning, reading the latest research or competing in hackathons & Kaggle competitions. My happy place is building computational intelligence that helps companies scale and helping people gain sovereignty through systems.

The Nerd Details: 

𝐂𝐨𝐝𝐞: Data Engineering (15+ years), Python (14 years), JavaScript (10+ years), SQL
𝐒𝐲𝐬𝐭𝐞𝐦𝐬: Salesforce Admin (10+ years), Master Data Management (10+ years), AI System Design
𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲: Corporate Strategy, GTM Architecture, Business Operations

Write about what I love:

Wealth @ WealthSystems.ai
Technology @ LifeintheSingularity.com
Revenue Operations @ MasteringRevenueOperations.com

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