Mark Kilens

CEO and Co-founder of TACK ⛵️ & ClubPF ⚓️ | People-first GTM Champion

Mark Kilens is VP of Marketing at EasyLlama and Founder of TACK.

EasyLlama is an all-in-one compliance and learning platform that reduces human risk and increases team productivity across your entire workforce.

TACK modernizes how businesses go-to-market to create, capture, and convert demand into revenue using its proprietary People-first GTM Model. 

He previously was CMO of Airmeet, a leading virtual and hybrid event platform. He oversaw Airmeet’s global marketing team responsible for brand, demand, lifecycle, and product marketing.

Before that he was the VP of Content and Community at Drift where he led the brand, content, creative, and events teams. He oversaw Drift’s community, Drift Insider, which has 65,000+ members, and Drift’s FLASH, RevGrowth, and HYPERGROWTH events.

Prior to joining Drift, he served as VP of Marketing and founder of HubSpot Academy. As a former customer, Mark joined HubSpot in 2010 where he spent two years working with thousands of HubSpot customers to scale their inbound strategy. 

From there, he went on to build HubSpot Academy from the ground up, and in doing so, educated and certified millions of people and helped grow HubSpot to more than $600 million in revenue.

When he’s not working, Mark enjoys plenty of steak and lobster, a round of golf or two, and loves being on snow or in the ocean.

Go fast, take chances!

Chief Marketing Officer

Leading Airmeet’s global marketing team and working to advance the event-led growth movement across B2B.

Airmeet is the most customizable and immersive virtual and hybrid event platform for growing businesses. 

Exciting news to share. 

We’re looking for several RevOps, sales, and customer success professionals to join EasyLlama.

I’ve been with company for a year now and I can say for certain this is one of the best places I’ve ever worked.

Reminds me a lot of my early days at HubSpot.

Tons of exciting problems to evolve, a huge market, clear product market fit, and a customer base that loves the product and brand. 🦙 

Not to mention the amazing leadership team we’ve assembled over the last year. 🔥 

Plus I love our company values.

We’re looking for more people to DRIVE EasyLlama into our next phase of growth.

Doer
Resourceful
Impactful 
Valued Partners
Excellence-Obsessed

Head over to our careers page to see all the open roles and send me a DM if you think you’re a match for one and I’ll do my best to intro you to the recruiter. 

Here’s a preview of some of the jobs.

- Revenue Operations Manager
- Manager of Customer Success
- Several Account Execuitve roles

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Mark Stouse

GTM Economics | 2023 GTM 50 | CMO ➡️ CEO | Plan | Predict | Prove | Pivot | 50 Startups to Watch for 2023 | Ten Most Influential Analytics Leaders

As CEO, I lead the team with the only AI-native, MASB certified, FASB-compliant causal AI platform  that enables GTM teams to plan, predict, prove, and pivot their investments in real time. With more than 25 years of experience in marketing, communications, customer success, and commercial strategy, I help transform business performance with data-driven insights and agile decision making.

I also leverage my expertise in GTM analytics and economics to co-chair the Modern GTM Organizational Structure at the Marketing-Accountability-Standards-Board, and to serve as a member of the Brand Valuation Work Group at the Association of National Advertisers. In addition, I have been recognized as an innovator and leader in the analytics field, with multiple awards, patents, and publications. I am committed to advancing the practice and standards of GTM accountability and optimization across industries and markets.

Chairman and CEO

If you’re having difficulty understanding why your executive team does what they do, here’s the key:

It comes down to one thing, quarter by quarter:

Competition for capital.

Every decision — headcount, strategy, AI adoption — is filtered through that lens.

Over the last few years, many companies quietly slipped from growth stories to value stories.

When you’re a growth company, the narrative is expansion.

When you’re a value company, the narrative becomes EPS.

And once EPS becomes the story, the operating system becomes cost-cutting.

The problem is that by 2022, most of the obvious costs were already gone.

Outsourcing was done. Real estate was trimmed. Vendors were consolidated. Org charts were flattened.

There weren’t many large expense pools left to harvest.

Then GenAI arrived.

And it was messaged not as a growth engine, but as a structural cost reducer:

	•	Replace knowledge workers
	•	Reduce consulting
	•	Automate cognitive labor
	•	Expand margins

For capital providers, this was perfect.

AI wasn’t just innovation — it was a new cost-cutting frontier.

The message was simple:

Lower headcount → Higher margins → Better EPS → Stronger capital position.

Investors loved it.

And once they loved it, the quid pro quo tightened: If AI can structurally improve margins, why aren’t you moving faster to cut expense?

Here’s the problem.

Most companies are not seeing material enterprise value from early GenAI deployments.

Yes, tasks are faster.

Yes, documents are generated more quickly.

Yes, some workflow friction is reduced.

But enterprise value does not come from faster tasks.

It comes from:

	•	Better capital allocation
	•	Better strategic decisions
	•	Better product-market fit
	•	Better competitive positioning

AI has largely been sold as an efficiency play in an environment where the core issue is effectiveness.

Many companies aren’t struggling because their costs are too high.

They’re struggling because their markets are saturated. Because differentiation is thinning. Because growth engines are weakening.

Cutting costs extends runway.

It does not restore lift to the plane.

That distinction matters.

Because when AI is framed as an EPS lever rather than a growth lever, it becomes financial engineering, not transformation.

And financial engineering eventually runs out of room, for companies and for people.

The real debate isn’t whether AI works.

It’s whether we’re asking it to solve the right problem.  

And whether we are thinking about AI correctly. Can correlation really get us all the way there?  More and more experts are combining GenAI and Causal AI, and the results are both effective and efficient. 

Bill Schmarzo Eddie Short Malcolm Hawker Jon Cooke Samir Sharma Julia Nimchinski Scott Brinker

Don’t usually work on Saturdays but I took a call earlier today with a finance leader looking to change the game between marketing, sales, and product. 

He made this comment:

“You’d think this wouldn’t be true, but based on what I see, these teams don’t understand each other at all. Marketing teams have almost never sold anything but a point of view on themselves, and very often unsuccessfully. Sales sees Marketing as a service they need, not as a multiplier of their performance. And Product teams are just building shit that someone thinks that customers want.”

Wow, huh?  I asked him to amplify. 

“Let’s face it, Sales teams are populated by people whose entire purpose is to separate a customer from its cash, thereby making money themselves. All the BS aside, they don’t care about the customer, and the customer knows this deeply.”

What about Marketing?

“[B2B] marketers are like the Air Forces in WW2 who thought they could win the war from the air. Marketers sit remote from the battle on the ground, often without direct knowledge of what’s really going on. They don’t know about their customers, and it’s obvious to customers that they don’t know. At least sales teams know their customers — they just only care about the cash.”

I am sitting there stunned that this leader is saying this so clearly. 

Ok, what about Product teams?

“Product guys are builders. They’ll build what they think is cool so they can say they built it. They’ll build what the founder or CEO tells them to build. But they’ve never lived the life of a user of their product. They don’t know them, and other than for UX surveys, they don’t care. They did their bit. Selling it is someone else’s bit.”

So what is the human solution here?

“Make everyone’s performance review partly, maybe even mostly, about how they aided the success of the other teams. Did Product build something so great that it accelerated deal flow?  Did Marketing multiply Sales performance beyond what Sales could have done by themselves?  Did Sales pass accurate customer and competitor intel back to Marketing and Product? Everyone’s got to see GTM as one effort, and not three. And this requirement is going to X-out a lot of marketers, sales people and product people who can’t make the change from their little rote worlds to the one that requires thinking and caring.”

What do you think about this conversation, particularly in light of the others I wrote about last week?  

I can tell you that these conversations are getting a lot more common, particularly with FP&A teams inside Finance.

The era of data dominance is over — and it didn’t last very long.

This MarTech article by Mike Pastore discusses something many organizations are quietly discovering: despite massive investments in data management, measurement systems continue to fail to deliver the clarity leaders expected.

Privacy changes and signal loss are part of the story. But the deeper issue is more fundamental.

For the past 15–20 years, most marketing analytics has been built on correlation, not causation.

Correlation can tell us which things move together. But it struggles to tell us which things actually drive outcomes. Put another way, correlation fails to explain #why something is happening. 

That distinction matters more than ever.

As data volumes exploded, the industry assumed that more data and more machine learning would naturally lead to better decisions. In many cases, the opposite happened: more dashboards, more “attribution” models, more reports… and less trust.

Causation, however, is different.

Causation is indivisible from the reality it models. And reality is the ultimate context.

When people ask me to define reality, the best definition I’ve found is simple:

Reality is what you bump into when you are wrong.

Correlation can survive inside a spreadsheet for a very long time. Causal claims don’t survive long once they collide with reality.

What we’re seeing now isn’t the end of data.

It’s the end of the assumption that data alone is enough. And ironically, it’s the scale and speed of GenAI tools that have accelerated our bump into Reality. 

The next era of analytics will depend less on collecting more information and more on understanding the actual drivers of outcomes — including time lags, external forces, and counterfactual scenarios.

In other words, the key question is shifting from “What does the data say happened?” to something much harder and much more useful:

“What would happen if we changed something?”

Worth a read. Thanks Mike!

https://lnkd.in/gXerq2B2

Building a world-class community of SEOs, marketers and MOps pros.

Data is just a receipt. Context is the story.

In the second half of 2025, the conversation shifted. After years of "data this" and "data that," I started hearing a new tune from marketers and vendors alike.

Data was old news. Context was king.

We’ve spent a decade building the infrastructure to collect every bit and byte possible. But the market is finally realizing that data is simply a record of what happened — not why it matters.

We’re hitting a "Come to Context" moment.

Marketing and marketing ops pros are becoming modern-day transcendentalists, searching for meaning in a desert of raw data. 

Inspired, in part, by the work of Scott Brinker and Mark Stouse, I’ve put together a few thoughts on why "meaning" is our new most valuable asset.

https://lnkd.in/ehchzEPm

#marketing #marketinoperations #bigdata

Open video: The era of data dominance is over, and it didn’t last very long | MarTech on martech.org

The era of data dominance is over, and it didn’t last very long | MarTech

I woke up about an hour ago and picked up my phone, something we can argue the merits of later. 😄

There was a Signal from a good friend recapping some observations he had as a result of his work in the energy sector. His comments prompted me to write this post on my phone. 

See what you think. 

Bill Schmarzo Samir Sharma Malcolm Hawker Fred Lardaro John Thompson Rohan Light Beth Bauer Dr. Lisa Palmer Jon Cooke Scott Brinker Julia Nimchinski Kerry Cunningham Karrie Sullivan

Open article: The Market Is Pricing a Variable. What's Actually Happening Is a Cascade. by open.substack.com

The Market Is Pricing a Variable. What's Actually Happening Is a Cascade.

Superb assessment of the AI v SaaS economics by Sam Jacobs at Pavilion. Highly recommended reading.

Open article: Agentic Math by open.substack.com

If you’re not yet following David LaCombe, M.S., you’re missing out on some of the best expressed GTM thinking on LinkedIn. 

Most GTM teams think of #Incrementality in terms of budgetary efficiency. And while that’s not wrong, it’s not even close to the whole enchilada. 

Incrementality in the GTM world is about more causally substantive connections with desirable business effects, leading to the necessary outcomes. It’s a ratio, not between this channel and that MQL, but between CAC and better deal volume (revenue), velocity (cash flow) and size (margin opportunity).  

Did the company’s Big Three Outcomes improve because of GTM expense?

The answer is not about budget allocation. 

It’s about capital allocation.

Boards and CFOs aren’t anti-marketing. They’re anti-mystery math.
 
They’ve watched teams celebrate Return on Ad Spend (ROAS) while cash gets tighter. They’ve seen “attribution wins” that didn’t move revenue. And they know one hard truth: 𝘆𝗼𝘂 𝗰𝗮𝗻’𝘁 𝘀𝗰𝗮𝗹𝗲 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗰𝗮𝗻’𝘁 𝗽𝗿𝗼𝘃𝗲.
 
That’s why incrementality matters.
 
Incrementality answers the only question that counts: 𝗪𝗵𝗮𝘁 𝗱𝗶𝗱 𝗼𝘂𝗿 𝗚𝗧𝗠 𝗲𝗳𝗳𝗼𝗿𝘁 𝗰𝗮𝘂𝘀𝗲 𝘁𝗵𝗮𝘁 𝘄𝗼𝘂𝗹𝗱𝗻’𝘁 𝗵𝗮𝘃𝗲 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱 𝗮𝗻𝘆𝘄𝗮𝘆? Not “who got credit,” but “did we change the outcome?”
 
If you’re getting started, learn the smallest unit of incrementality:
 
𝗢𝗻𝗲 𝗴𝗿𝗼𝘂𝗽 𝗲𝘅𝗽𝗼𝘀𝗲𝗱 𝘁𝗼 𝗮 𝗚𝗧𝗠 𝗮𝗰𝘁𝗶𝗼𝗻 𝘃𝘀. a comparable group not exposed;  measured on one outcome over one time window.
 
That single contrast is the foundation of mastery.
 
Start with a simple holdout (email, retargeting, branded search). Measure lift. Calculate cost per incremental result. Then iterate.
 
Small businesses don’t need perfect measurement. They need 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗴𝗿𝗮𝗱𝗲 𝘁𝗿𝘂𝘁𝗵.

𝗜'𝗹𝗹 𝗱𝗿𝗼𝗽 𝗮 𝘀𝗶𝗺𝗽𝗹𝗲 𝗲𝘅𝗮𝗺𝗽𝗹𝗲 𝗼𝗳 𝗶𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹𝗶𝘁𝘆 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝘀 ⤵️

#GTM #Incrementality

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Mark Kilens

Mark Stouse

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The Battle

Mark Kilens

Mark Stouse

Followers

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Engagements

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Mark Kilens's LinkedIn Performance

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