Fractional CMO | GTM Visionary

Business-first mindset fractional CMO with experience harmonizing business strategies and marketing tactics across 8 industries.

Christian Hollums

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

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Very important updates to the LinkedIn Algorithm. This is essentially the same realities in AI Search applied to LI activity. 

One additional reality: in AI Search’s eternal quest for the most credible content, it prioritizes sources and sites most known to be credible. LinkedIn is making these internal changes in part because a lot of user content posted there is dragging down LinkedIn as a credible source of information.

Open article: The Unofficial LinkedIn Algorithm Guide, Refreshed AGAIN by open.substack.com

The Unofficial LinkedIn Algorithm Guide, Refreshed AGAIN

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

I really appreciate the questions I've been receiving about the difference between correlation and causality, and how that extends into specific GTM use cases like Marketing Mix Modeling, Incrementality, Forecasting, Risk, and Decision Governance.  The piece below, and it's linked piece on Substack, work hard to give you clear, substantive answers.

I wrote this post in help answer a lot of questions we are getting about correlative processes like #MarketingMixModeling, which is a specific use cas

Open article: When Correlation Stops Working: Econometrics, Actuarial Science, and the Emerging Limits of Generative AI by Mark Stouse • 3 min read

When Correlation Stops Working: Econometrics, Actuarial Science, and the Emerging Limits of Generative AI

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

This is a post I’ve resisted writing. 

The last thing anyone wants is to have #War held up as an exemplar of contextual volatility. Yet I think we all have felt the effects of many types of Conflict over the past 11-12 years, if not longer. 

What marks out the #Now is the accelerating loss of buffering factors that have enabled us to “live with” the stressed systems. We all stand more exposed to what Shakespeare called “the slings and arrows of outrageous fortune.”

I’ve probably reviewed 50,000 causal models in the past 20 years as a business leader sitting in many different roles. After a while, you begin to see patterns in the causal relationships.  

One is that the time it takes for “bad” to generate effects is much faster than “good.”  

We’ve seen this in the past 11-12 days of war in the Middle East. But time lag is a continuum of impacts. There’s a sort of “down payment,” and then there’s a series of medium and longer term effects that take longer to pay out. In most circumstances, these restructuring effects are far more durable and damaging than whatever showed up earlier. 

Causal models can be uncomfortable because they reveal the delta between what we’ve done and how well that squares with #Reality. What is Reality? It is what we crash into when we are wrong.

Open article: How Trump’s War With Iran Changed the World in a Week by nytimes.com

How Trump’s War With Iran Changed the World in a Week

I want to share Kerry Cunningham’s post because it opens up a very necessary conversation. 

Proof Causal.ai deals with this issue almost everyday. There is precious little common ground around definitions of Risk and Value inside customers today, which is why the role of Finance — for good or ill — continues to increase.  

With respect to Proof, the risks to one buyer represent the value to another. Causal models can reveal where GTM is making decisions that no longer are effective, and those teams are often threatened by that prospect. However, it is exactly that outcome which promises value to Finance, whose objective is to be the best possible steward of shareholder capital and shareholder value. 

The fact that Causal AI ends up aligning functions and Finance for collective benefit is not something that many buyers grok during the sales motion because their Fear gets in the way. 

It’s only later that they see the fact that Causal models derisk their decisions more effectively than any other approach.

For every complex problem there is an answer that is clear, simple, and wrong. — name redacted

Per the good folks at LinkedIn, 40% of buying processes that are abandoned are abandoned because the buying group can’t come to consensus.

There are multiple options for addressing a business need, but the business chooses to do nothing.

That is a negative sum game for all the competitors.

How many of you are fighting negative-sum battles all day every day?

Buyer confusion or indecision results from how we compete for the business, and that actually reduces total category spend.

When every company needs somebody’s version of the offering, total number of deals in the category is probably not impacted  (but I’m open to thoughts on that!).

But for everyone else, when each competitor promotes its approach as the only real way to solve the problem, that communicates to potential buyers that the category itself has not matured to the point where it is a safe investment. 

The category becomes less buyable.

Confusion increases decision friction and reduces buyer confidence.
·     Deals stall.
·     Projects get postponed.
·     Budgets get reassigned.

Competitor Derogation
Most companies I have worked for have had an official policy of not saying bad things about the competition. Most of those companies do not enforce that policy and actively contravene the policy all day every day.

It can absolutely feel like that’s what one has to do to win deals. I get that. But if your category is a nice to have, or the minimum viable version of your category is good enough, you’re probably contributing to a negative-sum game, reducing spend.

Vendor Hype Wars
When every company in a category claims category-defining innovation...

Buyers may discount all the claims as noise.

Trust & confidence decline.

And when trust and confidence decline, so does spending.

Ironically, the vendors that are competing hardest for their share may be shrinking the market they depend on.

That’s a negative-sum game.

Is there a better way to compete? Is it naive to think that b2b companies could choose to compete in a less negative-sum way?

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Christian Hollums

Mark Stouse

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The Battle

Christian Hollums

Mark Stouse

Followers

Posts

Engagements

Likes

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 Stouse's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreMark Stouse's LinkedIn Performance