For the last decade, if you could pull data and put it into a chart, you were safe. We called this being “data-driven.”

But companies are slowly realizing that being data-driven on a noisy foundation is dangerous. They have plenty of charts. What they are missing is the truth behind them.

We are shifting from an economy that rewards Pattern Finding to one that rewards Causal Thinking.

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The Difference

Most people use “correlation” and “causation” interchangeably. In business, they are opposites.

Correlation is “What Happened.”

• You see that on rainy days, umbrella sales go up.

• You see that when you send more emails, revenue goes up.

• AI excels here. It can scan millions of rows of data to find these relationships instantly.

Causation is “Why It Happened.”

• Did the rain cause the sales? Yes.

• Did the email cause the revenue? Maybe not.

Consider a product team analyzing feature usage.

The Pattern: Data shows that users who configure “Advanced Settings” retain 3x longer than those who don’t. The dashboard implies a clear win: Force every new user to configure settings during onboarding.

The Reality: The strategy fails. The settings didn’t cause the retention. The user’s high intent caused both.

Only people who already loved the product bothered to customize it. The feature was a filter, not a factory. When you mistake the two, you waste months engineering “onboarding flows” that force casual users to act like power users, annoying everyone and fixing nothing.

The Human Fortress: Structured Intuition

There is a fear that AI will replace all analysis. This misunderstands what analysis is.

AI is a prediction engine. It assumes the future will look like the past. It sees the noise, but it cannot see the context. This is where Intuition enters the equation.

We often treat data and intuition as enemies. “Don’t trust your gut, trust the data.” This is wrong. Good intuition is actually just rapid, subconscious causal reasoning.

• When a CFO looks at a forecast and says “that looks too high,” they aren’t guessing. They are sensing a hidden variable the model missed.

• When a marketer says “customers won’t like this copy,” they are predicting a human emotional reaction that no dataset can capture.

Causal thinking doesn’t replace your intuition; it sharpens it. It gives you the vocabulary to explain why your gut is right. It allows you to say, “This correlation is a mirage because of X factor.”

AI can process the “what.” Only a human can intuit the “why.”

The Universal Language of Truth

This isn’t a new invention for business. We are just finally catching up to the rest of the serious world.

If you look outside our industry, you see that the “invisible rules” of causality govern everything that matters.

• In Medicine: Researchers don’t just observe that patients taking a pill get better. They run randomized trials to prove the pill caused the healing, separating the cure from the placebo.

• In Law: A prosecutor cannot just show a suspect was at the scene. They must prove beyond a reasonable doubt that the suspect’s actions caused the crime.

• In Economics: Experts look for “natural experiments” to see how policy changes actually shift human incentives.

We have been playing by different rules in business - accepting “bent rulers” and comfortable half-truths because the numbers looked nice on a slide. That era is ending.

The Path Forward

This shift - from observing patterns to understanding cause - is the most transferable skill you can learn.

It applies to marketing, yes. But it also applies to leadership, product design, and personal growth. Once you learn to see the difference between a signal and noise, you cannot unsee it.

Over the coming weeks, we are going to explore these “invisible rules.” We will look at how to think like an epidemiologist when diagnosing a business problem. We will look at how to think like a judge when weighing evidence.

The goal is not to become a statistician. The goal is to nurture a specific kind of curiosity.

It’s the curiosity that refuses to accept the easy answer. The instinct that asks, “Is this true, or does it just look true?”

In a world drowning in data, this clarity is the only competitive advantage that lasts.

- Talgat

We are living through an absurd paradox. We have more data than we have ever had. We have more precision than ever before. Yet somehow, we are less certain about what is actually working than the Mad Men generation was with their three-martini lunches and gut instincts.

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The promise was that data would bring truth. Instead, it brought noise. The ecosystem fragmented. Walled gardens built higher walls. One human is now counted as five different users across five different platforms. Bots inflate the numbers, and bad actors game the systems.

We didn’t get clarity; we got chaos disguised as sophistication.

This problem extends far beyond marketing—it is everywhere decisions meet measurement. But nowhere is it more painful than inside a growth team trying to scale.

The Symptom: The Tower of Babel

Walk into any marketing meeting and watch what happens when someone asks the simplest question in business: “What is actually working?”.

• The performance marketer cites their attribution model to prove efficiency.

• The brand team references their MMM study to prove lift.

• The CFO questions why none of these numbers match the actual revenue in the bank account.

Everyone is right. Everyone is wrong. And everyone knows something is fundamentally broken.

The organization develops a form of multiple personality disorder. Teams retreat into tribalism, defending their specific metrics because admitting the metrics are flawed feels like admitting incompetence.

But it’s not incompetence. It’s a structural failure. We are trying to build a skyscraper, but our tools are lying to us.

The Bent Ruler Problem

Imagine you are building that tower. Your team is brilliant. Your materials are solid. But here is the problem: every single ruler you are using is bent .

And they aren’t just bent; they are bent in different directions.

• The Attribution Ruler: It favors whatever happens last. It tells you the kicker scored the points, ignoring the team that moved the ball down the field.

• The Ad Platform Ruler: It is calibrated to show its own contribution in the best light. It claims credit for sales that would have happened anyway.

• The Agency Ruler: It is calibrated to demonstrate value and justify the retainer.

• The MMM Ruler: It relies on assumptions from six months ago, before the world changed.

The problem isn’t that these rulers exist. The problem is that we pretend they all measure the same thing. We take seventeen different realities and try to stitch them into one coherent strategy.

We have become data-driven on a noisy base, rather than evidence-based on actual business outcomes.

The Coping Mechanism: Tech Stack Bloat

When the rulers don’t align, reality gets fuzzy. And when reality gets fuzzy, organizations panic. But rather than fixing the measurement, they reach for a coping mechanism: Tech Stack Bloat.

This follows a predictable cycle of avoidance:

1. The Unification Myth: We buy another dashboard to “unify everything.” We convince ourselves that if we just pool all the bad data into one place, it will magically become good data.

2. The Complexity Shield: We buy platforms with “proprietary methodology” and “advanced AI”. The complexity becomes a feature, not a bug. If the math is complicated enough, nobody can prove it’s wrong.

3. The Sales Cycle of Hope: Each new tool is sold by someone who genuinely believes it’s the answer, and bought by someone desperately hoping it is.

It is motion that looks like movement. Buying a new tool feels like progress. It allows you to tell the board, “We are solving this,” without admitting that for the last six months, you were flying blind.

But adding more bent rulers doesn’t create one straight one. It just increases the noise.

The Compounding Ignorance Tax

While you are busy integrating the new “source of truth,” you are paying a tax. Every day you operate with bent rulers, you pay for it in compounding wrong turns.

• Month 1: You kill a campaign because it looked inefficient in a 7-day attribution window. In reality, it was driving customers who bought three weeks later.

• Month 3: You scale retargeting because the numbers are beautiful. But you are just paying to reach people who had already decided to buy.

• Month 6: You restructure based on an MMM study. By the time the study is finished, the market dynamic has shifted twice.

By the end of the year, your metrics have completely divorced from your P&L.

Here is the irony: Companies with the most sophisticated measurement setups often pay the highest ignorance tax. They have the most tools, the most analysts, and the least truth.

From Data to Evidence

The shift you need to make is uncomfortable but simple: Stop trusting models and start testing actions.

You don’t need more data. You need better evidence.

Measurement Clarity isn’t about a new tool. It is the discipline of observing what actually happens to the bottom line when you act - not what some model suggests might have happened in a parallel universe.

If you want breakthrough growth, forget the fancy tools and ask the questions that matter:

1. When you invest a dollar, can you trace it to actual value created?

2. When you change something, can you see the impact in your business, not just the dashboard?

3. Are teams optimizing for reality, or for whatever makes their metrics look good?

Get the ruler straight. Base your decisions on evidence, not noise. Once you see reality clearly, there is no going back.

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Talgat

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Nine years in measurement inside BigTech taught me the technical side. The last 12 months I’ve been fully on the other side. Working directly with business owners. Running tests. Building frameworks that actually work when you’re spending your own money and need answers in weeks, not quarters.

I’ve also been watching. Thinking. Observing the patterns that keep this industry stuck. Because if I’m going to contribute to real change, I need to understand what’s blocking it.

So when I invited Gabriele Franco (CEO of Cassandra) to sit down and talk, I wanted to compare notes. Two people who’ve been in the trenches, seeing the same patterns, trying to solve the same problems from different angles.

What came out of that conversation confirmed what I’ve been seeing: we know what works. The tools exist. The methods are proven. But most businesses are still stuck measuring the wrong things in the wrong ways.

Not because they’re behind. Because the path forward isn’t clear.

The Pattern We Both Keep Seeing

Gabriele shared a story I’ve heard variations of a dozen times:

Client spends $1M a month. Shopify shows actual revenue. Then they add up what Meta reports, what Google reports, what TikTok reports. Total comes out 40% higher than reality.

The math doesn’t work. It can’t work. But everyone just... accepts it.

Your platforms report what they can see. The problem is what they see is incomplete and optimized to make them look good. Nobody’s lying. The system just wasn’t designed to show you truth.

Most businesses pick the dashboard that feels most accurate, or average the numbers, or build some hybrid model. They keep moving. Keep spending. Keep trusting metrics that don’t reflect what’s actually happening.

The waste is real. Usually 20-40% of total spend. You just can’t see which 20-40%.

Where Most Businesses Actually Are

I built a framework I call the Measurement Consciousness Atlas. Not just because I love frameworks. Because after working with many businesses and marketing teams, I kept seeing the same patterns. The same stuck points. The same transformation triggers.

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Stage 1 is where everyone starts. You trust platform metrics. You optimize for ROAS or CAC. You make decisions based on attribution. It feels scientific because the dashboards are precise.

The waste happens here. You just don’t see it yet.

Stage 2 is where change begins. Something breaks the illusion. Usually the numbers stop adding up. Finance flags it. Or you pause a channel and sales barely move. Or you add everything up and realize it’s mathematically impossible.

You know something’s wrong. You don’t know what’s right.

This is the most important transition point. Most businesses get stuck here because the next step requires questioning everything they’ve been doing.

What typically blocks the move from Stage 2 to 3:

• Identity protection - The CMO who built their career on these metrics can’t admit they might be wrong

• Political inertia - Changing measurement means admitting past decisions were based on wrong data

• Fear of simplicity - “If the answer was this simple, why didn’t we do it before?”

Stage 3 is where transformation happens. You stop treating attribution as truth. You test everything. You measure what actually moves the needle. You know your baseline.

Companies that make it here typically find the waste within 30 days. Not because they suddenly got smarter. Because they finally asked the right questions.

Stage 4 is where you build your own infrastructure. The economics shift. You outgrow what standard platforms can offer. You build your own systems because you understand the mechanics well enough to do it better.

The gap between these stages isn’t technical capability. It’s organizational willingness to question what everyone’s been treating as fact.

Two Approaches to the Same Problem

Gabriele and I are working on this from different angles.

He’s building Cassandra - making MMM accessible, faster, less dependent on six-month consulting cycles.

I’m focused on upleveling decision makers. Business owners who need practical frameworks to get to truth fast enough to act on it. Not perfect academic truth. Directional truth that improves your P&L this quarter.

We agreed: the companies that win aren’t the ones with perfect data. They’re the ones who move faster from confusion to clarity. Who waste less this month than last month. Who can prove what works and scale it before their competitors figure it out.

In the next few weeks I’ll share:

• How to diagnose where you actually are

• What blocks the transition between stages

• Practical guides to create breakthrough moments

If you’re spending $30K+/month on ads and suspect you’re wasting 20-40% but can’t prove it, join the waitlist → See If you qualify

Taking small batch of founding members for pilot. Need actual decision authority and willingness to act fast.

What We Didn’t Say On Camera

This industry has built a lot of infrastructure around keeping measurement complicated.

Not maliciously. It’s just the natural result of incentive structures. Complex feels valuable.

Here’s what I’ve learned over years of doing this work with real businesses:

The people who move fast are the ones with skin in the game. They’re spending their own money or directly responsible for bottom line P&L. They feel every wasted dollar. And when they see truth, they act immediately.

No committees. No approval chains. Just: show me what’s broken, let’s fix it, let’s move on.

That’s who this is for.

You don’t need perfect. You need better than yesterday. And you need it fast enough to matter.

Talgat

P.S. It’s been a while since my first post. A lot happened - I’ll share the progress and lessons soon. Posting more frequently from now on. Quality over quantity. Thanks for staying around. More to come.

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Watch the full conversation on YouTube

What to expect

Incrementality Insider promise

Expect more than just information - expect a transformation. You'll gain the knowledge and tools to break free from outdated marketing practices, unlock new levels of efficiency, and achieve results that truly matter to your business or career. This is your journey to a New You — a more confident, capable, and successful you in the ever-evolving advertising world.

Transformation in practice: Measurement maturity roadmap

Unlocking your potential is a journey, not a destination. It involves evolving your understanding and practice of incrementality measurement from novice to mastery. I'm developing a roadmap outlining the key stages and steps to advance through each level to guide you. Think of it as your GPS for navigating incrementality. Stay tuned for the next issue to get this roadmap.

Guiding Principles

"If you don't stand for something, you'll fall for anything." 

- Gordon A. Eadie

The marketing world is full of a bunch of hype, and everyone has a different opinion on what works. It's easy to get lost in all that noise and lose sight of what actually moves the needle. That's why I stick to these core principles in everything I do. They're the foundation that Incrementality Insider is built on - shaping how I approach things, what I analyze, and every decision I make.

1. Fresh & Exclusive perspective: Deliver unique perspectives on incrementality measurement that you won't find anywhere else. No rehashed ideas or recycled content, just original thinking to help you stay ahead.

2. Clarity & Simplicity: Break down complex ideas into easy-to-understand explanations, avoiding jargon and theory. The main goal is to make incrementality knowledge accessible to everyone.

3. Unbiased & Vendor-Neutral: Maintain an unbiased perspective, focusing on universal principles and practices rather than specific products, platforms or methodologies. 

4. Evidence-Based: Back up recommendations with real-world examples, case studies, and the latest research in incrementality measurement domain. 

5. Actionable Strategies: Go beyond theory to provide practical tactics and frameworks that can be implemented to improve marketing ROI.

6. Continuous Improvement: Stay at the forefront of incrementality measurement, providing subscribers with cutting-edge knowledge and emerging best practices.

7. Leading the Evolution: Committed to advancing the field of incrementality measurement through thought leadership, community building, and advocating for best practices that benefit the entire industry.

That's it for today!

If you have any ideas, suggestions, or feedback, hit the reply button.

Talgat