In November 2022, ChatGPT went public. It felt like magic.

Around the same time, my daughter was born. I remember uninstalling X for a while because it felt like two different inflection points were happening at once — one personal, one technological.

Back then, AI felt like a clever toy. A better autocomplete. A smarter Google.

Fast forward to today. AI can:

• Write and execute code.

• Query databases.

• Use APIs.

• Plan multi-step workflows.

• And increasingly, correct itself.

And that shift did not happen gradually. It happened because the stack matured.

If you run a business, this matters.

I wrote and researched this piece (with AI) mainly for myself as a business owner and a consultant, to try to wrap my head around what has changed so far and what is likely to be impacted because of this going forward.

This piece walks through what actually happened between 2022 and 2026.

Not the hype version. The structural, factual version. Simplified.

So you can understand what is likely to be impacted (Part 2) — and position yourself accordingly (Part 3).

Phase 1: “It’s Just a Chatbot”

(November 2022 – Mid 2023)

Trigger phrase: “Let’s play with this.”

When ChatGPT launched, it felt magical.

You typed a question. It responded fluently. It followed instructions.

What made it different wasn’t raw intelligence. The transformer architecture had existed since 2017. What changed was reinforcement learning from human feedback (RLHF) — meaning the model was trained to behave in a conversational, helpful way.

Suddenly, AI wasn’t an API in a research lab.

It was a chat window.

And entrepreneurs — myself included — started testing it against real work.

At this stage, most people thought:

“This is a smarter Google.”

It wasn’t. But it looked like one.

Meanwhile, open-weight models started emerging. Baseline language models became commoditized quickly. The foundation layer started flattening.

Phase 2: “Make It Bigger”

(Mid 2023 – Early 2024)

Trigger phrase: “What if we just give it more context?”

The next obsession was memory.

Context windows expanded from thousands of tokens to hundreds of thousands — and eventually into the million-token range.

On paper, that meant you could:

• Upload entire codebases

• Paste in financial data rooms

• Feed hours of transcripts

It felt like the memory problem was solved.

But here’s the nuance most people missed:

Bigger memory ≠ better reasoning.

Research showed that models recall best from the beginning and end of long inputs — and struggle with information buried in the middle.

In other words:

A bigger desk doesn’t make you more organized. It just gives you more room to create a mess.

Phase 3: “Stop Stuffing. Start Using Tools.”

(Late 2023 – 2024)

Trigger phrase: “Can it actually do the thing?”*

This is where things get interesting.

Instead of stuffing more data into the model, researchers and labs started asking:

What if the model doesn’t need to know everything? What if it just needs to use tools?

Early systems showed models could search the web and cite sources. Research demonstrated that models could learn when to call APIs. Architectures emerged that interleaved reasoning and action.

Translated into business terms:

Before:

• AI describes how to solve a problem.

After:

• AI executes parts of the solution.

That’s a structural shift.

Tool calling allowed models to output structured requests — which your software executes deterministically — and then feed the results back into the model.

Instead of:

• Calculating in-text (error-prone)

• Parsing raw logs

• Guessing database answers

The model can:

• Write SQL

• Call a calculator

• Execute code

• Retrieve precise data

Phase 4: “Let It Think Longer”

(Late 2024)

Trigger phrase: “What if we give it time?”

Instead of answering immediately, new model architectures were trained to “think” internally before responding — allocating more compute at inference time.

This concept is called test-time compute.

In simple terms:

Earlier models were reflexive. These models deliberate.

And the results were dramatic on reasoning benchmarks.

But here’s the nuance again:

This doesn’t mean the base intelligence exploded exponentially. Some researchers argue it’s a smarter search over existing knowledge.

Still — in practice — it improved:

• Multi-step math

• Logical deduction

• Coding reliability

Business translation:

You can now pay for “slow thinking” when it matters. Just like hiring someone who doesn’t answer instantly, but answers correctly.

Strategic takeaway:

Inference became a new scaling axis. Compute is no longer just about training. It’s about thinking.

Phase 5: “Viability Threshold Crossed”

(2025)

Trigger phrase: “Okay… this actually works.”

This is where things started to feel different.

Several developments converged:

• Tool calling matured

• Reasoning modes stabilized

• Benchmarks shifted from trivia to execution

• Coding agents moved from novelty to viability

Now — let’s be precise.

Coding is not universally “solved.”

But it crossed a viability threshold for many structured tasks.

We moved from:

“This is impressive.” To: “We can build workflows on this.”

And that’s the inflection.

The Hidden Constraint: It’s No Longer Model IQ

At this point, something subtle happened. The bottleneck shifted.

It is no longer:

• Can the model do it?

• Is the context large enough?

• Can it reason?

It is now:

• Can we design reliable systems around it?

• Can we verify cheaply?

• Can we integrate into messy organizations?

• Can we control blast radius?

Because here’s the uncomfortable truth: Agentic systems fail in new ways:

• Infinite loops

• Silent hallucinations

• Tool misuse

• Security vulnerabilities

More agency = more blast radius.

Intelligence is now cheap. Verification is expensive.

BETTER ≠ PERFECT & CAPABILITY ≠ RELIABILITY

So Why Does It Feel Different Now?

Because five layers matured at once:

1. Usable interfaces

2. Large-enough memory

3. Reliable tool invocation

4. Inference-time reasoning

5. Standardization of integrations

The stack stabilized.

And when a stack stabilizes, applications explode.

We are entering that phase.

Overarching Lesson: Intelligence Is Becoming a Commodity

Let me steelman the skeptics for a second.

Yes:

• Long context still degrades.

• Agents are brittle without guardrails.

• Security risks are real.

All true. And yet.

The cost of usable intelligence has collapsed.

And whenever a constraint collapses, value shifts elsewhere.

• When storage became cheap, databases won.

• When bandwidth became cheap, streaming won.

• When compute became cheap, SaaS won.

When intelligence becomes cheap…

• Orchestration wins.

• Verification wins.

• Context wins.

What This Means for You

If you run a business, here’s what you should internalize from this journey:

• This is not a chatbot story anymore.

• The unlock was tool use + reasoning, not just bigger models.

• The bottleneck is now organizational design.

Leverage compounds fastest where:

• Work is structured (or can be made so)

• Verification is cheap (or can be made so)

• Feedback loops exist (or can be created)

In Part 2, I’ll build a framework for evaluating where your company sits in this shift.

For now, sit with this:

AI is no longer a feature. It is becoming infrastructure.

And infrastructure changes competitive landscapes faster than people expect.

Yesterday, I sat in a room at the Amplitude Marketing Summit, surrounded by a group of amazing Marketing Analytics Specialists. Amplitude (the giant of Product Analytics) was pitching their vision for the future of marketing measurement inside their ecosystem.

First of all, I want to applaud Amplitude. Not just because it was an amazing event and a lot of fun, but I think it’s very smart to invite a bunch of opinionated people from an industry that you want to serve and let them rant about your product. More companies should consider this to get valuable feedback and insights, IMO.

The promise was seductive: “Unify your product and marketing data to see the full journey, and act on it immediately.”

The implication was clear: If we just track the user cleanly enough, we can calculate a precise ROAS (Return on Ad Spend) for every single user, down to the penny, and optimize accordingly (with a load of AI on top, obviously).

But as I watched the roadmap unfold, I realized there is a fundamental friction here. It isn’t just a tooling gap; it’s a worldview gap.

Product Logic assumes that you can understand and optimize the world by stitching together user-level events into a clean, deterministic story. Marketing Reality is that the “Real User ID” does not exist, severed by privacy laws, walled gardens, and messy human behavior.

If we try to force Marketing into a Product mold, we risk optimizing for a reality that no longer exists. (Or maybe, never even existed…)

The “Swiss Army Knife” Fallacy

The core tension starts with the idea that one measurement model works for everything.

In the Product world, the “User” is the center of the universe. This works beautifully for B2B SaaS or Product-Led Growth (PLG). If you are Slack or Dropbox, the marketing journey is the product journey. A user signs up, logs in, and interacts. The data is deterministic. The ID is golden.

But if you are a D2C Ecommerce brand, a publisher, or an Enterprise with an 18-month sales cycle, that logic falls apart.

Take the concept of the “User Journey.”
Product Logic tries to flatten everything into a single timeline per user.

But right now, I have three tabs open on Amazon for three completely different products. I am one “User,” but I am exhibiting three distinct Intent Clusters.

• Tab 1: Buying diapers (High urgency, low research).

• Tab 2: Looking at extensive reviews for a camera (Low urgency, high research).

• Tab 3: A book I clicked on by accident.

If you flatten this complexity into a single “User LTV” or “Session Value,” you erase the context of why I am there.

Product Logic treats the “User” as the atomic unit of analysis, but in marketing, the atomic unit is often the Intent or the Context, not the person.

The Myth of the Pre-Login “User”

This conceptual mismatch (Intent vs. User) is already a massive hurdle. But the real crisis happens when you realize that even if you want to track that User, you physically can’t.

Product Logic relies on a clean, deterministic chain: User sees ad -> User clicks -> User converts.

This works in a logged-in state. But in the early stages of the customer journey (the “Anonymous Void” where marketing actually happens) this chain is broken. And depending on the business model this could apply to the largest part of your traffic.

If you think in terms of “User Journeys” here, you are optimizing for a ghost.

1. The Technical Severing (Cross-Device & Privacy)

The “User” is actually just a browser cookie or mobile identifier. And it’s not getting any more accurate or useful…

• Cross-Device Fracture: A user sees an ad on Instagram (Mobile In-App Browser), gets interrupted, and buys later on Safari (Desktop). You see a “Bounce” on mobile and a “Direct” conversion on desktop, unrelated. The “Journey” is a lie.

• Browser Hostility: Apple’s ITP (Intelligent Tracking Prevention) caps cookie lifespans. If a user clicks an ad today but buys in 8 days, the tie is gone. The journey is erased. (And - IMO, Apple is not done playing this game.)

2. The Legal Severing (Consent)

In Europe (GDPR) and increasingly in the US (CCPA), the journey doesn’t start when the user visits; it starts when the user consents.
If 30% of your traffic rejects tracking, your “User Journey” map has 30% holes in it. You aren’t seeing a complete picture; you are seeing a survivor bias of people who like clicking “Accept.”

This combination—technical fragmentation and legal gating—means that for the vast majority of the marketing funnel, the “User” is a flawed concept. You cannot stitch what you cannot see, and you cannot attribute value to a ghost.

The “Statistical Shield”: Why Complexity Won’t Save You

When marketers realize the “User ID” is broken (thanks to Apple’s ITP, tracking prevention, and cross-device fractures) panic sets in.

The common reaction is to run toward complexity. We start hearing buzzwords like “Probabilistic Modeling,” “Advanced MMM,” and “Triangulation.”

I’m very interested in these approaches, try to read as much as I can about them, and always invest time and money if my customers want to explore these. But I’m increasingly more skeptical about their real world applicability, especially for my segment of customers (Large small businesses, or Small enterprise.)

In my opinion, often this is just using statistical models as a shield. The target of my critique isn’t the math itself, it’s the misuse of these models as an unquestionable authority.

It feels safe to say, “Our proprietary Bayesian model attributes this to Facebook,” because it sounds scientific. But often, these models are just black boxes that validate our existing biases.

We feed them messy data, apply complex math, and treat the output as gospel.

Complexity is not a strategy. If you can’t explain why a channel is working without pointing to a black-box algorithm, you aren’t measuring; you’re guessing with confidence.

If we can’t trust the simple tracking, and we shouldn’t hide behind complex models, what is left?

There is no silver bullet. In my opinion, you can stop trying to find the perfect tool and start building a stack that respects and surfaces the messiness.

Here is where I would focus:

0. Acknowledge, Accept & Educate on the Complexity

First and foremost, before you write a single line of code or sign a contract for a new tool, you need to address the human element.

The single biggest reason marketing measurement projects fail is not technical. It’s cultural. It is the mismatch between the C-Suite’s desire for absolute certainty and the messy reality of the ecosystem.

• Acknowledge the Goal: You must instill a basic understanding across the organization (especially with Finance and Leadership) that the goal of Marketing Analytics is not “Accounting” (perfectly tracking every penny). The goal is “Navigation” (knowing which direction to steer the ship) and “Making Better Decisions, Faster”.)

• Accept the Imperfection: You will never get perfect data. It does not exist. You need to explicitly define, document and accept the tradeoffs that you’re making and onboard everybody on to them to stop re-hashing the same discussions over and over again.

• Educate Continuously: This is not a “set and forget” project. The landscape changes every time Apple releases an iOS update or a regulator passes a new law. You must commit to an ongoing process of educating your stakeholders on why the numbers look the way they do.

If you don’t set these expectations upfront, every technical solution you build will be viewed with suspicion when it inevitably fails to match the bank account perfectly.

1. Build the Best Possible Identity Graph

Now, this might feel contradictory to my earlier point of not being able to cram marketing analytics in the deterministic product analytics setup. The key here is “Best Possible” Identity Graph. So we’re accepting the imperfection and we’re also accepting that it’s not deterministic and it’s not fixed in time.

You can’t measure what you can’t hold onto. You need to make your grasp on user identity across (anonymous/unknown) browsers as strong as possible to improve your understanding and connect as much as possible.

• The Goal: Stitch the session as aggressively you can. (Take into account legal.)

• The Tactic: Don’t just rely on cookies. Use fallback logic. Look at hashing emails on entry (e.g., newsletter signups) to link that browser to a backend ID. Depending on legal options and customer types, evaluate IP lookup and fingerprinting.

This is an essential part and one key element is that the identity graph should continuously be enriched and updated retroactively to include new knowledge (e.g. new devices/browsers belonging to the same user).

Doing this well, often requires multiple things to work well together. It is very unlikely that a tool can generate a useful User Identity Graph out of the box. (CDP’s tried…) The main unlock in doing this well is likely inside something that is unique to your business.

(Example: a former US client had great success identifying their B2B users mobile devices by sending certain app-activation emails at times where the customer was more likely to open the email on their mobile device instead of desktop (early and late work-days). The redirect from the email link to the app was then able to identify their mobile browser and thus stitch their previous mobile journey to the rest of their desktop journeys.)

2. Segregate Your Landing Pages

We rely too much on advertising parameters that get stripped out by privacy filters. (And I’m fearful that in the future it’s not just click_ids that will get stripped by browsers.)

• The Goal: Make the traffic source undeniable, even if the tracking pixel fires blank, by falling back on the landing page for source/campaign attribution.

• The Tactic: Create specific landing pages for specific campaigns per source.

  • If you are running a specific Facebook campaign, land them on “/f-offer-a.” and send the Google traffic to "“/g-offer-a.”

  • Work with your SEO team to make sure these pages are not indexed and not duplicate content.

  • Rewrite all traffic that lands on these pages as “direct” or other unlikely sources to the intended source of the ad traffic aimed at that page.

You will get your ad spend out of the ad platforms in an aggregated format. Most likely something like campaign per day. It’s essential that you ‘smear it out’ as accurately as possible across all incoming traffic from that source/campaign for that day so as not further deteriorate any downstream logic.

3. HDYHAU (How Did You Hear About Us?)

Digital data misses the “Dark Social” world (podcasts, word of mouth, Slack communities.)

• The Goal: Capture the “Zero-Party” data.

• The Tactic: Ask them. Implement a post-purchase survey.

• The Rule: When digital data is generic (Direct/Unknown), treat HDYHAU as the primary source of truth. If your analytics says “Direct Traffic” but the customer says “The All-In Podcast,” believe the customer.

• The Nuance: Human memory isn’t perfect (recall bias is real). But when digital data gives you nothing, a human guess is infinitely better. When they conflict on specifics, don’t just blindly overwrite, capture both signals to understand the difference between capture (the click) and influence (the memory).

4. Spend Time vs. Conversion Time

Most tools report on Conversion Time. (e.g., “We made 10k today”).
But if you spent the money to acquire those users 30 days ago, you are mismatching your financials.

• The Goal: Financial coherence.

• The Tactic: Attribute revenue back to the Click Time (or Spend Time). You need to know if the money you spent in September actually generated a return, even if that return happened in October. If you don’t do this, you will cut budget in months that look expensive but are actually just “maturing.”

This is another one that is a big shift from the product analytics suite.

5. Marginal ROAS

Stop optimizing for Average ROAS.

• The Goal: Understand the value of the next dollar.

• The Reality: Your dashboard shows you a blended average. It says your ROAS is 4.0. But your Marginal ROAS (the return on the extra 100 you just spent) might be 0.5.

• The Tactic: You have to swing the budget. Push spend until efficiency drops, then pull back. You can’t model this from a static report; you have to experiment with the spend levels to find the point of diminishing returns.

Watch this for a deeper explainer on Marginal ROAS:

6. Try to Make First-Touch Attribution Work

If you can get your user-stitching (Point #1) to work, see if you can get First-Touch Attribution to an acceptable level.

• The Goal: Intellectual honesty about growth.

• The Reality: Most standard attribution models (Last Click, Data-Driven) are biased toward channels that “harvest” demand (like Retargeting or Brand Search).

• The Tactic: If you have a solid identity graph, First-Touch is the most intellectually honest answer to the question “What marketing tactic is actually bringing new people into our world?” It ignores the noise of the closing channels and focuses entirely on the acquisition signal.

This is hard to do, and I’ve encountered customers now where it’s unlikely to happen (e.g. the quality of the user identity graph is not good enough to switch to first touch). But I do think it’s possible for most companies and business models.

“Don’t you believe in MMM & Incrementality?!”

Reading the above, you might think I’m some kind of Luddite who rejects modern measurement science. That’s not the case.

I’m not properly educated to explain to you if/how these models do/do not work. I think they do work, in certain cases. But I’ve not been exposed to many real world successes (and I’ve tried, and continue to try!)

My issue is that they are often sold as a default solution for everyone. And recently (~last 2 years) it seems like that has been resurfaced, but the underlying fundamentals did not seem to change…

As far as I know, MMM is useful if you have a lot of offline spend. It helps you align macro budgets. But for a digital-native brand spending $50k/month on Meta and Google? In most cases with limited spend and volatility, an MMM will not give you any actionable insights to work with.

Incrementality Testing (Geo-Lift) is arguably the gold standard for “Truth.” But it requires a specific environment to work well.

• Geography Matters: Most of our customers mainly operate in the EU, and that’s a challenge for a clean GEO holdout. In the US, you have clear Designated Market Areas (DMAs) that allow for clean control vs. holdout testing. In Europe, markets are fragmented by language and borders, making clean geo-testing significantly harder and more expensive.

• Scale Matters: To get statistical significance on a holdout test, you need volume. If you are small, the “noise” of normal sales variance will drown out the “signal” of your ad test.

So yes, use them if you have the scale and the offline mix that demands them. But don’t use them as a bypass to avoid doing the hard hygiene work listed above.

What about Triangulation? (MTA + MMM + Incrementality)

While academically ideal, I really don’t see any of my customers being able to successfully do triangulation anytime soon. Some problems:

1. The Ownership Silo: Marketing owns MTA (optimizing for growth) while Finance often owns MMM (optimizing for efficiency). When the models inevitably disagree, it triggers political turf wars over budget rather than a unified search for truth.

2. The Velocity Mismatch: You are attempting to merge real-time click data with backward-looking quarterly modeling. By the time you reconcile the two, market conditions have shifted, rendering the insight obsolete.

3. The Calibration Void: Most teams lack the advanced Bayesian frameworks required to mathematically weight conflicting signals. Instead of clarity, they get three contradictory dashboards and total decision paralysis.

It’s very hard to do one of these well, let alone 3. And then if you do all 3 well, it’s very hard to use them to fuel your decision… (I obviously don’t work with Uber, AirBnB and Booking so maybe there’s a scale where this does work?)

The Uncomfortable Conclusion

It’s not simple.

If it were simple, someone would have fixed it already.

It will likely never be simple. The ecosystem is too fragmented, the privacy laws are too strict, and human behavior is too chaotic.

It is unlikely that one single tool, whether it’s Amplitude, Google Analytics, or a specialized attribution vendor, will get you there. A lot lies in the nuance of your specific business model.

• Are you B2B SaaS or D2C Ecom?

• Is your sales cycle 5 minutes or 5 months?

• Do you rely on Google Search or Influencers?

This brings us back to the Amplitude vision. Amplitude is right to chase this. The separation between “Acquisition Data” and “Retention Data” is artificial, and bridging that gap is the next frontier. It also makes sense from the business point of view to have these things in the same tool.

But the danger lies in overselling determinism in a probabilistic world.

All those amazing “Product Analytics” features (A/B testing, CRO, Personalization) rely on that one, golden, deterministic User ID.

If we pretend that the ID is as solid in the Ad Impression stage as it is in the Logged-In stage, we build our house on sand. You risk personalizing an experience for a “user” that is actually three different people on a shared iPad, or optimizing a funnel based on a journey that never happened.

The Goal is to Do Better

The good news is that the goal is not perfection, the goal is to make better decisions tomorrow than you made yesterday… At least thats what I tell myself when I overcomplicate things and get overwhelmed with dead-ends 😁.

Stop searching for a single source of truth; build a stack of partial truths that you understand.

Sidenote: A lot of my recent thinking on attribution and marketing measurement is influenced by Constantine Yurevich of SegmentStream. He’s a bit controversial (on purpose) but I think he’s right about the non-applicability of a lot of complex statistics to marketing optimization efforts. I can highly recommend his course to challenge your thinking on this topic.

My name is Rick Dronkers, and I’m the founder of Data to Value.

Based on our work with clients across the world, I’ve seen a fundamental misunderstanding in the market. Companies invest heavily in collection and reporting, but they miss the most critical step.

We believe data-driven marketing isn’t about having perfect data; it’s about making better decisions, faster.

Here’s a look at our core philosophy and how we help companies move from just having data to using it as a strategic asset.

The Hard Truth: Your Data Has No Intrinsic Value

Let’s start with a provocative idea: By itself, your data has a negative value.

Think about it. You pay for the technical and HR costs to capture it. You pay to store it. And you assume massive risk factors related to security and privacy.

Data collection has no intrinsic value. Data transformation has no intrinsic value. Even those “insights” have no intrinsic value by themselves.

Value is only created when somebody or some system takes an action based on that data. This “activation” step is the only part that generates a return, yet it’s the part that is most often forgotten.

No Action, No Value

The Traps We All Fall Into

Because the activation part is hard, we tend to get stuck in common traps:

1. The “Waterfall” Method: We think in a linear way: Collect Data -> Analyze -> Transform -> Data Science -> ...Magic? This rarely works and often ends in projects that die before delivering value.

2. The “Silver Bullet” Solution: We look for a technology solution that promises to solve all our problems with the click of a button. It never does.

3. The “Perfection” Wild Goose Chase: We get stuck trying to get “perfect” data, optimizing for 100% accuracy instead of moving forward with 70% and taking action.

The reality is, our industry is built on a foundation of People > Process > Technology, in that order. You can’t buy technology to fix a people or process problem.

Our Approach: Think Like MacGyver

To break out of these traps, we need to think iteratively. We need to be pragmatic. We need to think like MacGyver.

Instead of building a perfect, multi-year data palace, we focus on “duct-taping” a solution together to prove it works. We focus on one simple, powerful concept: Closing the Loop.

Our model is a continuous cycle: Collect, Transform, and Activate.

Most data projects get stuck between “Transform” and “Activate.” Our entire methodology is designed to force that activation step, to close the loop as quickly as possible, and then do it again.

Data is just the gasoline. You still need a driver, a crew, and a car to win the race. Our job is to get your “minimum viable” car on the track and finish a lap.

How We Prioritize: From Ideas to IMPACT

We start by helping you move up the Data Maturity model—from Hindsight (What happened?) to Foresight (What will happen?). But we do it with a relentless focus on pragmatism.

It starts with a simple prompt:

“We hope to achieve {{improvement}}, by implementing {{data}} and use it in {{channel}} to do {{use_case}}.”

For example: “We hope to achieve improved return on ad spend (improvement) by implementing profit data (data) and use it in Google Ads (channel) to do profit-based bidding (use case).”

This simple sentence clarifies the goal. From there, we score these use cases using our IMPACT Framework:

• Importance: How important is this to your strategic goals?

• Money: What is the expected monetary impact?

• Potential: Can this use case unlock other use cases?

• Achievability: How easily can we develop and deploy this?

• Confidence: How confident are we in our estimates?

• Time to Value: How quickly can we build this?

Here’s our secret: While it’s tempting to chase the biggest Monetary value, we often push clients to prioritize Achievability and Time to Value first.

Why? Because closing a data loop is hard. Closing small loops is easier than closing big ones. We believe in getting a few reps in, building team confidence, and proving we can deliver. That momentum is what ultimately allows us to tackle the big, complex projects.

The Real Goal: Making Better Decisions, Faster

“Better” is a hard thing to define. It implies you know exactly where you are and exactly where you want to be. Often, those goals are vaguely defined and there are various opinions on what goals specifically mean to whom.

“Faster,” however, is something we can control and align on...

We are huge believers in the Jeff Bezos memo:

“Most decisions should probably be made with somewhere around 70% of the information you wish you had.” - Jeff Bezos

In marketing, most decisions are reversible, two-way doors. If you’re wrong, you can just stop it or revert it with minimal consequence. We should be optimizing for a framework that allows us to make faster decisions, not perfect ones.

This is where our hypothesis-driven approach comes to life. Instead of “Doing,” we “Test / Try.”

• If it works, we Scale and Learn.

• If it fails, we Reverse and Learn.

In both cases, we learn. We get new information that feeds the next evaluation, and the loop gets tighter. This is how you build a real data-driven culture.

Our Operating System: SOSTAC

To manage this entire process, we use the SOSTAC framework:

1. Situation Analysis: Where are we now? (This includes quantitative data and subjective surveys of your team’s data literacy and confidence.)

2. Objectives: Where do we want to be?

3. Strategy: What do we need to get there? (People, process, tech.)

4. Tactics: What does that look like specifically? (This is where the IMPACT framework lives.)

5. Action: How will we ensure we make it? (Our project management and delivery.)

6. Control: How will we measure progress?

This framework is our commitment to transparency and our way of ensuring that every piece of technical work is tied directly to a business objective.

Who We Are

We are Data to Value. We were founded in 2019 and are a fully remote team distributed across Europe and working for ambitious marketing teams around the globe.

Our core promise is simple: You work with Senior Consultants ONLY. We don’t hire junior consultants. We believe our clients deserve to work with experienced experts who can not only build the technical solutions but also guide the strategy.

We’re a team of pragmatists, not purists. If you’re tired of data projects that cost a fortune and deliver nothing, let’s talk → https://dtv.nu/explore

Let's start with a bold statement, as is tradition: the B2B Go-to-Market playbook that built a generation of SaaS companies is fracturing. (Calling things "dead" is a bit cliché, but "fracturing" feels about right… right?!)

For the last decade, we operated on a simple, comforting premise: create good content, rank on Google, get clicks, capture leads, and measure everything in a neat little funnel. (And let's be honest, it was never that neat to begin with.)

That model is breaking down. This doesn't feel like a temporary dip or a cyclical trend; it feels like a permanent, structural shift, driven by the AI intermediaries that have wedged themselves between you and your customers. Instead of pretending I have a crystal ball, this is my attempt to connect the dots, to look at what the data says, what it could mean, and the new questions it forces us to confront.

This is more than just a marketing problem. I'm convinced it's a CEO-level, board-level crisis impacting the fundamental unit economics of your business.

If you're a B2B SaaS leader, you're likely feeling this already. You're seeing once-reliable organic traffic flatten, your Customer Acquisition Costs (CAC) spiral, and the blame game between sales, marketing & customer success getting louder.

This is the chaotic reality of "The Great SaaS Reset." And the core enemy we've been fighting for years, the Data-Decision Disconnect, has just been given a massive upgrade.

What the Data Seems to Be Telling Us

For those who still think this is just about a few "AI Overviews" on Google, the data paints a brutal picture of the new buyer journey.

• Your Buyers Are Outsourcing Their Research: The B2B journey no longer starts with a Google search bar. It starts with a chat prompt. Analysts indicate that up to 90% of B2B buyers now use AI tools in their purchasing process. They aren't just "using" AI; they're letting it do the initial work of discovery for them. For me personally, Perplexity has largely replaced Google, and every in-depth question becomes a Gemini Deep Research query. (Seriously, if you haven’t tried this yet, you should. It’s a glimpse of the future.)

• The Clicks Are Vanishing: The traffic leak is real and quantifiable. A rigorous Ahrefs analysis found that the mere presence of a Google AI Overview causes the click-through rate for the #1 organic result to drop by a staggering 34.5%. For the informational "top-of-funnel" queries our content has historically relied on, some studies suggest generative answers are soaking up as much as 60% of user intent.

• The Value Exchange is Broken: This is the part that strikes me most. The entire economic model of content marketing was a fair trade: we provide value, and we get traffic in return. That deal is off. AI platforms systematically scrape expert content, but the referral traffic they send back is a rounding error. Ahrefs data shows AI chatbots account for a mere 0.1% of total referrals. They take the value and keep the audience.

This isn't just a marketing KPI issue; it's a financial reckoning. Recent benchmarks from firms like Benchmarkit show the median New CAC Ratio has jumped 14% to a painful $2.00 for every $1.00 of new ARR. As the most efficient acquisition channel gets choked off, companies are forced into more expensive channels, turning inward to expansion revenue not as a strategy, but out of necessity.

Reframing the Problem: It's Not Lost Traffic, It's Lost Trust

So, what do we do? The instinctive reaction is to panic about traffic or chase new vanity metrics like "AI mentions" to replace the old ones. I think this is a mistake.

To focus on lost traffic is to miss the point entirely.

The real casualty here is trust. Because if you can't trust your data, you can't make sound decisions. And I've always believed that measuring the wrong thing accurately is worse than measuring the right thing approximately.

The traditional marketing dashboard is now, more than ever, a house of lies.

Last-click attribution, already a flawed model, is now dangerously obsolete. It's blind to the "dark funnel" where buyers are actually influenced: in communities, on podcasts, and now, inside AI chat interfaces. In our own work, it’s not uncommon to find significant gaps between what software attribution says and what buyers self-report.

Relying on these broken models forces investment in the wrong things, creating a deep rift between GTM teams and leadership. Every budget conversation becomes a battle because the line between action and outcome is hopelessly blurred. Trust erodes. The Data-Decision Disconnect becomes a chasm.

Sketching a New Compass

To navigate this new reality, we have to stop trying to fix the old model and start exploring a new one. This requires a shift in how we think about measurement and value creation. Here are the most promising paths I'm seeing.

1. Explore a Hybrid Measurement Model

The search for a single source of truth is over. It never really existed. The most forward-thinking RevOps & Go-to-Market leaders I see are building a new compass by combining signals from three sources:

• Machine Listening: Analyze what's left. Server logs are a great start. Seeing which content assets AI crawlers ingest is a fascinating new leading indicator for influence. If the AI doesn't see you as authoritative, no human ever will.

• Mindshare Measurement (Share of AI Voice): Track your brand's visibility within AI answers. When a target buyer asks a commercial question, does your brand show up? This feels like a very new but critical piece of the puzzle.

• Human Listening (Self-Reported Attribution): Get to the ultimate ground truth. Ask your highest-intent prospects, "How did you hear about us?" This simple field is one of the most potent tools for illuminating the dark funnel.

This hybrid approach feels like the first, essential step toward a Unified RevOps Data Framework: a system where sales, marketing, and CS can finally view the same map, even if the terrain is foggy.

2. Build a Data Moat, Not More TOFU Fodder

The era of churning out high-volume, low-defensibility "what is..." articles is over. AI can do that better and faster. It seems the new content strategy must center on building a Data Moat: a portfolio of proprietary assets that an AI cannot simply summarize, but is forced to cite.

Put simply: Your content is no longer bait for a click; it’s an application for a job in the AI's brain.

This likely means investing in things like:

• Proprietary Data & Research: Commissioning your own industry surveys and benchmark reports. Become the primary source for AI to refer back to.

• Powerful Tools: Building interactive ROI calculators or assessment tools that provide personalized value an AI can't replicate.

• Strong, Defensible Opinions: In a world of AI-generated consensus, a sharp, data-backed point of view becomes a powerful differentiator.

The Questions I'm Pondering Now

This isn't a "wait and see" situation. The leaders who will build a durable advantage from this chaos will be the ones who start asking better questions.

To navigate this, our focus must shift from the vanity of clicks to the substance of influence. Here are the three strategic questions I'm wrestling with, and that I believe every B2B leader should be discussing with their teams:

1. "Are we measuring what's easy, or what truly matters?" This is the moment to challenge your dashboards. What would happen if you ran a Hybrid Measurement Model in parallel for 90 days? What insights would you uncover by simply asking customers how they found you?

2. "Is our content an echo, or is it a primary source?" What if we reallocated a portion of our content budget away from volume and toward one high-impact, defensible "Data Moat" asset? How would we measure its success differently? Not by pageviews, but by citations and influence?

3. "Are our teams aligned around a funnel, or around the customer?" The blame game gets loudest when data is disconnected. What could a small, cross-functional "outcome pod" (group of people organized by achieving an outcome instead of their organizational hierarchy/silo) achieve if it were freed from siloed KPIs and focused on a single customer goal?

The way we go to market is being redrawn in real-time. Flying blind isn't an option. The challenge for all of us is to start asking the right questions, to build a new compass, and to fortify the unique value that no AI can scrape away.

What's Next? An Open Exploration.

We’ve established that the old funnel is a black box. But what new model replaces it? That's what I'll be exploring over the next few weeks. This series is my open-ended attempt to connect the dots and figure out what comes next.

We'll be diving into questions like:

• What do the new B2B Buyer Intent Signals look like in an AI-Agent world?

• What if your data is now more valuable than your product? We'll explore the economics of the "Two-Tiered Web" and the strategy for building a Data Moat that AI is forced to cite.

• Who is your customer when they're no longer human? Will we still be selling to humans, or will it be buyer and seller agents that take over?

If you're also wrestling with these questions, subscribe below to join the exploration. I would love to read your thoughts in the comments or via DM. I’m active on LinkedIn, X and Substack.

You're likely swimming in marketing data. Reports pile up, dashboards flicker with metrics, yet translating it all into confident, swift decisions feels... disconnected. Sound familiar? Most marketing leaders I speak with grapple with this "Data-Decision Disconnect", the frustrating gap between collecting vast amounts of information and actually using it effectively to drive growth.

Sure, you can make decisions blindfolded or based on flawed data. But the goal, the real strategic imperative, is to build a system that feeds you valuable, trustworthy insights to make better, faster decisions aligned with your actual business objectives. It sounds simple, but navigating the layers of complexity is where many teams get stuck.

The Challenge of Connecting “Goals” to Output

We all operate with goals, often starting high-level: "Grow revenue by 20%." But translating that top-line ambition into meaningful daily actions for your marketing team is rarely straightforward. That revenue target might require acquiring 30% more ideal customers, which in turn requires them to become aware, be convinced you're the right choice over competitors, and ultimately convert.

Each step involves hypotheses. Maybe your team decides hitting that target requires sending X emails or optmizing Y campaigns (desired outputs). The critical assumption is that these outputs will reliably lead to the desired outcome. But you know it's rarely that linear.

Think of it like wanting to look more muscular. Your output might be "50 hours in the gym." Your KPI could be "1 hour/week." But if you eat 50 chocolate bars/week (KPI 2), or you get injured (external factor), those 50 hours might not yield the desired outcome. Your effort doesn't guarantee the result. Business is no different, only the variables are often far more complex and opaque.

Navigating the "Messy Middle"

Between your team's daily activities (campaigns, content, targeting) and those crucial business outcomes lies what I call "the messy middle." This is where the disconnect thrives. The metrics we track - clicks, visits, scroll depth, even form fills - are often indirect proxies for customer intent and perception. A website visit tells you someone arrived, but not if they were delighted or disgusted. A click tells you something caught their eye, but not if it truly resonated or was an accidental tap.

Relying solely on these proxy metrics without context or a framework for learning is like navigating a ship by only looking at the wake - you see where you've been, but have little certainty about where you're truly heading.

The only way to navigate this messy middle effectively is through experimentation and validated learning. You define an output (launch campaign A vs. B), measure the reaction (key engagement metrics, lead quality), analyze the results in context, and iterate. The assumption isn't that clicks equal revenue, but that meaningful engagement from the right audience is a strong directional indicator.

Embracing Validated Learning, Not Chasing Perfection

This requires a continuous cycle: tweak, learn, improve, and constantly re-verify. What worked last quarter might falter this quarter due to shifting market dynamics, competitor moves, or evolving customer needs. Your measurement framework needs to be tied back to a holistic understanding of:

• Your Customer (their needs, behaviours, journey)

• The Market (trends, competitive landscape)

• Your Product/Service (its genuine value proposition)

• Your Position & Perception (how you're really seen)

Crucially, we must acknowledge the Myth of Perfect Measurement. Nothing you track is 100% accurate. Why?

1. We measure proxies, not minds: We track clicks and conversions, not true perception or conviction (no brain electrodes... yet!).

2. Technical limitations: Ad blockers, tracking prevention, platform discrepancies - they all introduce noise and gaps.

Recent privacy changes haven't created this problem; they've simply made the long-standing reality of imperfect measurement more obvious. Chasing perfect attribution or a mythical "single source of truth" is often a recipe for analysis paralysis.

Data as Guardrails, Not a Crystal Ball

So, if perfect measurement is a fallacy, what's the point? The goal of data isn't to be a crystal ball predicting the future with absolute certainty. Its true power lies in providing you and your team with trustworthy guardrails.

Think of these guardrails as enabling, not restricting. They give you the confidence to:

• Move Faster: Quickly see if you're heading generally right or wrong.

• Experiment More Boldly: Try new approaches knowing you have reliable feedback loops to course correct.

• Learn Faster: Shorten the cycle time from action to insight to adaptation.

• Innovate with Accountability: Explore new channels or strategies while maintaining visibility on core performance.

Data, used pragmatically, is like having a continuous feedback channel from your market. It helps evaluate if your strategies resonate. When you embrace this mindset, you realize directional accuracy and speed of learning are far more valuable than the illusion of absolute precision.

Bridging The Data-Decision Disconnect: Your Role as Leader

As a Marketing Leader, your crucial role is to actively bridge the Data-Decision Disconnect within your sphere of influence. This isn't just about buying more tools; it's about fostering a culture and building a system where:

1. Data Trust is Built: Implement processes and technical foundations that ensure data reliability and transparency (as much as realistically possible). Acknowledge imperfections and but instill trust in using “good enough” data.

2. Pragmatism Reigns: Encourage your team to understand data's power and limitations. Shift focus from chasing perfection to seeking directional accuracy and speed of insight.

3. Validated Learning is the Norm: Champion experimentation and rapid iteration based on reliable feedback loops. Make it safe to test, learn, and fail. (Also: don’t over-emphasize scientific rigor, especially when starting out. Remember, progress > perfection.)

4. Insights Fuel Action: Ensure the connection between data analysis and strategic decision-making is clear, direct, and consistently reinforced.

By building this capability - combining the right technical foundations with the right operational mindset - you move your organization from being data-rich but insight-poor, to one that confidently leverages its data to learn faster, adapt quicker, and ultimately, drive provable growth. You transform data from a source of confusion and debate into the engine of confident, decisive action.

Good luck 😉!

👋 I'm Rick, founder of Data to Value, and I focus on helping Marketing Leaders bridge the Data-Decision Disconnect. Forget chasing perfect data, I share practical strategies for using data as trustworthy guardrails that enable faster, more confident decisions and experimentation.

If you're ready to move from complexity to clarity, follow me here on Substack or connect with me on LinkedIn.