Neil Patel recently shared something that caught my attention: "There are many ways to run your marketing and measurement.

But here's what marketers prefer these days." That simple line nails the tension most teams feel right now. We have more channels, more tools, and more data than ever, yet confidence in performance reporting often feels lower than it did a few years ago.

In this post, I want to expand on what Neil is pointing at: measurement is not one thing. It is a set of choices. And in 2026, marketers are increasingly choosing approaches that are more resilient to privacy changes, less fragile than click-based attribution, and more useful for real budget decisions.

Key idea: The best measurement setup is the one your team will trust enough to use when money is on the line.

The problem with "many ways" is that they conflict

When Neil says there are many ways, he is underscoring a reality: different measurement methods answer different questions. If you ask one tool, you may get a very different story than another tool, even when both are "right" in their own frame.

Here are the most common questions marketing leaders need answered:

What is driving short-term conversions this week?
What is creating demand that converts later?
What is incremental, meaning it would not have happened otherwise?
How should we allocate next month or next quarter's budget?

Click-based attribution is strongest on the first question and weaker on the others. Brand lift surveys are helpful on the second and third but do not tell you how to rebalance spend tomorrow. Marketing mix modeling helps on budget allocation but is not designed for daily optimization.

So when Neil hints that marketers "prefer" something now, I read that as: they prefer measurement that matches the decision they are trying to make, and they prefer systems that do not collapse when tracking gets noisier.

What marketers prefer these days: measurement you can actually operate

If you have ever had a meeting where three dashboards disagree, you know why preferences are shifting. Modern teams want measurement that is:

Decision-first (tied to a real budget or optimization lever)
Privacy-resilient (works with less user-level tracking)
Fast enough (insights arrive before the window to act closes)
Explainable (stakeholders understand why a number moved)
Calibrated (you can sanity-check it against reality)

If measurement cannot change a decision, it is reporting, not measurement.

Below are the approaches I see more teams leaning into, and how they fit together.

H2: A pragmatic measurement stack (instead of one "source of truth")

The fastest way to improve trust is to stop forcing one methodology to do every job. A better approach is a layered stack where each layer has a clear purpose.

H3: 1) Platform and analytics attribution for directional signals

Channel platforms (Google Ads, Meta, LinkedIn, TikTok) plus web analytics can still be useful, especially for:

Creative and audience iteration
Campaign troubleshooting
Relative performance inside a channel

But teams are increasingly treating these as directional, not definitive. The reason is simple: attribution models vary, conversion windows differ, view-through is inconsistent, and tracking gaps are real.

Practical tip: write down what you will use this layer for. For example, "We use platform ROAS to pick winners within a channel, not to compare across channels."

H3: 2) Incrementality testing for "what caused lift"

This is where preference is shifting most clearly. Marketers want to know what is incremental.

Incrementality can be measured in several ways:

Geo tests (hold out regions)
Conversion lift or brand lift experiments inside platforms
Audience holdouts (hold out a percent of users)
Pre-post designs with careful controls (less ideal, sometimes necessary)

The benefit: it answers the hardest question, "Did marketing cause this, or did it just capture demand that was coming anyway?"

The tradeoff: experiments take planning, clean execution, and patience.

A simple way to start: pick one high-spend channel and run a clean holdout test once per quarter. Even one solid experiment can recalibrate months of reporting.

H3: 3) Marketing mix modeling (MMM) for budget allocation

As signal loss grows, MMM has made a comeback because it does not rely on user-level tracking. It uses time-series data to estimate how spend and external factors relate to outcomes.

MMM is especially valuable when you need to:

Compare channels at a strategic level
Include offline media or macro factors
Understand diminishing returns and saturation

The tradeoff is speed and granularity. MMM is typically weekly or monthly, not hourly. But it is excellent for budget setting, which is where many teams most need confidence.

H3: 4) A calibration layer to keep everyone honest

This layer is the connective tissue between methods. Calibration means you regularly compare and reconcile your measurement systems.

Examples:

Use incrementality tests to adjust your channel-level ROAS expectations
Use MMM to sanity-check attribution-led budget shifts
Use CRM and pipeline data to validate that conversion quality is stable

This is what creates durable trust. Without calibration, teams end up arguing about the model instead of improving marketing.

The hidden reason preferences changed: executives want fewer surprises

Neil's post is short, but it points to a shift in what stakeholders reward. A few years ago, the best marketer was the one with the most detailed dashboard. Now, the best marketer is the one whose forecasts and recommendations hold up.

That is why measurement preferences have moved toward:

Fewer vanity metrics, more business outcomes (profit, LTV, payback)
Clear assumptions (conversion windows, exclusions, refunds)
Repeatable rituals (monthly calibration, quarterly tests)

Stakeholders do not need perfect measurement. They need measurement that is stable enough to steer the business.

A quick framework: match the method to the decision

If you are building or refining your measurement approach, use this mapping:

Daily optimization decisions (bids, budgets, creative): platform reporting + analytics
Weekly channel prioritization: blended reporting + leading indicators (CPL, CAC, payback)
Monthly budget shifts: MMM or structured blended models calibrated with tests
Quarterly strategic bets: incrementality experiments + MMM guidance

The biggest mistake is using the same number for all four. That is how teams end up over-investing in retargeting, under-investing in demand creation, and constantly "discovering" that last quarter's reporting was too optimistic.

A practical checklist to align your team in 30 days

If I were responding directly to Neil with a tactical plan, it would look like this:

Define your North Star outcome (revenue, profit, qualified pipeline) and the time horizon.
Document what each dashboard is allowed to answer (and what it is not).
Establish one blended KPI for leadership (for example, CAC and payback period).
Run one incrementality test on a major spend area.
Create a calibration meeting cadence: monthly review of gaps between attribution, CRM, and experiments.
Decide how you will handle uncertainty: ranges, confidence bands, or scenario planning.

Within a month, you will not have perfect measurement, but you will have something better: alignment. And that is usually what unlocks faster, smarter decision-making.

Closing thought

Neil Patel's line, "There are many ways to run your marketing and measurement," is both obvious and easy to ignore. The real insight is the second sentence: marketers are converging on what they prefer because the environment forced it. Less tracking, more complexity, and higher expectations mean the winners will be the teams that combine directional attribution with rigorous incrementality and strategic modeling.

If you take one thing from this, let it be this: pick measurement methods that your team will actually use to make decisions, then calibrate them regularly so trust compounds over time.

This blog post expands on a viral LinkedIn post by Neil Patel, Co-Founder at Neil Patel Digital. View the original LinkedIn post →