How marketing mix modelling helps CMOs make smarter decisions

If you are a CMO in 2026, you are navigating a measurement minefield. Attribution still helps teams steer week to week — but on its own it can’t answer the CFO question of what really drove incremental growth.

Privacy regulations are tightening and every major platform now grades its own homework. Your marketing dashboards look healthy. Your CFO’s numbers do not always agree. Somewhere between clicks, conversions and quarterly revenue, the truth about ROI is getting lost.

This disconnect is not a reporting problem. It is a decision problem. And it is exactly why marketing mix modelling (MMM) has re-emerged as a board-level capability. MMM isn’t new to leadership teams, but it is now far more powerful, faster and more accessible than it used to be.

The measurement ladder every CMO is climbing

There is no single silver bullet for marketing measurement. What works in the short term rarely works for long-term planning.

That is why we think in terms of a measurement ladder.

Rather than replacing each other, they work together to solve different decision problems across various time horizons. Each rung of the ladder builds confidence. Attribution guides action, incrementality proves impact, and MMM unifies everything into a single decision framework.

The reinvention of marketing mix modelling

For years, MMM had a reputation problem. It was slow, expensive, hard to explain and even harder to refresh.

Modern MMM looks nothing like that. And it doesn’t operate in isolation. The strongest MMM programmes actively ingest learnings from attribution and incrementality testing — using them to set priors, validate channels and calibrate model outputs.

Today, it blends classical econometrics with machine learning to move from correlation to causation. It captures how marketing and context influence outcomes over time by modelling:

• Memory effects through adstock
• Delayed response through lag structures
• Diminishing returns through saturation curves
  

The result is not just a historical ROI report. It is a living system that produces response curves, elasticities and predictive scenarios for every channel.

The groundwork that separates insight from illusion

Modern MMM tools are powerful. But they only perform when the foundations are right.

Before a single model is run, the work that matters most happens outside of code. This is the phase where business context is translated into modelling logic and where causality is shaped long before any algorithm is applied.

To translate business context into modelling logic, organisations must focus on four priorities.

1. Start with the business, not the model

Discovery defines direction. Pre-modelling begins with business-led discovery sessions involving marketing, finance and trading teams. The purpose is not to discuss techniques, but to define what the business actually needs to know.

Typical outputs from this stage include:

• The decisions the model must inform – budgeting, promo timing, channel mix, scenario planning
• The core KPIs to optimise – sales, profit, bookings, LTV
• Known demand drivers – promotions, seasonality, competitor activity, macro-economic factors
• A clear view of who owns which data and where it lives

This ensures the model is built to answer commercial questions rather than abstract analytical ones.

2. Design inputs that mirror reality

Model what you can control, what you must control for, and what you can observe.

A well-structured MMM starts with the right variables. By separating controllable inputs from contextual factors and brand indicators, we ensure the model reflects both marketing effort and real-world conditions.

This is typically done through a three-tier variable framework:

• Tier A – Controllable
  Paid media, promotions, pricing, email and CRM activity
  
• Tier B – Contextual
  Seasonality, competitor behaviour, macro-economics, weather, events
  
• Tier C – Brand and funnel signals
  Brand awareness, search volume and site traffic, used carefully

Clarity in variables builds confidence in results:

• Clarifies what drives vs. what explains performance
• Prevents confounding and over-attribution
• Enables cleaner, more interpretable coefficients
• Guides how new data sources can be added later

3. The invisible work behind reliable ROI

Open-source MMM frameworks, such as Meta’s Robyn and Google’s Meridian, accelerate model building, but the real accuracy comes from the preparation that occurs before a single run. The “hidden 70%” of effort (data diagnostics, variable selection, and business review) is what turns automation into reliable causality.

• Diagnose seasonality, lags & outliers before modelling
• Align with business teams to verify metrics and context
• Use findings to shape adstock, saturation & control variables
• Run Robyn/Meridian on clean, contextualised data → credible RO

This discovery, diagnostics and setup phase is what turns automation into reliable causality.

4. From anomalies to measurable insight

Marketing data always tells a story — the challenge is recognising when the plot doesn’t fit expectations. By pairing diagnostics with business context, hidden drivers behind peaks and troughs can be uncovered that raw data alone cannot explain:

• Recurring weekend dips traced to call-centre closures
• Mid-month spikes linked to brochure email sends
• Sustained Q2 uplift caused by an extended early-bird offer

Each insight becomes a new contextual or control variable, improving the model’s explanatory power and ensuring it reflects how the business really operates.

How modern MMM actually works

Modern MMM blends classical econometric structure with machine learning to isolate true cause-and-effect over time. At the heart of the model is the MMM engine, which processes three types of information:

• Inputs – marketing spend, conversions and contextual factors
• Processing logic – adstock, lag and saturation dynamics
• Outputs – ROI, elasticities and response curves by channel

This structure allows the model to reflect how marketing really works in the real world, not how platforms report it. Three behavioural dynamics are critical:

1. Carry-over – adstock

Marketing has memory. The effect of activity continues beyond the week it runs, which MMM captures through adstock functions.

2. Delayed response – lag

Some channels influence outcomes days or weeks later. Lag structures allow the model to represent this delayed impact.

3. Diminishing returns – Hill saturation

Incremental spend does not scale linearly. MMM models where returns start to flatten, turning spend into response curves rather than simple attribution.

By blending econometric rigour with automation, modern MMM achieves speed without sacrificing interpretability.

From understanding to optimisation: causal, predictive, prescriptive

MMM isn’t just about explaining the past. It creates a continuous loop – understanding causality, forecasting outcomes, and recommending optimal investment decisions. 

This loop is supported by validation and calibration:

• Statistical validation using hold-outs, Mean Absolute Percentage Error (MAPE) and confidence intervals
• Cross-checking outputs against business expectations and historical campaigns
• Calibration using incrementality tests or known uplifts
  Quarterly refresh cycles to refine priors and parameters

This is how MMM evolves from analytical curiosity into causal, predictive and prescriptive intelligence that leadership can rely on.

The end of marketing measurement as we knew it

We are moving into an era where platform metrics are no longer enough. Leaders need clear financial evidence to justify investment and guide growth.

Attribution will always play a role. Incrementality helps validate specific causal questions. MMM provides the strategic layer that brings these perspectives together into a single, consistent view of performance. This is what stops measurement from becoming fragmented – or worse, an expensive Excel exercise that everyone reviews but no one uses.

At Braidr, we turn messy signals into confident choices. By unifying attribution, incrementality and MMM, we’ve helped leading brands optimise millions in marketing spend and invest with confidence. If you’re ready for that level of clarity, get in touch.