What is Marketing Mix Modelling? The Complete 2026 Guide to MMM

As a measurement expert with 15+ years of experience, I have been helping brands decode the true payback of their marketing. I have seen the industry shift from tracking to incrementality.

Now in 2026, with Meta not talking to Google and the reducing usefulness of tools like GA4, approaches like Marketing Mix Modelling have never been more vital.

So, what is MMM?

Marketing Mix Modelling (MMM) is a statistical technique used by businesses to quantify the incremental impact of media channels, price, and external factors on sales.

By using historical data, MMM builds a mathematical relationship between independent variables, such as media spend, pricing, and economic factors, and dependent variables like sales, new customers, or web visits.

Crucially, whilst digital attribution uses a tracking approach to measure a relationship between an impression and a sale. MMM uses a statistical approach to measure the same relationships. This gives brands two crucial advantages:

1. You can measure the impact of factors you aren’t tracking
2. The data quality doesn’t reduce with “poorer” tracking data

What questions does MMM answer?

The primary goal of MMM is to isolate the impact of every factor driving business performance. It separates marketing influences from external drivers like seasonality, economic conditions, and competitor activity.

This allows brands to answer a series of tactical & strategic questions, including:

• What is my true ROI? Identify which channels or campaigns deliver the best incremental return on investment.
• How much more can we spend on my Meta activity? (or other channels) Forecast the impact of scaling spend and identify the "unprofitable zone" where the next £1 spent returns less than £1 in revenue

• How does CAC/ CPA change during a promotion? Understand the contribution of different marketing tactics and how they interact with wider environmental factors, including the economy, seasonality, and promotions.
• How do I optimise my budget? Use the Linea Scenario Tool to:
  • Allocate the next pound (£) of budget to the most efficient channel for future growth
  • Understand how to hit your Q2 growth targets
  • Where should I spend an extra £1m budget in Q4
  • Allocate a fixed budget over the year
• When will I see the impact of marketing? Often, the focus of Brand or Upper funnel activity when activity that happens today, which may drive sales in the future. An MMM captures the Adstock impact to reflect how sales will occur over time. See more here

Why Marketing Mix Modelling is Essential in 2026

In 2026, traditional digital tracking has become increasingly difficult due to privacy regulations and the decline of cookies. Brands, more than ever, are struggling to answer their finance teams' questions with off-the-shelf solutions. MMM offers a "privacy-safe" alternative because it relies on aggregated data rather than individual user tracking.

Furthermore, as platforms like Google and Meta dominate ad spend, brands need independent measurement to verify the true incremental impact of these channels.

Additionally, two key trends have changed within the measurement landscape:

1. More cost-efficient and faster MMM providers like Linea have come into the space. Traditionally, MMM was seen as slow and expensive, with results not available for 3 or 4 months after the quarter. Using Linea brands can get results with full transparency in a matter of days. This allows for a change in the type of questions your MMM can answer. Moving from being a post campaign reporting tool to allowing brands to run in campaign changes or scale/ de-scale channel spend
2. Open source MMM, like Meridian from Google. These have allowed non-industry professionals to test the power of MMM within a free, open-source environment. In addition, both Google & Meta have spent time promoting how MMM is the gold standard for marketing measurement. Their significant weight in the UK ad industry, accounting for over 60% of media spend, has further entrenched MMM in importance.

How does it differ from other measurement approaches?

The industry has largely narrowed down to 3 main marketing measurement techniques:

1. Last-click attribution: A tracking approach that assigns 100% of the credit for a conversion to the last touchpoint before purchasing, e.g. GA4
2. Geo Testing (Experiments): A measurement method that compares business results between two similar geographic regions, one where marketing is active (test) and one where it is held back (control), to determine the incremental uplift of an advertising channel.
3. Marketing Mix Modelling (MMM): as per this article

See how they compare below, for a detailed overview of when to use each approach here:

	Last-click attribution	Geo Testing (Experiments)	Marketing Mix Modelling (MMM)
Tracked/ Statistical	Tracked	Tracked	Statistical
Incremental	No	Yes	Yes
Short-term impact?	Yes	Yes	Yes
Long-term impact?	No	No	Yes
Pro’s	Can run detailed channel tests	Prove the incrementality of one channel (e.g. TV or YouTube, etc.) Run multiple tests to dive deeper “in channel”	Measure long(er) term impacts Incremental & “like-for-like” cross-channel effectiveness Set/ Allocate cross-channel budgets
Con’s	Not incremental Tracking issues causes bias in results	Difficult to control for all factors for accuracy Short-term focused	Often no detailed in channel results Is statistical, not tracked, which requires other ways to

Definitions: Tracked vs. Statistical:

• A tracked approach uses cookies or “logged in” customers to see how they move from an advert through to a conversion (purchase/ sign up)
  • Statistical: uses a regression approach to measure how changes in a variable, e.g. media, drive changes in purchase

What are the issues with MMM?

Historically, MMM has faced three major hurdles:

1. Speed: Traditional models often took months to build, making the results outdated by the time they were delivered.
2. Statistical transparency: Being a statistical approach, it relies not on the “simple” tracked relationship, as can be seen in an attribution or testing approach. As such, it requires more confidence to take action.
3. Trust & transparency Gap: The availability of attribution approaches readily and freely available provides an expectation as to the performance of media channels. An MMM model, which shows that results are different from pre-held beliefs, requires more understanding as to “why the results are different”
4. "Free" Tool Pitfalls: More modern open-source tools like Robyn or Meridian are great starting points. When put in the wrong hands, teams can be left to believe that MMM doesn’t answer their question.

How do you implement an MMM?

Setting up a robust Marketing Mix Modelling requires 4 steps:

1. Data Connectivity: Automating the flow of all data.
2. Model Build: At Linea, we use a hierarchical Bayesian model. This allows us to:
   1. Get detailed in-channel results
   2. Measure how marketing effectiveness changes alongside wider factors such as the economy or seasonality
   3. Capturing Memory Effects allows us to measure the medium and long-term impacts of marketing
3. Platform reports: Linea platform gives full access to:
   1. What were the key drivers of sales
   2. How does media contribute to driving sales by channel, campaign, placement & creative
   3. How does performance compare with previous periods or wider information
   4. How confident are we in channel results
4. Take action: We provide tools to help you drive implementation
   1. Can we scale channels this week, month or quarter
   2. How much should we spend this week, month or quarter
   3. What media channels will optimise our budget
   4. And much more

How do you get value out of MMM?

Value is derived when you hit the sweet spot between:

✅Understood: the difference between measurement approaches & when to take action from MMM
✅Trusted: by all teams across the business
✅Provides the right tools to help you take action: Tools tailored to answer a specific business question

To ensure teams take action at Linea, we focus the initial MMM delivery to be singularly focused on building trust and answering one business question. Too often, teams try to use the initial update to try and answer too many questions. We focus on one area and ensure it is understood and implemented.

Only once that question has been answered, value has been proven, do we scale to wider business questions. This sets MMM up as a tool focused on driving growth.

Why use Linea?

At Linea Analytics, we have redefined the standard for always-on MMM. We are the best in the industry because we deliver:

• Results in days, not months: Our proprietary modelling set up allows us to connect to data, build models in real time and deliver impact with the Linea Platform. This allows us to scale the questions we can answer with MMM, building MMM updates in days when competitors take 3-4 months.
• Greater Transparency: We bridge the gap between complex data science and marketing strategy, building tools that stakeholders actually trust. Additionally, we build both in the Linea environment and in your Brands environment to allow long-term ownership
• Actionable Tools & Team: We don't just provide reports; we provide the platforms to run scenarios, scale channels & optimise budgets in real-time.

Want to find out more about Linea? Book a demo to see how you can answer your key business questions.