How to do MMM analysis with regression

A while back, I was in a meeting with a marketing team, a sales lead and the CFO. They were all looking at the same results, but from three different dashboards. Meta said paid social drove most of the leads. Google gave credit to search. Salesforce showed a lagged spike but couldn’t tie it back to anything specific. Everyone had data. No one knew what was going on.

That happens more often than you’d think. Teams have all the tools — attribution models, platform metrics, campaign reports — but still can’t agree on what’s working or where to focus next. 

That’s not a data problem. That’s a modeling problem — and one that marketing mix modeling (MMM) is built to solve.

Marketing mix modeling (MMM) analysis at a glance

Marketing mix modeling analysis is a method used to understand how different marketing activities and external factors contribute to business outcomes like sales or leads. It uses historical data — usually spanning a couple of years — to estimate the effect of each input, such as ad spend, seasonality or pricing changes.

Using MMM is a way to step back and look at what’s actually driving results across your full mix — paid, organic, offline and even seasonality or pricing shifts.

What’s great about it is that you don’t need user-level data. That’s key right now, especially with all the privacy and tracking changes. Instead, you use historical data to see how different marketing efforts and external factors move the needle.

The challenge is that most folks still lean on attribution for measurement — first-touch, last-touch, multi-touch — without really defining what they’re looking for or understanding what a touchpoint means for the business. That gets messy fast. Marketing mix modeling gives you a more stable, zoomed-out view. It helps you answer the bigger question: where should the next dollar go?

What does this mean for analysts? 

Marketing mix modeling fills an important gap. It shifts the conversation from backward-looking marketing performance summaries to forward-looking investment planning. This means you're not just reporting results — you're helping shape future strategy. So, where does MMM fit into your strategic planning?

The purpose of MMM analysis as a strategic tool

The goal of MMM isn’t to assign credit. It’s to improve decision-making.

A lot of marketing reporting still centers on individual customer journeys. That often leads to long discussions about which channel or team drove results and less time spent figuring out what to do next.

With MMM, your analysis takes a different approach. Instead of trying to trace every step of the customer journey, it looks at trends over time. This helps answer questions like:

• Which channels are most efficient overall?
• Where should we allocate marketing budget next quarter?
• What’s the right mix between brand and performance?

When done well, MMM gives teams a more complete view of what’s working and where the gaps are. It’s especially helpful for diversified marketing mixes, including upper-funnel and offline campaigns, and in B2B where lead cycles are long and attribution is often patchy.

This method also helps get different teams — marketing, sales and finance — on the same page. Everyone is working from the same model, built around shared goals and a consistent view of performance. That broader view is only possible because of how MMM models are structured. Let’s look at what goes into building one, from data inputs to model selection and validation.

How is an MMM model built?

Building an MMM model starts well before any analysis begins. The first step is always business alignment. We talk to stakeholders — marketing, sales and finance — to understand what questions they need answered.

From there, we define what success looks like and what metrics matter. This is where clear definitions are critical. Terms like "lead," "conversion" and "touchpoint" mean different things to different teams. We make sure those are nailed down early.

Once that’s done, we shift focus to data quality and availability. That includes making sure form submissions are tracked correctly and reviewing CRM and ad platform integrations. 

Next, we gather marketing data — usually two or more years of marketing activity and business outcomes. This includes marketing spend, impressions, conversions, external factors (like seasonality or economic indicators) and operational changes (like pricing or sales team structure).

Then, the marketing effectiveness modeling begins. We run regressions to estimate how much each input affects the output. We test multiple versions to control for noise and check that the results are stable. The goal is to understand the marginal impact of each channel, not just what’s correlated with revenue.

When the model is working, we use it to guide decisions: where to cut spend, where to scale and where further testing is needed. 

To make those decisions with confidence, you need to understand the math behind the model, not because you have to calculate it by hand, but so you can trust how it works and explain it clearly.

The math required for MMM analysis

Marketing mix modeling uses multiple regression to figure out how different variables, like spend, seasonality or pricing, influence a business outcome such as sales or pipeline. Some variables are controllable, some aren’t. Regression offers a flexible way to deal with messy, overlapping data — exactly what most marketing teams face in the real world.

The goal is to isolate the impact of each variable while holding others constant. So if you’re running paid search, LinkedIn ads and a pricing promo at the same time, regression helps untangle their individual effects, even when they overlap.

The basic structure looks like this:

Y = β0 + β1X1 + β2X2 + … + ε

Where:

Y is your outcome (like sales or pipeline)

X1, X2, etc. are your inputs (like media channels, promotions or seasonal effects)

β1, β2, etc. are the coefficients that show how much each input influences the outcome

ε is the error term — the part the model can’t explain

This structure matters because marketing rarely runs in isolation. Campaigns overlap, promotions stack and brand and performance activities happen in parallel. Regression helps cut through the noise and quantify each piece of the puzzle.

Two key terms come up when interpreting regression results:

• P-value tells you whether a variable has a statistically significant impact. In plain terms, a low p-value (usually under 0.05) means you can be confident that the variable is actually influencing the outcome, not just showing a random correlation.

• Multicollinearity happens when two or more inputs move together so closely that the model struggles to tell them apart. For example, if TV and radio always run at the same time, it becomes hard to separate their effects. This can inflate or distort the model’s estimates, so it’s important to check for and manage it.

Regression also works well with aggregated historical data. It doesn’t rely on user-level tracking, which makes it more durable in a privacy-focused world. As long as you have a consistent view of marketing and sales performance over time, you can build a solid model.

You don’t need to run the math yourself. Tools like Funnel can handle the modeling for you. But understanding how regression works helps you ask better questions, validate the outputs and turn insights into action.

At the core of most MMM approaches is standard regression — a reliable method that helps measure the true impact of your marketing investments.

How standard regression fuels MMM analysis

It might look overwhelming, but you don’t need a statistics degree to use regression effectively. You won’t be doing the calculations by hand, but you do need to understand how the model works so you can interpret results and explain them clearly to stakeholders.

Marketing mix modeling uses multiple linear regression to figure out how much different inputs, like media spend, discounts or distribution, contribute to a business outcome like your pipeline or sales. It answers the question: If we increase X, what happens to Y?

Let’s say you’re modeling the impact of TV, paid search and product discounts on weekly revenue. The regression looks at historical patterns and gives each variable a coefficient — a number that says how much impact that input had.

For example:

• A TV spend coefficient of 0.4 might mean every additional $1,000 generates $400 in sales.
• A p-value below 0.05 tells you this effect is statistically solid — it’s not just noise.
• An R-squared of 0.85 means the model explains 85% of the variation in your outcome. 

But MMM isn’t just about simple, straight-line relationships. We also account for:

• Diminishing returns — your first $10K in spend might perform great. The next $10K? Not so much. We model that using S-curves or log transforms.

• Lag effects — some marketing tactics take time to show up. For example, a campaign in April might influence sales in May.

• Adstock — think of it like marketing memory. TV, out-of-home and even certain digital formats have effects that carry over beyond the day they run.

You also need to watch for multicollinearity — when two inputs are highly correlated, like Facebook and Instagram spend. That can confuse the model, so we look at correlations very closely.

In practice, you’re building a model that’s useful, not flawless. It should give you a reliable sense of which levers move the business — and by how much — so you can invest with more confidence.

While this is just an introduction to the more basic modelling we do, it’s only one piece of the picture. 

How Funnel approaches MMM analysis

Funnel’s approach to marketing measurement goes well beyond standard regression. While linear models are useful for identifying broad trends, most marketing data is too complex, dynamic or fragmented to rely on a single method. That’s why Funnel uses a triangulated approach to measurement, combining media mix modeling, multi-touch attribution and incrementality testing to produce more accurate and actionable insights.

This combination is a core differentiator. By blending MMM with attribution and experimentation, Funnel addresses common limitations like data sparsity, lack of granularity and delayed feedback. Rather than choosing one model and accepting its blind spots, we use each method to cross-check and strengthen the others, resulting in a clearer picture of what’s working.

Our modeling framework is built for real-world complexity. In addition to standard regression, Funnel applies hierarchical Bayesian models to capture regional variation and brand effects over time, LSTM networks for user-level attribution and AI to support predictive and adaptive media optimization. These tools help us identify nonlinear patterns, channel interactions and changes in performance as they happen.

What sets Funnel apart is not just how we model data but how we operationalize it. Our models run daily, update continuously and integrate directly into activation platforms. This makes it possible to use model outputs in real time to adjust budgets, optimize bids and inform cross-channel decisions.

Funnel is also built for usability. While open-source tools like Robyn require deep technical skill, Funnel delivers advanced analytics through a platform designed for marketing and analytics teams. Whether you're managing campaigns across multiple regions or balancing brand and performance investment, the platform adapts to your complexity without slowing you down.

Every model is tailored to the business question, data availability and decision-making needs. Whether it’s a quick-turn model for short-term tradeoffs or a deeply nested model for long-range planning, our work is grounded in transparency and focused on outcomes. Funnel gives you more than statistical confidence. It gives you the clarity and momentum to act.

Expert tips and tricks for MMM analysis

Here are some lessons we’ve learned from running MMM projects across different industries and growth stages — from early-stage B2B to mature enterprise teams:

Start with business questions

Don’t start with the data — start with what the business needs to know. Is the question about reallocating budget, justifying spend or forecasting next quarter’s pipeline? The model should answer a question, not just generate a slide.

Tailor reports to the audience

One model can serve multiple teams, but how you present it should differ. CMOs care about overall ROI and long-term marketing strategies. Analysts want the assumptions, limitations and math. Sales leaders want to know what’s driving leads. Filter accordingly.

Avoid over-reporting

It’s easy to get stuck in the weeds with attribution waterfalls, channel-by-channel breakdowns and complex visuals. But more charts don’t mean better decisions. Show fewer metrics, with more clarity.

Don’t rely on attribution alone

Attribution is useful but often incomplete, especially in B2B, where deals span quarters, involve multiple touches and rely on both marketing activities and sales data. Using MMM gives you a broader, outcome-based view because you’re working with aggregated data rather than user-level tracking. It doesn’t replace attribution, but it helps validate or challenge it.

When paired with incrementality testing, you get triangulation — a multi-method approach that gives you more confidence in what’s really driving results.

Keep the model updated

Things change — seasonality, creative, the media mix. A model that worked six months ago might not reflect what’s happening today. We recommend a quarterly refresh, especially for fast-moving teams, to ensure optimum marketing effectiveness.

Use AI for speed, not conclusions

AI can help with things like clustering personas, flagging anomalies or summarizing qualitative insights. But don’t outsource judgment to artificial intelligence. The model still needs human context to be meaningful.

Test where possible

Regression models show patterns, but experiments prove causality. Use geo or incrementality testing to confirm what the model suggests, especially before making big shifts in spend.

Don’t wait for perfect data

You’ll never have 100% clean, complete marketing or sales data. Aim for “accurate enough to act” and refine as you go. Most of the value comes from directional clarity, not mathematical precision.

Done right, MMM becomes a planning tool, not just a reporting layer. It helps teams shift from explaining what happened to confidently deciding what to do next. That’s the real value of MMM — not just understanding the past, but generating direct insights that shape what you do next and drive real business impact.

MMM means direct insights that actually change your business reality

MMM analysis shifts the role of marketing analytics from reporting to decision support. Instead of chasing attribution or proving impact, you’re identifying what actually contributes to revenue and where to invest next. 

With clean data, clear goals and a solid multiple linear regression model, you can replace guesswork with evidence. 

For analysts, it’s a chance to drive strategy, not just track results. As privacy regulations evolve and platforms change, MMM offers a durable way to measure and plan across channels. It’s a practical tool that helps teams stay focused, aligned and ready to act.