Multiple regression (MMM analysis) explained for marketers

The dashboard said conversions were up. So did the CRM. But nobody could agree on why.

Paid search was ramped up. So was LinkedIn. A pricing promo also went live halfway through the quarter, and sales had finally adopted the new playbook. Revenue was growing, but attribution pointed in three different directions.

This is the point where most marketers start pulling screenshots and slicing data, hoping to prove something. But proving something is different from understanding it.

That’s where modeling comes in. Not the kind used for forecasting lead volume. A different kind — one that looks at all the moving parts and estimates which inputs influenced results and which just happened to be there.

Technically, this is called multiple linear regression, but we’ll refer to it simply as multiple regression to keep things clear and practical. 

What is multiple regression?

Imagine trying to explain a spike in sales when five different things happened at once. The sales team launched a new outbound cadence. A mid-month discount went live. Paid social spend doubled. A webinar hit record attendance. And a competitor quietly dropped out of the market.

Marketing impact rarely comes from one thing. It’s usually the result of multiple variables working in combination — some more powerful than others, some barely moving the needle.

A multiple regression model is a tool designed to sort that out. It’s a statistical technique that looks at how several variables relate to a single result, like revenue or conversions.

This kind of model estimates how strongly each independent variable contributes to a dependent variable, while holding everything else constant. In marketing terms, it helps answer questions like: If all activity stayed the same except paid search, how much would sales have changed?

Unlike a simple regression model, which looks at the relationship between just one predictor variable and an outcome, multiple regression can handle more realistic scenarios — campaigns where multiple predictors run simultaneously.

This diagram compares simple regression, which measures the impact of one variable, like paid search spend on sales, with multiple regression, which evaluates the combined influence of several inputs — such as search, email volume and discounting — on a single marketing outcome.

While simple regression shows a single cause-and-effect line, multiple regression reflects how real campaigns function, with multiple predictors working together to explain changes in pipeline or revenue against one objective.

Multiple regression doesn’t rely on last-touch logic or tracked journeys. Instead, it builds a model based on historical data points, estimating how changes in one input relate to shifts in the outcome, assuming the rest of the system remains unchanged.

What you then get is a more reliable view of how real-world actions connect to real-world results. Not just attribution, but explanation — something no single platform report can offer.

It’s not even about assigning credit. It’s really about clarity. With multiple regression, you’re better equipped to plan the next campaign, shift spend or explain what’s working so you can take your next step with more confidence.

How multiple regression is used in marketing mix modeling (MMM) 

Marketing mix modeling (MMM) relies on one core engine: the multiple regression model. It’s the framework that helps marketers estimate how different activities — media spend, pricing, promotions and other variables — contribute to business results.

Unlike attribution models, MMM doesn’t track individual users or rely on tags. Instead, it uses historical data to model the relationship between multiple independent variables and a single dependent variable, usually sales or pipeline.

This is especially useful in real-world campaigns where tactics overlap. A webinar might coincide with a paid media push and a pricing promo. The model attempts to separate the effects, though this becomes harder when inputs are highly correlated.

Each input gets a regression coefficient — a number that estimates the impact of a one-unit change in that input, holding all other variables constant. For example, a coefficient of 0.4 on paid search might suggest that a $10,000 increase in spend typically results in a $4,000 lift in revenue.

This kind of model reveals more than surface-level trends:

• Highly correlated inputs, like Meta and Instagram spend, can be flagged.
• Explanatory variables with no measurable effect can be removed.
• The model highlights which predictors drive outcomes, and when.

Good modeling also includes validation. Historical performance metrics like R² (how well the model fits the data), MAPE (mean absolute percentage error) and holdout testing or cross-validation help ensure the model is capturing real patterns, not just noise.

MMM also extends basic regression with marketing-specific dynamics:

• Adstock: the lingering impact of past campaigns.
• Lag effects: when results appear weeks after activity.
• Diminishing returns: when each additional dollar of spend delivers less incremental lift.

The result isn’t just a report. It’s a decision-making tool — a model that supports budget shifts, channel strategy or performance explanations across multiple variables with a consistent, data-backed story.

Understanding how the model works is only part of the picture. The next step is knowing which independent variables you can include and how to choose inputs that reflect the full range of marketing and business activity.

The independent variables that multiple regression can account for

A well-built multiple regression model doesn’t just handle marketing activity. It also handles the context around it. Marketing mix modeling uses this kind of regression model to account not only for paid media, but also for pricing, timing, market shifts and other explanatory variables that may influence results.

Each of these is treated as an independent variable — an input that may (or may not) explain changes in the response variable, like revenue or conversions. The goal is to estimate the effect of each one while holding everything else constant.

It’s a bit like trying to figure out what made a dish taste better when you changed several ingredients at once — more salt, different spice levels, carrots instead of peas. Regression helps isolate which change made the difference.

In MMM, common predictor variables include:

• Media spend by channel: search, social, display, TV, video, OOH
• Promotions: discounts, limited-time offers, bundles
• Sales inputs: rep coverage, enablement pushes, outbound cadences
• Brand activity: PR, awareness campaigns, event presence
• Product-related variables: launches, pricing changes, availability
• Market context: competitor launches, seasonal spikes, economic events

These inputs aren’t treated equally. Each is tested. Some show a strong relationship with results. Others show none at all. That’s the value of using a structured statistical approach like multiple regression — it highlights what matters and filters out what doesn’t.

When two inputs are highly correlated — say, Meta and Instagram spend — the model can catch it using tools like the variance inflation factor (VIF) or cross-validation. That helps keep the outputs stable and trustworthy.

Treating all these as variables may sound abstract, but it’s what allows the model to identify real performance drivers and explain which inputs are pulling weight in actual campaigns.

What to know before trusting the results

Multiple regression is powerful, but like any model, it has limitations. It works best when a few key assumptions hold true.

• When the model assumes the relationship between spend and results is straight and predictable.
• When it expects each channel acts separately without influencing the others.
• If two channels always move together, like social and influencer spend, the model can struggle to tell which one is actually driving results.

Consider a football team that suddenly starts winning after hiring a new coach, buying a star striker and changing tactics. Regression helps estimate which change made the biggest difference. However, if two things always happen together, it’s harder to know what really worked.

The same challenge applies to marketing. When channels overlap or shift together, the model can’t always isolate their true impact. And even when assumptions are met, there are other pitfalls to watch for:

• Overfitting: when a model tries too hard to match the past, it may struggle to predict the future.
• Spurious correlations: just because two metrics move together doesn’t mean one caused the other.
• Unreliable inputs: poor or inconsistent data can skew results, no matter how good the model is.

These aren’t reasons to avoid regression — just reminders that results need context, especially in a fast-moving marketing environment. Used thoughtfully, MLR can highlight what’s working, what’s not and where to shift spend.

How to validate a regression model (without a stats degree)

Not every model is reliable out of the box. Just because a regression fits past data doesn’t mean it will predict future results well. That’s why validation is essential. It helps you separate models that explain the past from models that can actually generalize.

Here are three practical techniques that make validation accessible.

The first is R², or R-squared, which tells you how well the model fits your data. In simple terms, it shows how much of the change in your outcome, like revenue, can be explained by the inputs. A value of 0.8 means the model explains 80 percent of the variation. It’s useful, but not always conclusive. A high R² can look impressive, but it might just mean the model is too closely tailored to the data it was trained on.

The second is MAPE, or Mean Absolute Percentage Error. This measures how far off your predictions are, on average, in percentage terms. If your MAPE is 10 percent, your predictions were typically 10 percent above or below the actual results. It’s a simple way to evaluate accuracy. The lower the MAPE, the more reliable your model.

The third is cross-validation and holdout testing, both of which test how well the model performs on unseen data. Holdout testing means setting aside a portion of your historical data, like the last three months, and seeing how well the model predicts it using only earlier data. 

Cross-validation takes this further by rotating through different parts of the data. The model is trained and tested on multiple combinations, which helps ensure it isn’t just memorizing the past but can perform consistently across different samples.

You don’t need a stats degree to validate a regression model. With just a few key checks, you can be confident your model is built to handle real-world data, not just explain what already happened.

With that in mind, here’s how the model works in action.

A sample multiple regression formula

The structure behind a multiple regression model is surprisingly straightforward. It’s just an equation with a few moving parts, all grounded in campaign and performance data.

Here’s what it looks like in its simplest form:

In this regression model, each slope coefficient represents the estimated impact of a one-unit change in the corresponding variable, holding all other variables constant.

So, if β₂ for email is 0.5, that means increasing weekly sends by one unit (however defined) correlates with a 0.5 unit lift in sales, assuming everything else stays the same.

This is the backbone of every MMM analysis: a regression equation trained on real data points, designed to capture the relationship between multiple predictors and a single response variable.

Think of it like adjusting the levels in a music mix. If you turn up drums, vocals and bass at once, it’s hard to tell which one changed the feel of the track. Regression helps isolate the effect of each “channel,” so you know what’s actually moving the needle.

It’s not about memorizing formulas. In fact, you won’t need to do these calculations by hand. A tool like Funnel can take care of the calculations for you.

It’s about understanding what each part of the model represents and how to use those outputs to support budget shifts, performance deep-dives or strategy planning with credibility.

What this looks like in practice

Imagine a B2C e-commerce brand running paid search, Meta ads and influencer campaigns — all while offering a seasonal discount and launching a new product. Sales go up, but the team wants to understand why.

They use multiple regression to model the relationship between these activities and one dependent variable: weekly revenue.

Each marketing input is added as an independent variable in the model. The regression output shows that paid search has the highest impact on sales, followed by influencer campaigns. Surprisingly, Meta ads and the seasonal discount show no significant effect. The model also detects multicollinearity between Meta and influencer spend, prompting the team to rethink how those channels are activated together.

By using adstock and lag effects, they learn that influencer campaigns continue to drive sales for two weeks after launch. With diminishing returns factored in, the team finds that pushing more budget into paid search has a clear upper limit on impact.

The final output isn’t just a list of coefficients — it’s a data-backed view of what’s working, what’s wasting budget and where to invest next. This element of where to invest next is critical to how marketers should perform MLR.

How marketers should perform multiple regression

Multiple regression isn’t a black box. It’s a tool built to bring structure to messy, overlapping marketing activity. In MMM, it turns raw performance data into clear relationships that can be analyzed, explained and acted on.

Interpreting a multiple regression model starts with understanding its purpose. It’s not designed to assign credit. It’s built to estimate effect: to answer questions like which campaign had influence, which inputs delivered diminishing returns and what should change in the next round of planning.

Regression models work best when variables are well-defined, consistently measured and reflective of the actual strategy in marketing. 

Inputs don’t have to be flawless, but they should reflect how campaigns actually operate. Many are numerical — like spend, impressions or discount rate. Others are categorical — such as campaign type, region or product line. And while they aren’t numbers by default, the model can still handle them. These categories are converted into a format the model can process (like dummy variables), so they can be included alongside numeric inputs without issue.

In rare cases, marketing teams might use logistic regression to predict binary outcomes (e.g. yes/no conversion), but for MMM and budget modeling, the model is standard. It’s preferred because it estimates the scale of impact, not just likelihood.

Understanding the model doesn’t mean decoding every calculation. It means knowing what’s being tested, what the outputs represent and how the design matrix of inputs connects to strategic planning. When individual observations (weekly campaign data, for example) show consistent relationships between inputs and outcomes, those patterns can be used to adjust spend, reallocate resources or set expectations, based on data, not guesswork.

The value of regression isn’t in complexity. It’s in clarity.

Clarity that you can act on

When campaigns overlap and reporting feels unclear, regression gives you structure. It helps move past attribution gaps and platform noise by estimating which inputs influenced performance and by how much.

Understanding how a regression model works means asking better questions, challenging assumptions and communicating results with more confidence. There’s no need to run the math yourself, but knowing what the model is doing makes strategy stronger and conversations easier, especially with finance or revenue leads.

No model is perfect. But one that’s directionally sound and grounded in real data gives real clarity you can act on. Ask your insights team how regression is being used today — and what it’s telling you.