A weather forecast that’s right 60% of the time is better than flipping a coin, but you probably wouldn’t trust it before planning an outdoor event. When you’re relying on data to make budget decisions, you want some sense of how much you can actually trust the model giving you those numbers. R-squared is one of the most straightforward ways to get that read.
For marketers using any kind of statistical model—including marketing mix models—understanding R-squared helps you ask better questions about the tools you’re using and feel more confident in the recommendations they produce. The difference between a model that explains your data well and one that’s mostly guessing can mean the difference between smart budget moves and expensive mistakes.
Key takeaways
- R-squared is a statistical measure that tells you how well a model’s predictions match what actually happened in your data.
- It’s expressed as a value between 0 and 1 (or 0% to 100%), where higher values generally indicate a better-fitting model.
- In marketing, R-squared helps you assess whether a regression model is actually capturing the relationships between your spend and your outcomes.
- A high R-squared doesn’t automatically mean a model is useful — context, the number of variables, and what the model is trying to do all matter.
- Adjusted R-squared is a more reliable version of the metric when a model uses multiple independent variables, because it accounts for the risk of overfitting.
- R-squared is one of several ways to evaluate a model, not a definitive pass/fail grade on its own.
- For marketing mix modeling specifically, model fit is just one piece of the puzzle; a well-fit model still needs to reflect how marketing actually works to be worth trusting.
What R-squared actually measures
At its core, R-squared—also called the coefficient of determination—measures how much of the variation in your outcome variable (say, revenue) is explained by the independent variables in your model (like ad spend across channels). It’s a way of asking: how much of what happened can this model account for?
The value runs from 0 to 1. An R-squared of 0 means the model explains none of the observed variation in your data. An R-squared of 1 means it explains all of it perfectly. In practice, you’ll almost always land somewhere in between, and what counts as a “good” R-squared value depends heavily on the context.
In most marketing applications, an R-squared above 0.7 or 0.8 suggests the model is capturing a meaningful portion of what’s driving outcomes. That said, a lower R-squared isn’t automatically a red flag, and a high one doesn’t guarantee the model is useful. We’ll come back to why that distinction matters.
How R-squared is calculated
You don’t need to run the math yourself to understand what R-squared is telling you, but knowing the basic logic helps. The calculation compares two things: the total variation in your data versus the unexplained variation left over after the model does its work.
Total variation is essentially how spread out your actual data points are around their average value. Unexplained variation—sometimes called the residual sum of squares—is the gap between what your model predicted and what actually happened. R-squared is the proportion of total variation that the model successfully accounts for. The higher that proportion, the better the model fits the observed data.
In simple linear regression (one independent variable predicting one outcome), this is fairly intuitive. When you add more variables to the mix, the calculation stays the same in principle, but interpretation gets more complicated, which is where adjusted R-squared becomes important.
R-squared vs. adjusted R-squared
Every time you add another variable to a regression model, R-squared goes up, even if that variable has no real relationship to your outcome. The model is technically explaining more variation, but it may just be overfitting to noise in your data rather than capturing a real pattern.
Adjusted R-squared corrects for this. It penalizes the model for adding variables that don’t actually improve predictive power, giving you a more honest read on fit. When you’re working with multiple independent variables—which is the norm in any realistic marketing analysis—adjusted R-squared is the more reliable number to pay attention to.
A meaningful gap between regular R-squared and adjusted R-squared is a signal worth investigating. It often means the model has more variables than it needs, or that some of them aren’t pulling their weight.
What a low R-squared value actually means
A low R-squared value means a significant portion of the variation in your outcome isn’t being explained by the model. In some contexts, that’s expected and fine. Marketing outcomes are influenced by a long list of factors that are genuinely difficult to capture in any model: economic conditions, competitor activity, seasonality, word of mouth, and more. No model perfectly accounts for all of that.
Where a low R-squared becomes a real problem is when you’re trying to use a model to make decisions and you don’t know it’s missing major drivers of your results. If a marketing mix model has a low R-squared, the attribution numbers it produces may not reflect what’s actually driving your revenue. Acting on that kind of output can lead to misallocated budget and missed growth opportunities.
That’s one of the reasons model evaluation shouldn’t stop at R-squared alone. You want to look at whether the model’s outputs make sense directionally, whether they hold up over time, and whether the underlying assumptions reflect how marketing actually works.
Why model fit matters for marketing mix modeling
Marketing mix models are built to answer high-stakes questions: which channels are actually driving revenue, where to put more budget, and what’s likely to happen if you make a change. The accuracy of those answers depends on how well the model fits your data, but also on whether it’s built on realistic assumptions.
A model can have a strong R-squared and still produce misleading attribution if it’s using assumptions that don’t reflect modern marketing dynamics. For example, many traditional MMMs assume that marketing channels operate independently of each other. That assumption makes the math simpler, but it ignores the very real spillover effects that happen when a paid social campaign drives organic search or branded traffic. A model missing those relationships might fit historical data reasonably well while completely misrepresenting what’s actually driving results.
This is part of why R-squared is best understood as one input into a broader evaluation, not the final word on whether a model should be trusted.
What good model fit looks like in practice
When evaluating any regression model used for marketing analysis, a few things are worth checking alongside R-squared. The model should be able to predict outcomes on new data with reasonable accuracy, not just the historical data it was trained on. The contribution estimates for individual channels should be directionally plausible and consistent when the model is rerun. And the model should be sensitive to real changes in your marketing, like a budget shift or a new campaign launch, rather than treating everything as noise.
Strong R-squared with none of those properties is a warning sign. Good predictive power on new data with a moderate R-squared is often more useful. The goal is a model that helps you understand your marketing well enough to make better decisions, not one that simply looks impressive on paper.
How Prescient approaches model quality
At Prescient, we built our marketing mix model from the ground up rather than adapting open-source frameworks. One of the core reasons for that is that standard MMM approaches tend to inherit assumptions—like channel independence and fixed saturation curves—that simply don’t hold in the way modern DTC brands actually advertise.
Our model treats marketing as a dynamic, interconnected system rather than a collection of isolated channels. This approach produces attribution that’s more structurally accurate, which means the R-squared and other fit metrics reflect a model that’s actually capturing how your marketing works, not one that’s curve-fitting around flawed assumptions. We also update our models daily, so fit is continuously evaluated against fresh data rather than a static historical window.
If you’re curious what that looks like in the platform, we’re happy to walk you through it.
