Running an MMM model is the easy part. Most teams get their channel ROAS numbers, feel good about the output, and then either accept everything at face value or ignore it entirely because the numbers contradict what they thought they knew. Neither is the right approach. (If you're still getting up to speed on what MMM is and how it works, start with our complete guide to marketing mix modeling.)
This post covers what MMM output actually tells you, what it cannot tell you, and how to turn model results into a budget decision you can defend.
What ROAS by channel is actually measuring
MMM ROAS is not the same as the ROAS figure you see in your ad platform dashboards. Platform ROAS measures what the platform's attribution model credits itself for. MMM ROAS is the model's estimate of incremental contribution — how much of your outcome metric is statistically associated with spend in that channel, after accounting for other channels, seasonality, and baseline sales.
The two numbers will often disagree. That disagreement is informative, not a sign that one is wrong.
Start with the fit quality
Before trusting any channel-level output, look at the actual vs. predicted chart. The model should track your sales trend reasonably well, including seasonal peaks. If the predicted line is systematically off during key periods — a holiday spike it missed, a sustained divergence over several months — then the channel attribution is unreliable regardless of what the ROAS numbers say.
A good fit does not guarantee correct attribution. A bad fit almost guarantees incorrect attribution. Check this first.
Question the channels that surprise you
If a channel comes back with very high ROAS that contradicts your intuition, do not dismiss it and do not accept it uncritically. Ask why. Two common reasons a channel looks better than expected in an MMM model:
First, spend correlation. If two channels tend to increase and decrease together across your history, the model may be attributing contribution from one to the other. This is a real limitation of observational MMM and there is no clean fix other than acknowledging the uncertainty.
Second, the channel is genuinely undervalued in your current attribution setup. Last-click and platform attribution both have well-documented biases toward lower-funnel, click-based channels. MMM often surfaces stronger contribution from brand, video, and upper-funnel spend than your dashboards show. That is frequently correct, not an error.
Question the channels that look worse than expected
A channel showing low MMM ROAS is worth investigating before cutting budget. Ask whether spend in that channel was relatively flat across your measurement period. MMM models need variation to estimate contribution. A channel that ran at roughly the same spend every week will often show weak attribution simply because there was nothing for the model to learn from. That is a data problem, not a channel performance problem.
Do not reallocate everything at once
MMM output is a directional signal, not a precision instrument. The appropriate response to a model that suggests Google is outperforming Meta is not to immediately shift 40% of your Meta budget to Google. It is to run a smaller test — reduce Meta spend by 10 to 15% for 6 to 8 weeks, hold Google flat, and observe what happens to total revenue. If the model is right, you will see improvement. If it is wrong, the downside is contained.
This approach also generates better data for your next model run, because you are introducing spend variation that was previously absent.
How to present MMM results to a CFO or CMO
The instinct is to lead with the channel ROAS table. Resist this. The numbers invite debate about methodology before the audience has any context for interpreting them.
A better structure: start with fit quality and establish that the model tracks your business performance well. Then show the channel contribution breakdown as a share of total modeled revenue, not as a ROAS figure. Finally, present the reallocation recommendation as a test proposal with a defined measurement period, not as a conclusion. This gives stakeholders something to approve rather than something to argue with.
What MMM cannot tell you
MMM is an observational model. It identifies statistical associations between spend and outcomes in your historical data. It cannot distinguish correlation from causation. It cannot tell you what would happen in a scenario that looks very different from your historical spend patterns. It is not a substitute for incrementality testing or geo holdout experiments.
Use MMM to identify where to run experiments, not to replace them.