Use more than one MMM approach when the allocation decision is material enough to deserve a serious challenge. The goal is not to average several answers or call the majority correct. It is to understand whether several credible ways of looking at the data tell a coherent story.
This is not a vote
Marketing data is not a clean laboratory. Channels often rise and fall together. Promotions and seasonal demand overlap with media. Customers encounter several messages before buying. Any MMM must make choices about data structure, transformations, carryover, saturation, and what the available history can actually distinguish.
Using several viable specifications is a model-risk review. One approach may find a relationship more clearly than another; another may expose that an apparently precise reading depends on a fragile assumption. If the views broadly agree, the business has stronger grounds to act. If they do not, the honest conclusion may be that the channel is not yet scoreable with confidence.
Compare the decision, not just the output
Start with the evidence each model used
Check whether the models are using the same outcome definitions, spend taxonomy, promotion and seasonality context, and time period. A disagreement caused by different inputs is a reconciliation task—not evidence of deep methodological insight.
Look at ranges and sensitivity
An iROAS range or a wide confidence interval is information. If a channel looks attractive only under one narrow carryover or saturation assumption, the recommendation should say so. Explore plausible ranges using the data and business context; do not treat a convenient setting as a universal DTC fact.
Translate each reading into an operating consequence
Ask what would change if the model is directionally right. Would a lower-funnel channel need a stricter last-click target? Would an upper-funnel channel merit a controlled increase? Would the answer be “do not score yet”? The comparison should help a team decide what to do, not produce a longer technical appendix.
When models disagree, find the next source of evidence
Disagreement is common when the historical signal is weak or highly correlated. It does not automatically mean a channel failed or that one framework is defective. It may mean a relationship visible in a focused experiment cannot be isolated cleanly in the wider historical record—or that the history has not produced enough variation to separate two channels.
For a tested channel, a well-designed lift study is the causal anchor for the conditions it tested. A model such as Google Meridian can use that result as a known constraint while estimating the channels that remain untested. A second approach can then ask whether the broader historical data independently supports the same story. The test does not make every uncertainty vanish; it gives the models firmer ground.
When a material channel produces conflicting, untestable readings and no credible path to learn more, do not convert uncertainty into false precision. Classify it as provisional, preserve the open question, and avoid making a large irreversible decision on a number the team cannot explain.
When one model is enough
Multiple models add cost, interpretation work, and potential confusion. One well-documented approach may be enough when its conclusion is stable across reasonable assumptions, the decision is reversible or low stakes, and its recommendations match the broader evidence the team already has. Some organizations also need one operating number for speed. That can be sensible—as long as the team remains clear about what that number cannot see.
The right question is not “should every brand use three MMMs?” It is “how much challenge does this decision deserve?” Use multiple viable perspectives where the consequences justify it. Use testing where a specific causal question needs an anchor. Then hand the team a decision system it can rerun and question, rather than an opaque answer it has to accept.
FAQs
Do multiple MMMs make the answer more accurate?
Not automatically. They increase the chance that material assumptions, data limitations, and conflicting readings become visible before a team acts on them.
What if two models disagree about a channel?
First align the inputs and inspect uncertainty. Then turn the disagreement into a hypothesis, an interim guardrail, or a test plan. Do not pick a winner simply to restore a single score.
Can a lift study replace MMM?
A lift study is stronger for the specific intervention it tests. MMM remains useful for the broader portfolio and for identifying the next question worth testing.