Ask what makes the marketing-mix estimate believable

State the modeled decision and outcome

Define the geography, time period, outcome and media granularity. Ask whether the estimate concerns total channel contribution, an additional unit of spend or a specific spending change. These are different questions and may support different allocation decisions.

Inventory plausible common causes of both spend and sales, such as expected demand, distribution, prices and promotions. Ask how the model addresses them and what remains unmeasured. A channel that scales up before predictable demand peaks can look powerful even when part of the movement would have happened anyway.

Inspect data variation and model assumptions

Review whether channels moved independently enough to distinguish their contributions. Highly synchronized campaigns can make individual estimates difficult to identify. Ask how carryover, saturation, seasonality and baseline demand are represented, and which assumptions materially drive the recommendation.

Have the analyst show sensitivity to reasonable alternative specifications. A stable total fit with wildly shifting channel allocation deserves caution. Do not demand one preferred result from the model. The review should discover which conclusions survive uncertainty and which depend on a narrow modeling choice.

Check calibration against the exact question

Google Meridian describes using experiment results to inform priors while considering mismatches in period, population, media scope and estimand. A campaign-level experiment or a marginal spending test cannot simply be pasted into a differently defined channel effect.

For each calibration input, request its source, uncertainty, design quality and relevance. Separate strong experimental evidence from professional expectations. Both may inform a model, but their roles should be visible. Preserve uncertainty rather than treating a past estimate as an exact constant that the model must reproduce.

Require diagnostics and bounded decisions

Meridian’s diagnostics distinguish convergence and predictive fit, including train/test checks, and caution that predictive accuracy is not the main causal objective. Ask for those checks plus sensitivity and plausibility review. No single diagnostic certifies causal correctness.

Translate the result into a bounded next decision. Prefer changes supported across reasonable assumptions, document uncertainty and identify what experiment could most improve the next model update. Avoid extrapolating far beyond observed spending without explicit caveats. Keep the model version and evidence record so later results can be compared honestly.

Make the reasoning concrete.

A hypothetical company runs paid social and online video together during every promotion. Two model specifications predict sales similarly but assign very different contributions to the two channels. A prior experiment covered only one short campaign.

Do not present a precise channel ranking as established. Document the instability, use cautious decisions and identify the evidence that would resolve the practical uncertainty.

Take the questions into your next brief.

Use these prompts in your own notes. Nothing is submitted from this page.

1. Estimand

   Which spending change and outcome does the model estimate?

2. Confounding

   What drives both spending and demand?

3. Variation

   Can the data distinguish channels that moved together?

4. Calibration

   How closely does each experiment match the modeled question?

5. Robustness

   Which recommendations survive alternative assumptions?

Watch for the wrong conclusion.

• Treating high predictive fit as proof of causal attribution.
• Using a point estimate from a mismatched experiment as an exact prior.
• Hiding unstable channel allocations.
• Extrapolating confidently beyond observed spending ranges.

Sources & scope

Frameworks, worksheets and hypothetical examples are original editorial synthesis. Linked primary sources support only the stated concepts. Source checking is not an independent audit of effectiveness. No named human author or reviewer is claimed.

• Google Meridian: Calibrate treatment priors →Primary model documentation · Supports: Explains experiment-informed priors and mismatches in timing, population, media scope and estimand. Calibration adds assumptions and does not turn a model into an experiment.
• Google Meridian: Run model diagnostics →Primary model documentation · Supports: Separates convergence and predictive fit checks and documents train/test diagnostics. Predictive fit does not establish correct causal channel effects.