From Platform Metrics to Marketing Science: When to Use Experiments, Incrementality and MMM
A measurement maturity map that gives each method a job instead of forcing one dashboard to answer every marketing question.
One method should not answer every question
Platform attribution, controlled experiments and marketing mix models observe the world differently. The common mistake is turning the most convenient tool into the “source of truth” for every decision. Measurement maturity means assigning each method a job.
Platform metrics: operational speed
Use platform data for delivery, pacing, creative diagnostics, funnel monitoring and near-term optimization. It is fast and granular. Its weakness is causal interpretation, especially when channels compete for credit or target high-intent users.
Experiments: causal precision on bounded questions
Randomized holdouts, lift studies and geo experiments are powerful when the business can define a specific treatment, control and outcome. They answer high-value questions such as whether a campaign, feature or spend change caused lift. Their weakness is cost, operational complexity and limited scope.
MMM: broader budget questions over time
Marketing mix modeling uses aggregate historical variation to estimate channel effects and response curves while accounting for non-marketing factors. Modern frameworks such as Google Meridian explicitly frame MMM as causal inference and expose assumptions. MMM can address channels that are hard to user-track, but it still depends on data quality and causal assumptions and benefits from calibration with experiments.
A mature system connects the layers
Operate with platform data, validate important assumptions with experiments, and use broader models for portfolio allocation. Feed experimental knowledge into model priors where appropriate. The goal is not one perfect number; it is a decision system where methods cross-check each other and uncertainty is visible.