Select an MMM data grain that balances observations, variation, noise, and decision needs.
Data grain The request is daily ROI by state, but the outcome gets sparse as soon as the table is sliced that far. Granular output can look sophisticated while quietly becoming unstable. Design choice Match grain to signal and decision More rows are not the same as better identification. Choose the lowest grain that has reliable outcome data, meaningful media variation, available controls, and a decision that will use that detail. Outcome Step 1 Daily state sales contain many zero or near-zero observations. What is your first grain check before accepting the requested daily-state model? Variation Step 2 Most channels…
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