BUSINESS SCENARIOS · ILLUSTRATIVE
From business questions to reviewable decisions
See how brands, agencies, growth teams and analysts can structure an MMM analysis.
01
DTC brands · Illustrative
Which channels merit further validation before the next budget cycle?
A team invests in search, social and video and wants to discuss platform attribution reports against one business KPI.
Align the inputs
Align channel spend and sales or conversions by date, with price, promotion, stockout and holiday information.
Review the evidence
Check correlated spend and historical coverage, then examine out-of-sample performance and uncertainty in channel contributions.
Compare scenarios
Compare budget scenarios within model and business constraints; flag channels relying on extrapolation or lacking experimental support.
The deliverable
Deliver a budget scenario for review and a prioritized list of data gaps or channel experiments.
02
Media agencies · Illustrative
Make the analysis traceable for each client
An agency serves multiple clients and must explain why different datasets call for different models and evaluation criteria.
Organize by project
Record each project's data window, mapping, KPI and assumptions to keep client definitions separate.
Choose available engines
Configure engines available in the deployment for each dataset; use common validation windows for meaningful comparisons.
Prepare review materials
Deliver diagnostics, conclusions, limitations and proposed actions together, rather than reporting only the largest ROI.
The deliverable
Create a repeatable delivery structure while preserving independent evidence for every client.
03
Growth and finance · Illustrative
Align goals, constraints and evidence before a budget review
Growth teams focus on opportunities while finance teams focus on total spend and acceptable risk. Both need a scenario they can review together.
Define business constraints
Define the planning period, currency, total budget, channel bounds and any test budget to retain.
Compare candidate scenarios
Compare baseline and proposed allocations, prediction definitions and uncertainty, while respecting operational limits.
Plan follow-up review
Record the implementation window and actual spend changes, then review business outcomes; do not present forecasts as realized revenue.
The deliverable
Focus the review on testable assumptions and explicit trade-offs, without treating one model score as the final answer.
04
Data analysts · Illustrative
Turn model disagreement into the next validation question
Different engines can predict similarly while assigning different channel contributions. Analysts need to identify why.
Compare like with like
Fix the data version, time window, KPI and controls; record engine settings and random seeds.
Inspect diagnostics
Inspect temporal holdouts, residuals and parameter stability, plus Bayesian convergence and posterior intervals where applicable.
Add independent evidence
Align experiment estimands with the model, then compare refitted results without assuming calibration must improve predictions or narrow intervals.
The deliverable
Produce an explanation of disagreements, a validation plan and the limits of each conclusion for the next decision.
Turn a scenario into your next step
Explore the sample budget workflow, then review your data and implementation scope once your objective is clear.