Marketing Mix Modeling Brief Builder
Create a practical marketing mix modeling brief that defines channels, data requirements, assumptions, and decision outputs before analysis starts.
Prompt Template
You are a marketing analytics lead. Create a marketing mix modeling project brief for the business below. Business type: [ecommerce, SaaS, marketplace, retail, etc.] Markets/regions: [list] Channels in scope: [paid search, paid social, TV, email, affiliates, etc.] Primary KPI: [revenue, conversions, qualified leads, subscriptions] Available data history: [months/years] Known data issues: [tracking gaps, promo effects, missing offline spend] Decision to support: [budget allocation, incrementality, seasonal planning] Stakeholders: [CMO, finance, agency, founders] Provide: 1. Problem statement and success criteria 2. Data inputs needed and granularity requirements 3. Variables to control for, including seasonality and promotions 4. Model assumptions and where they may break 5. Output views stakeholders will care about 6. Risks that make MMM unreliable and how to mitigate them 7. A 30-60-90 day project plan
Example Output
MMM Brief — DTC Skincare Brand
Objective
Estimate the incremental contribution of paid social, paid search, influencer spend, and email so the team can reallocate Q3 budget with less guesswork.
Required Inputs
- Weekly spend by channel and campaign for 24+ months
- Weekly revenue and new customer orders
- Promotion calendar with discount depth
- Website sessions and branded search trend proxy
- Inventory outages and major PR moments
Main Risks
- Only 8 months of consistent TikTok spend, weak signal for saturation curves
- Promo periods overlap with influencer bursts, making attribution messy
- Regional spend not split cleanly in current finance export
30-60-90
**30 days:** data audit and KPI definitions
**60 days:** feature engineering and first-pass model review
**90 days:** stakeholder readout with scenario planner for budget shifts
Tips for Best Results
- 💡MMM is a planning tool, not a magical lie detector, so ask the model to surface assumptions explicitly
- 💡If you do not have at least 18-24 months of decent data, request alternatives like geo tests or simpler channel analysis
- 💡Include finance early because spend and promo data is usually messier than ad platform screenshots suggest
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