Build a Marketing Mix Model Framework with AI

You are a marketing analytics expert helping me design a Marketing Mix Model (MMM) to measure the true ROI of my marketing investments and eliminate guesswork from budget allocation. ## My Business Context - Industry: [YOUR INDUSTRY] - Annual marketing spend: $[TOTAL SPEND] - Key revenue channels: [LIST 3-5 CHANNELS: paid search, social, email, direct, partnerships, etc.] - Sales cycle length: [DAYS/WEEKS] - Current attribution method: [CURRENT METHOD: last-click, first-touch, linear, or none] - Data availability: [SPECIFY: GA4, CRM, ad platform APIs, offline sales data, etc.] ## My Challenge I need to understand which marketing activities actually drive revenue, not just clicks or impressions. I'm drowning in operational debt—too many tools, unclear ownership of results, and no clear path from marketing output to pipeline impact. I need a lightweight MMM that proves lift fast without requiring a data science team. ## What I Need From You ### 1. MMM Framework Design Create a step-by-step framework for building an MMM that: - Identifies the 5-7 most impactful marketing variables to model - Accounts for seasonality, external factors, and lag effects - Produces clear, defensible ROI metrics by channel - Can be built in 6-8 weeks with existing team and tools ### 2. Data Requirements Checklist List the minimum viable data I need to collect, including: - Historical data timeframe (how many months/years) - Granularity required (daily, weekly, monthly) - Data sources and integration points - Data quality checks and validation rules ### 3. Implementation Roadmap Provide a phased approach: - Phase 1 (Weeks 1-2): Data audit and preparation - Phase 2 (Weeks 3-4): Model building and testing - Phase 3 (Weeks 5-6): Validation and sensitivity analysis - Phase 4 (Weeks 7-8): Reporting dashboard and stakeholder alignment ### 4. Key Metrics to Track Define the core outputs: - Incremental revenue by channel - Marketing elasticity (% change in spend = % change in revenue) - Optimal budget allocation recommendation - Confidence intervals and model accuracy benchmarks ### 5. Common Pitfalls to Avoid Highlight 5-7 mistakes that derail MMM projects and how to prevent them. ### 6. Quick-Win Opportunities Identify 2-3 immediate actions from preliminary analysis that could improve ROI within 30 days, even before the full model is complete. ## Output Format Structure your response with clear sections, bullet points for easy scanning, and specific examples relevant to [YOUR INDUSTRY]. Include a sample dashboard layout showing how results would be visualized for executive stakeholders.