Know Exactly Where Every
Marketing Dollar Goes
Media Mix Modeling used to cost $100K+ and take months. Upload your spend and revenue data, and get channel contribution analysis, diminishing returns curves, and optimal budget allocation—in minutes, not months.
The Budget Allocation Problem
You're spending across 5+ channels. Do you actually know which ones work?
Every Platform Claims Credit
Google says Google drove the sale. Meta says Meta drove it. Email says email closed it. They can't all be right. MMM uses regression on your actual revenue data to measure true contribution—no platform bias.
Cookies Are Dying
Pixel-based attribution breaks more every year. MMM works with aggregated spend and outcome data—no cookies, no tracking pixels, no iOS privacy restrictions. It works the same whether users opt in or out.
You're Probably Over-Spending Somewhere
Every channel has a saturation point. MMM finds the diminishing returns curve for each channel, showing exactly where additional spend stops producing results and where budget is better allocated elsewhere.
How Media Mix Modeling Works
Upload spend data + outcomes. Get the full picture.
Three Steps to Your Media Mix Model
No consultants. No months of waiting.
Prepare Your Data
Gather weekly or daily data with columns for each channel's spend (Google Ads, Meta, TV, etc.) and your outcome metric (revenue, leads, conversions). At least 6 months for reliable results.
Upload Your CSV
Drop the file into MCP Analytics. The system detects spend columns and outcome variables automatically. You can adjust mappings if needed, or let the AI agent configure the analysis for you.
Get Your MMM Report
Receive a full media mix model: channel contribution percentages, response curves, optimal budget allocation, and scenario planning to test budget shifts before you make them.
What Your MMM Report Includes
Everything you need to make smarter budget decisions
Channel Contribution
What percentage of revenue each channel is responsible for. Based on regression, not self-reported platform metrics.
Response Curves
Diminishing returns curves for each channel. See exactly where spend stops being productive and where it's still scaling.
Optimal Budget Split
Data-driven budget allocation across channels, maximizing total outcome for your current spend level.
Adstock Effects
How long each channel's impact lasts after spend stops. TV may carry over for weeks; paid search may decay in days.
Seasonality Decomposition
Separate marketing-driven revenue from seasonal and trend effects. Know what your marketing actually contributed vs. what would have happened anyway.
Scenario Planning
Test "what if" budget shifts. What happens if you move 20% of TV budget to Meta? See projected impact before spending a dollar.
See What You'll Get
Interactive MMM report with channel contributions, response curves, and budget optimizer
MMM Sample Report Coming Soon
We're building the Media Mix Modeling module now. When it ships, this section will show a full interactive report with channel contribution charts, diminishing returns curves, and budget allocation recommendations—all generated from real demo data.
Preview a Marketing Spend Analysis in the meantimeMCP Analytics vs Traditional MMM
Enterprise-grade modeling without the enterprise price
Media Mix Modeling FAQ
What is Media Mix Modeling?
Media Mix Modeling (MMM) is a statistical technique that measures how each marketing channel contributes to business outcomes. Unlike pixel-based attribution, MMM works with aggregated data—no cookies, no tracking pixels. It can measure both online and offline channels, and it's not affected by privacy changes like iOS restrictions.
How much data do I need?
For reliable results, you need at least 6 months of weekly data (26+ data points) across your marketing channels and outcome metric. More data is better: 1–2 years of weekly data produces the most robust models. The data should include variation in spend levels—if you always spend the same amount, the model can't measure responsiveness.
How is MMM different from multi-touch attribution?
Multi-touch attribution tracks individual user journeys using cookies and pixels. MMM uses aggregated data and regression to measure channel impact. MMM can measure offline channels, doesn't depend on cookies, and accounts for external factors like seasonality. MTA provides user-level insights but breaks down with privacy changes and can't measure offline.
Can MCP Analytics handle adstock and carryover effects?
Yes. The MMM module models adstock (the lagged effect of advertising) and carryover effects automatically. A TV ad you run this week may still drive conversions next week; the model captures these decay patterns for each channel and factors them into contribution estimates.
Do I need a data scientist to use this?
No. Traditional MMM requires specialized consultants costing $50K–$200K per engagement. MCP Analytics automates the statistical modeling and presents results as actionable business recommendations: which channels to increase, which to decrease, and how to reallocate budget for maximum ROI.
Media Mix Modeling Resources
Understand attribution, channel optimization, and marketing measurement
Ready to Know Where Every Dollar Goes?
Sign up for early access to Media Mix Modeling. Upload your marketing data and start with the analyses available today.