Stop Guessing Which
Channels Actually Work
Last-click attribution tells you who scored the goal. Media mix modeling tells you who built the play. Upload your spend and revenue data, and MCP Analytics quantifies each channel's real contribution—including the ones that can't be tracked with cookies.
The Attribution Problem Every Marketer Faces
Cookie deprecation, cross-device journeys, and offline channels make click-based attribution increasingly unreliable.
Channel Contribution Decomposition
Regression analysis separates each channel's impact on revenue from baseline demand and seasonal effects. See what percentage of outcomes each channel actually drives—not just what it claims credit for.
Saturation Curves
Every channel hits diminishing returns at some spend level. MMM models the saturation curve for each channel, showing you exactly where additional spend stops producing proportional results.
Budget Optimization
Given your total budget, MMM recommends the optimal allocation across channels to maximize outcomes. Run what-if scenarios to see the expected impact before shifting a single dollar.
Data In. Channel Insights Out.
No consultants. No six-figure projects. Just your data and our models.
Prepare Your Data
Gather weekly or monthly data with a column for each channel's spend (Google Ads, Facebook, TV, Email, etc.) and your outcome metric (revenue, conversions, or leads). 12+ months of history is ideal.
Upload Your CSV
Drop the file into MCP Analytics. The system identifies spend columns and the target metric, then runs regression modeling to decompose channel contributions.
Get Channel Insights
Receive a report showing each channel's contribution, saturation curves, adstock effects, and data-driven recommendations for budget reallocation.
What the MMM Module Will Deliver
Full-featured media mix modeling in an interactive report
Channel Decomposition
Percentage of revenue attributable to each marketing channel, separated from organic baseline and seasonal trends.
Saturation Curves
Diminishing returns modeling for each channel. See where you're on the efficient frontier and where you're overspending.
Adstock & Carryover
How long each channel's effect lasts after spend stops. Some channels have immediate impact; others build over weeks.
Budget Optimizer
Given your total budget, the optimal spend allocation across channels to maximize the outcome metric.
Scenario Planning
What-if analysis: "What happens if I shift 20% from TV to digital?" See projected impact before making changes.
Seasonal Adjustment
Separates true channel lift from seasonal demand patterns so you don't credit December revenue to your November ads.
While the full MMM module is in development, you can run regression-based marketing spend analysis on your campaign data today. See marketing analytics →
MCP Analytics MMM vs Traditional Approaches
Enterprise-grade modeling without the enterprise price tag
| Capability | MCP Analytics | Consulting Firms |
|---|---|---|
| Channel contribution decomposition | ||
| Time to results | Minutes | 6-12 weeks |
| Cost | From $29/mo | $50K-$500K+ |
| Saturation curve modeling | ||
| Self-service (no consultants needed) | ||
| What-if scenario planning | Additional cost | |
| Refresh with new data | Anytime | New engagement |
| Interactive shareable reports | PDF / slides |
Media Mix Modeling FAQ
What is media mix modeling?
Media mix modeling (MMM) is a statistical technique that measures the impact of each marketing channel on business outcomes like revenue or conversions. Unlike click-based attribution, MMM uses regression analysis on historical spend and outcome data to quantify each channel's contribution—including channels that can't be tracked with clicks, like TV, radio, and billboards.
How much data do I need for media mix modeling?
Ideally, 12-24 months of weekly data with spend broken out by channel and a clear outcome metric (revenue, conversions, signups). More data points produce more reliable models. At minimum, you need enough weeks with variation in spend across channels for the regression to identify each channel's effect.
How is MMM different from multi-touch attribution?
Multi-touch attribution (MTA) uses user-level tracking data to credit individual touchpoints along a conversion path. It requires cookies or device IDs and only captures digital channels. Media mix modeling uses aggregate spend and outcome data—no user tracking needed. It works for all channels including offline (TV, print, events) and is not affected by privacy changes like cookie deprecation.
What channels can media mix modeling measure?
Any channel where you can track spend: paid search, social ads, display, email, TV, radio, print, direct mail, sponsorships, events, and more. MMM works especially well for channels that traditional attribution misses—brand campaigns, offline media, and upper-funnel awareness spending.
When will MCP Analytics media mix modeling be available?
The full MMM module is currently in development. Sign up for early access to be notified when it launches. In the meantime, the marketing spend analysis module provides regression-based channel analysis on your campaign data today.
Marketing Attribution Resources
Deep-dives on attribution, channel analysis, and marketing measurement
Analysis Methods
Related Reports
Be First to Use Media Mix Modeling on MCP Analytics
Sign up for early access. We'll notify you when the MMM module launches. In the meantime, try marketing spend analysis on your data today.