Stop Letting Ad Platforms
Grade Their Own Homework
Most agencies report what Google, Meta, and broadcast stations tell them. We build independent statistical models that reveal what's actually driving your sales — across every channel, simultaneously.
Request a Model ReviewAttribution Data Is Broken — and Most Agencies Don't Tell You
When every channel claims credit for the same sale, something is wrong with your measurement.
Platform Self-Reporting
Google reports Google's ROI. Meta reports Meta's. Neither accounts for what the other channel contributed — or what would have happened regardless.
GA4 Attribution Gaps
TV, radio, and direct mail drive real revenue that never registers in digital analytics. They look like they're not working because the tools can't see them.
Budget Decisions on Instinct
Without an independent model, budget shifts are educated guesses. Money follows dashboards, not actual performance.
What Is Media Mix Modeling?
Media Mix Modeling (MMM) is a statistical technique that analyzes your actual sales data alongside media spend — across every channel — to determine what's truly driving revenue.
Unlike platform dashboards, MMM is built on your data, not theirs. It isolates the real contribution of each channel, controls for seasonality and external factors, and produces a single independent view of performance.
The result: budget decisions grounded in evidence, not instinct.
How We Build Your Model
Data Collection & Validation
We pull your actual sales history, media spend by channel, and behavioral data — typically spanning 2–5 years. We validate for gaps, anomalies, and seasonality before modeling begins. Your data stays yours.
Statistical Regression Modeling
We run a multi-variable regression model that isolates the independent contribution of each media channel to your sales outcomes — controlling for baseline demand, seasonality, promotions, and market conditions.
Review & Calibration
Model outputs are reviewed against known business events — major promotions, seasonal patterns, market disruptions — to sense-check directional accuracy before the model informs any spending decisions.
Automated Weekly Delivery
Once built, the model runs on an automated schedule. Results — including channel ROIs, contribution trends, and spend recommendations — are delivered to your inbox every week without manual intervention.
Ongoing Budget Optimization
Every media planning conversation is grounded in model output. When the data signals a shift, we act on it — before the next quarter's plan locks in.
Every Channel. One Model.
Most attribution tools only see digital. Our models are built to include the full media mix — online and offline.
An Independent View of Every Channel — Updated Every Week
Your model isn't a static report. It's a live Tableau dashboard you can filter by market, channel, and time period — driven by an automated data pipeline that runs without manual intervention.
- Revenue contribution isolated by channel, year over year
- Filter to any individual market to compare performance
- Per-channel ROI calculated for every period — including negative signals worth acting on
- Weekly delivery to your inbox — no exports, no waiting on an agency report
Channel ROI — Not Impressions, Not CPMs
Every channel in your model carries an independently calculated return on investment. Here's an example from a real multi-location retailer we work with:
Weekly Policy Memo — Anonymized Client Example
Each week, the model flags channels with negative ROI for investigation and surfaces dormant channels with strong historical returns as reactivation opportunities — before budgets are locked.
The Model Tells You What Platforms Won't
In the example above, radio showed a −14.0x ROI. Without a model, spend in that channel would have continued based on reach metrics alone. The model caught it and flagged it for investigation — saving meaningful budget that could be redirected to channels with proven returns.
That's not a platform reporting on itself. That's independent evidence.
Evidence, Not Instinct
How our approach compares to conventional agency measurement:
Conventional Reporting
- Platforms report their own ROI
- TV and direct mail invisible in digital analytics
- Budget decisions from gut feel or prior year plan
- No model isolating each channel's true contribution
- Quarterly reports arrive too late to act on
- Attribution inflated by channels claiming shared credit
DW Creative MMM
- Independent model built on your actual sales data
- All channels — TV, radio, digital, mail — in one model
- Every budget recommendation tied to model evidence
- Channel contribution isolated, controlling for seasonality
- Weekly automated delivery — decisions stay current
- No platform has a stake in your model's results
What You Get
Custom MMM on Your Data
A validated regression model using your sales history and actual spend — not industry benchmarks or third-party panels.
Live Tableau Reporting
Interactive visualization showing channel contribution by time period and market. Filter and drill down without waiting on a report.
Weekly Policy Memo
Model outputs — channel ROIs, spend recommendations, and flags — delivered to your inbox every week via automated pipeline.
Budget Optimization
Every media planning conversation grounded in model findings. When data signals a shift, we act on it before budgets lock.
This Works Best For
Multi-Location Retailers
Running different media mixes across markets? The model isolates what's working in each location — not just the aggregate.
Home Services Companies
HVAC, roofing, remodeling, gutters. Long purchase cycles and offline conversion make digital attribution nearly useless without an independent model.
Advertisers Spending $500K+/Year
The model pays for itself when it prevents one bad budget shift. At meaningful spend levels, the signal-to-noise ratio makes MMM invaluable.
Brands Running TV or Radio
Broadcast spend is invisible in digital analytics. If you're running TV or radio, you need a model that can see it — or you're flying blind.
See What Your Media Mix Is Really Doing
We'll walk you through how the model works and what we'd need from you to build one. No commitment — just a straight conversation about your data.
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