Proprietary Methodology
Your platforms grade their own homework.
Fusion doesn't.
Fusion is King Data Lab's media mix modeling engine. It measures what your ad platforms can't — the true incremental contribution of every channel, where your spend hits diminishing returns, and exactly where to put the next dollar.
The problem
Platform attribution is structurally broken.
Every platform uses a measurement system it built and maintains to make its own numbers look as good as possible. Add iOS privacy changes and browser cookie deprecation and you're making six-figure budget decisions based on data that's missing 30 to 60 percent of the actual conversion path.
Double-counting
Platform-reported conversions across channels routinely sum to 2x or 3x your actual revenue. Each platform claims full credit for the same sale.
Blind spots
Upper-funnel channels like OTT, display, and streaming audio drive real influence but get zero last-click credit. Most teams cut them and wonder why performance drops.
No saturation signal
Platforms will take every dollar you give them and report conversions on it — they have no incentive to tell you you've already passed the point of efficient return.
What Fusion reveals
Three outputs that change how you allocate budget.
True incremental contribution per channel
Not who clicked last. Not who touched the path. How much of your actual revenue can be attributed to each channel when you hold everything else constant. This number is almost always different from what your dashboards show and the direction of the difference often surprises teams.
Channel Contribution — Sample
MMM-fitted incremental contribution · Sample data
Where your spend hits diminishing returns
Every channel has a point where the curve flattens — where additional spend stops generating proportional revenue. Fusion maps that curve for every channel in your mix. This is the number your platforms will never show you because they have no incentive to.
Saturation Curve — Paid Social Sample
Hill function fitted per channel · Sample data
The mathematically optimal budget reallocation
Once the model has fitted response curves for every channel, a constrained optimizer finds the exact budget split that maximizes leads or revenue without changing your total spend. The output is a specific, defensible reallocation your team can act on, not a directional suggestion.
Budget Reallocation — Sample
SLSQP constrained optimization · Sample data
How it works
Three phases. One model built on your data.
Data intake
We pull your historical spend by channel, your conversion or revenue outcomes, and any external factors: seasonality, promotions, market events. You don't need a data warehouse. A clean export from your ad platforms works.
Model fit
We fit Hill saturation curves and adstock decay parameters for every channel using your actual data. The model produces confidence intervals on every estimate — you see how certain the outputs are, not just the point prediction.
Playbook delivery
You receive a branded dashboard with your saturation curves, channel contribution breakdown, optimized budget allocation, and a 24-month revenue forecast. Updated monthly as new data comes in.
Who it's built for
Enterprise-grade MMM at a fraction of the agency price.
Most large agencies and platforms charge $50,000 to $200,000+ annually for media mix modeling — when they offer it at all. Fusion is built to deliver the same econometric rigor at a price point that makes sense for growth teams and mid-market brands.
Engagements start in the low thousands, not the tens of thousands. You get a real model, real saturation curves, and a real budget recommendation — not a deck with directional arrows.
The sweet spot is brands running $500K to $5M per year across 4 to 8 active channels. Below $500K the model cost starts to outweigh the efficiency gain. Above that threshold, a 15 to 20 percent reallocation on misallocated spend typically covers the engagement cost within the first quarter.
What you need to get started
- At least 24 months of weekly spend data by channel. More history produces tighter confidence intervals
- A consistent outcome metric: leads, revenue, transactions, or pipeline
- Spend data from your actual ad platforms. Exports from Google Ads, Meta, and any other active channels
- No data warehouse required. A clean spreadsheet export works fine
In-house growth teams
Running multi-channel media and need measurement that doesn't rely on platform self-reporting.
Performance agencies
Looking to add defensible MMM to their service offering without building an internal data science function.
CFOs and finance teams
Who need marketing to produce budget recommendations grounded in a defensible model rather than platform ROAS.
Ready to see what your channels are actually doing?
Book a 30-minute call. We'll tell you whether your data is ready for MMM and what the first output would look like on your actual numbers.