Proprietary Methodology
Your platforms grade their own homework.
Fusion doesn't.
Fusion is King Data Lab's media mix modeling methodology. It estimates the incremental contribution of each channel, identifies diminishing returns, and produces a defensible budget scenario grounded in your historical data.
The problem
Platform attribution is structurally broken.
Every platform reports performance through its own attribution system. Add privacy restrictions, identity loss, and inconsistent tracking, and teams can end up making major budget decisions from an incomplete view of the conversion path.
Double-counting
Platform-reported conversions can sum to more than the conversions your business actually recorded because multiple platforms may claim 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
A modeled budget reallocation
Once the model has fitted response curves for each eligible channel, a constrained optimizer tests budget splits against your chosen outcome and business rules. The result is a transparent scenario your team can review, challenge, and use in planning.
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
A practical MMM engagement for growth teams.
Fusion is designed for teams that need a decision-ready model without adding a permanent data science function. The work stays focused on your business question, available history, active channels, and planning constraints.
You receive the model outputs, assumptions, uncertainty ranges, and a clear allocation playbook—not a black-box score or a deck of unsupported recommendations.
Fusion may be a good fit when
- You manage several paid channels and need a cross-channel view
- You have at least two years of consistent weekly data
- Your team can act on quarterly or annual budget decisions
It may be too early when
- Tracking definitions or outcome data are still changing
- Spend has little variation or is concentrated in one channel
- You need user-level attribution rather than market-level planning
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.
Not ready for modeling yet? Audit the measurement foundation first →