Attribution is not causality.
Your reports aren’t lying to you. They’re answering the wrong question. “Which ads were near the revenue?” is not “which spend caused it?” Mediaura Signal answers the second one: causal impact in dollars, anchored to real experiments, confidence quantified. One number you can take to the board.
The Problem
The Wrong Question Has Four Ways of Producing Wrong Answers
Attribution counts who was standing nearby when revenue happened. That's correlation. It feels like measurement, it charts beautifully, and it collapses the moment you ask the only question a budget decision needs: what would have happened without the spend?
And even as correlation, the data underneath is quietly broken in four ways:
Tracking decays silently.
Tags accumulate, properties duplicate, a release ships and a pixel stops firing. Nobody notices until you've made a quarter of budget decisions on corrupted data.
Identity fractures across the journey.
One customer sees an ad on their phone, researches on a laptop, calls the front desk, and walks in. Your analytics counts four strangers and credits none of them.
Platforms grade their own homework.
Google reports Google's wins. Meta reports Meta's. Add the claims together and your channels "drove" 240% of your actual revenue.
Revenue lives where tracking doesn't.
The money changes hands at the POS, in the CRM, on a phone call. Attribution that can't reach those systems optimizes toward form fills and hopes they correlate with dollars. They usually don't.
Correlation on top of broken data: that's what most marketing reporting is. There's a name for it. Attribution theater: confident numbers that win the meeting and lose the money. Mediaura Signal was built to end the genre: fix the data first, then measure cause, not proximity.
The Answers
Three Answers You've Never Gotten From Marketing
You've seen a thousand marketing reports. Impressions, clicks, cost per lead, arrows pointing up. And if you've ever presented one, you know the private version of the problem: standing behind numbers you don't fully believe, hoping nobody asks the hard question. Here's what has never once been on the page:
What did the spend cause?
Not what it touched. What it caused: revenue that would not exist without it, stated in dollars, with the model's confidence visible.
Causal receipts, not activity summaries.
How sure are we?
Every number Signal publishes carries its own confidence, calibrated against real experiments. When the evidence is thin, the interval says so.
A number that admits uncertainty is a number you can actually use.
What's it worth to change?
Recommendations quantified in dollars, including the moves we evaluated and rejected, so the reasoning is auditable.
Handed to whoever runs your marketing to execute.
Signal generates these answers every reporting period, automatically, against your live data. The hard question stops being a threat. It becomes the part of the meeting you're ready for.
How It Works
Four Layers. One Job: Prove What Caused What.
Causal measurement is only as good as the data underneath it. That's why Signal is built as one connected system, not a bolt-on dashboard, and why each layer exists to make the one above it trustworthy.
- 1
Signal Tracker
First-party capture on your own domain. It records the events off-the-shelf pixels miss and connects the systems that don't talk to each other: call tracking to CRM, POS to ad platforms. HIPAA-compliant by design, BAA included, live in production today.
- 2
Identity Resolution
One customer, not four strangers. Every event gets stitched to a single customer record across devices, sessions, and offline touchpoints.
- 3
Revenue Mapping
Marketing connected to money, not proxies for money. Every closed transaction, whether it lands in the POS, the CRM, the EMR, or on a phone call, is mapped back to the campaigns that earned it.
- 4
The Mediaura Causal Engine (M-CE)
The layer that answers the question the rest of the industry avoids: what would have happened without the spend? M-CE runs multiple independent models, validates them against real experiments, and refuses to publish a number it can't defend.
When it says a channel caused $340K, it means $340K that would not have happened otherwise, and the evidence behind that claim is one click away.
Want the full methodology, every model, every validation step, every diagnostic? It's documented the way your auditor wishes everything were.
How the math worksIndependence
We Measure. Your Team Acts.
Google grades Google’s homework. Meta grades Meta’s. And any agency that both buys your media and reports on it is grading its own. The numbers can’t be trusted until the scorekeeper has no stake in the score.
Signal is the independent measurement layer. It works alongside whoever runs your marketing, your in-house team or your agency, including us. Signal reports what the evidence supports, hands the recommendations to your team, and never touches the media.
That separation isn’t a limitation. It’s the entire reason the number is worth taking to your board.
The Intelligence Layer
Meet Aura: The Analyst Who Never Guesses
Aura is how you talk to everything above. Ask a question in plain English, get an answer backed by your live data.
“Why did cost per admission spike in Cincinnati last week?”
“What happens if we move 20% of Meta budget to Google?”
“Which locations actually gained from the LTO?”
Here’s what makes Aura different from every AI assistant currently making up numbers: Aura cannot state a figure that a tool didn’t return. When you ask a question, Aura queries your production systems, waits for real results, and only then answers. No tool result, no number. That’s architecture, not a promise.
And every Monday at 7 AM, Aura reviews the entire previous week on its own, every channel, location, and campaign, and writes your team a short narrative report: what mattered, why, and what to do about it. It’s on the executive’s desk before the first cup of coffee, which means Monday’s meeting starts at the conclusion instead of the pulling-up-of-numbers.
What Changes When You Run on Signal
Industries
Built for How Your Revenue Actually Happens
Every industry breaks attribution differently. Signal is configured per vertical to fix the break that's costing you money.
Healthcare & Behavioral Health
The conversion isn't a form fill. It's an admission, and the path to it runs through PHI-restricted systems where most measurement tools legally can't operate. Signal was built from the ground up for this: HIPAA-compliant capture with BAA, causal measurement from first click through inquiry, assessment, and admit, and channels ranked by verified patient value, not admit count. Digital's halo into referral-driven admissions gets measured, not guessed at.
Built for healthcare? Start here →Restaurants & Multi-Location Retail
Most restaurant tools count clicks and online orders. Signal counts the check at the table. Ad spend connects to your POS (Toast, Square, Olo, whatever you run) across every location, with causal measurement run per location, because a Cincinnati Tuesday and a Charleston Saturday are different businesses. Foot traffic modeling, daypart and LTO performance, and budget reallocation based on measured lift, not vibes.
Multiple locations? Request a demo →B2B & Professional Services
Standard analytics gives up after 30 days. Your sales cycle is 127. Signal traces influence across the entire life of a deal, from first anonymous visit to signed contract, reconciled against your CRM, so marketing gets credit for the pipeline it actually built. Attribution and reconciliation are live today; the full causal modeling layer for long-cycle B2B ships through 2026.
Long sales cycle? Request a demo →More verticals in development. Higher Education is next. In development
The Backtest
Start With the Backtest
Don't take the model's word for it. Make it prove itself on your own history.
We replay 12 to 36 months of your data through the causal engine and show you the specific decisions the evidence would have changed: the channel that was quietly cannibalizing, the cut that cost more than it saved, the spend that was buying demand you already owned. Fixed fee. Fixed timeline. The analysis is yours to keep, whatever you decide next.
How it works
Fit check first.
An honest data-sufficiency assessment before you pay a dollar. If your volume can't support causal confidence, we'll tell you and part friends.
The Backtest.
Your history, replayed through the engine, with every finding tied to a decision.
The Verdict.
A readout in operator language: contribution margin, cost per admission, cost per cover. What was causal, what was coincidence, what it means for next quarter's budget.
From there, Signal runs continuously, so every future budget meeting starts from evidence.
Not ready? Read The State of Attribution TheaterComing soon or the M-CE methodology paperComing soon. Prefer to start with a conversation? Request a demo.
Security team reviewing us? Read our security posture
See What's Hiding in Your Marketing Data
Most demos we run uncover broken tracking and missing revenue inside the first fifteen minutes. We'll show you yours.
What happens next:
- 30-minute working session with a Mediaura engineer (not a sales rep)
- Live audit of your current tracking and attribution gaps
- A specific, prioritized list of what's broken and what it's costing you
- Industry-relevant case studies and a clear path to value
Security team reviewing us? Read our security posture →