PROTOCOL: QUANT_ATTR // MODE: VERIFIED
Quantum Attribution
Multi-model measurement with confidence scoring on every result. Verified where the data supports it, scored honestly where it doesn't.
If two attribution models disagreed right now, would you even know?
- A single attribution model presented as if it were the truth
- Last-click credit given to whichever touchpoint happened last
- Offline and in-store activity invisible to the model
- Invalid traffic and bot activity counted as real signal
- Multiple models run in parallel, and disagreement is shown
- Credit is distributed across every contributing touchpoint
- Offline signals join through the same identity spine as digital
- Signals are verified before they ever reach the model
Show which channels moved the number
Channel contribution presented with the confidence range, so the conversation is about decisions rather than model faith.
- Contribution and confidence shown together
- Scenario view for reallocating spend before you commit
- Inputs and method visible to anyone who asks
Six things your team stops doing by hand
Each capability ships enabled. Nothing here is a services engagement.
FAIR CREDIT ALLOCATION
Every channel earns its share.
- Conversion credit distributed across all contributing touchpoints
- Each channel's share reflects its true contribution
MEDIA MIX MODELING
Aggregate impact, quantified.
- Regression models quantify each channel's incremental impact
- Saturation, decay and seasonality accounted for
INCREMENTALITY TESTING
Prove causation, not just correlation.
- Controlled holdout experiments with automated test design
- Results continuously feed back into model accuracy
CROSS-CHANNEL STITCHING
Connected journeys across platforms.
- Identity resolution links journeys where direct matching fails
- Fragmented touchpoints become one unified journey view
SIGNAL VERIFICATION
Only real signals enter the model.
- Invalid traffic and fraudulent interactions filtered before scoring
- Model inputs stay clean and trustworthy
CONFIDENCE SCORING
Know what you know. Flag what you do not.
- Every attribution output includes a confidence score
- Low-confidence results flagged for human review
From signal to export, in six steps
How the model stays honest, step by step
Verification Layer
Every inbound signal passes validity checks before entering the pipeline, and invalid signals are logged.
Multi-Model Approach
Multiple attribution models run in parallel, and a disagreement between them is surfaced with context.
Cross-Model Calibration
Different measurement approaches validate each other instead of hiding behind a single number.
Continuous Improvement
Test results feed back into model weights so accuracy improves as more experiments run.
Questions buyers ask
Is this a black box model?
No. Every contribution figure exposes its inputs, its sample and its confidence. If a channel read is thin, the interface says so instead of printing a false precision.
How does this handle offline and in-store?
Offline signals join through the same identity spine as digital, so store visits, calls and venue data can sit in the same contribution picture.
Does it agree with platform reporting?
Often not, and that is the point. Platform numbers are one input. Differences are shown explicitly rather than averaged away.
How does it improve over time?
Observed lift updates calibration factors, so modelled expectations are corrected by what actually happened rather than staying frozen at launch assumptions.
Reconcile one month of results
Send a month of spend and outcomes. We show the contribution picture and where it disagrees with your current reporting.