PROTOCOL: QUANT_ATTR // MODE: VERIFIED
Quantum Attribution
No single model gets the last word. Every result shows how much to trust it: verified where the data supports it, marked as thin where it does not.
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
- More than one method, with the disagreements shown
- Credit is distributed across every contributing touchpoint
- Offline and digital measured in the same picture
- Invalid traffic kept out of the numbers
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.
- Each channel's contribution to the outcome, quantified
- Diminishing returns and seasonality taken into account
INCREMENTALITY TESTING
Prove causation, not just correlation.
- Controlled tests that show what a channel really added
- Measured lift outranks modelled lift in every result
CROSS-CHANNEL STITCHING
Connected journeys across platforms.
- Journeys connected across platforms and devices
- Fragmented touchpoints become one journey view
SIGNAL VERIFICATION
Only real signals enter the model.
- Invalid traffic and fraudulent interactions kept out
- What reaches the model is real activity
CONFIDENCE SCORING
Know what you know. Flag what you do not.
- Every attribution output shows how much to trust it
- Thin reads are marked as thin, never printed as precise
How it stays honest
Quantum Attribution measures the same outcome more than one way, shows you where the methods agree and where they do not, and marks every result with how much to trust it.
Four jobs it pays for in the first quarter
Budget reallocation
See which channels moved the number before you move the money, with the confidence range beside every figure.
Platform reporting reconciliation
Put platform-reported results next to an independent read, see where they differ, and decide which to trust.
Offline and in-store measurement
Store visits, calls and venue data sit in the same contribution picture as digital, not in a separate report.
Incrementality proof
Show a client what a channel really added, with a test behind the claim rather than a model's opinion.
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?
Lift you have actually measured in market counts for more than lift a model assumed, so the picture gets closer to reality with every campaign you run.
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.