An attribution tool is only as good as the signals it ingests, and it can't tell you when those signals are wrong. We audit what raw data feeds the tool - pixels, platform APIs, sometimes surveys - and reconcile its output against your orders table, treating the tool as an instrument to check rather than an oracle to trust.
The situation
You're paying for a tool meant to end the "which channel is working" debate, and instead its top-ranked channel doesn't match what the team can see happening day to day - orders that don't seem to line up with the model's story.
The pain
Every dollar allocated based on a distorted model is a dollar potentially misallocated, and the tool's confidence in its own number doesn't tell you whether the number is right.
What we implement
We audit what raw signals the tool actually ingests - pixel data, platform APIs, sometimes surveys - and reconcile its output against your orders table, treating the attribution tool as an instrument to check rather than an oracle to trust by default.
What you get
- Marketing budget allocated to what's proven to work, backed by evidence the tool's model can actually be checked against.
- A clear answer on whether the tool or the underlying tracking feeding it is the actual problem.
- One documented statement of which instrument answers which question, so the argument doesn't recur every quarter.
For your customers, this means the channel mix the brand invests in is the one it has actually confirmed works - not one shaped by an unreconciled model quietly feeding on bad tracking data.
Illustrative, not a measured result: a team whose MMM tool showed paid social as the clear top channel, while orders told a flatter story, might find the tool's model was double-counting a warm retargeting audience already captured elsewhere - a tracking fix, not a tool replacement, closed the gap. This is a scenario meant to show the shape of the outcome, not a client figure.