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The default assumption
When attribution doesn’t make sense, the solution seems obvious:
Change the model.
- last-click
- first-click
- data-driven
- position-based
The expectation is that a better model will produce better answers.
It doesn’t.
What attribution models actually do
Attribution models don’t create data.
They interpret it.
They take:
- recorded events
- identified users
- defined sessions
…and assign credit based on a set of rules.
If the inputs are incomplete or inconsistent, the output will reflect that.
Changing the model changes the distribution—not the truth
Switching models will change your numbers.
- channels gain or lose credit
- performance appears to shift
- reports tell a different story
But nothing about the underlying data has improved.
You’re applying different logic to the same inputs.
Incomplete data doesn’t become complete
Attribution models operate within constraints.
If your data includes:
- missing transactions
- fragmented user identity
- inconsistent event definitions
- tracking gaps across devices or sessions
…then every model is working with a partial view.
Changing the model does not fill those gaps.
It only redistributes what is already there.
Why this creates false confidence
Model changes often feel like progress.
They produce:
- cleaner-looking reports
- more intuitive channel splits
- numbers that align more closely with expectations
But this is alignment with assumptions—not reality.
The system remains unchanged.
Where attribution is actually determined
Attribution is not created in the model.
It is determined upstream—by how your system is defined.
It depends on:
- how events are structured
- how users are identified
- how sessions are defined
- how logic is applied across systems
Why models still matter
Attribution models are not useless.
They provide:
- a consistent way to interpret data
- a framework for comparison
- a lens for decision-making
But they are only as reliable as the data they interpret.
A good model applied to inconsistent data produces inconsistent results.
What actually improves attribution
Attribution improves when the system improves.
That means:
- reducing data loss
- aligning identity across sessions and platforms
- standardizing event definitions
- applying consistent logic
When the system is stable:
- models become more meaningful
- differences become explainable
- decisions become more reliable
What this leads to
If attribution doesn’t make sense, changing the model is not the solution.
It’s a change in interpretation—not an improvement in accuracy.
Reliable attribution comes from a system that produces consistent, complete, and aligned data.
The next step
Before evaluating attribution models, you need to understand how your data is actually being produced.
An Evaluate engagement identifies:
- where data is incomplete or inconsistent
- how attribution is being distorted
- what is required to improve reliability
From there, model choice becomes meaningful—because the system behind it is stable.
Start with Evaluate.
Doug McCaffrey
Designs and maintains analytics systems that remain reliable over time.
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