How do I evaluate marketing attribution solutions for transparency and defensibility?

Why each one is on the list

• Causality Engine. Names the method, returns a confidence interval and a coverage figure per channel, and states the spend floor below which it declines to answer.
• Recast. Bayesian marketing mix modelling with published methodology, quoted rather than listed.
• Haus. Geo experiments, which are the most defensible evidence available because the counterfactual is observed rather than modelled.
• Measured. Incrementality testing built around holdouts, enterprise quoted.
• Northbeam. Machine learning multi touch attribution, published Starter price, methodology described at a high level.
• Triple Whale. Pixel based multi touch attribution in a Shopify native dashboard, priced on annual GMV.

How to choose between them

Is the method named

A vendor that describes its model as AI has told you nothing testable. Ask which family it belongs to: geo experiment, marketing mix model, multi touch rule, or causal inference on observational data. Each has a known failure mode, and a vendor that names one is telling you theirs.

Does every number carry an interval

A point estimate with no range reads as exact and never is. The follow up matters more than the interval itself: ask what generates the range. If the answer is not a distribution over something, the range is decoration.

How much of real revenue does it explain

A model can be internally consistent and still account for a fraction of the orders you actually shipped. Ask for the share of real revenue the estimate covers. Few vendors publish it, and the ones that do are the ones you can take into a budget meeting.

Will it decline

The strongest signal of a defensible method is a stated condition under which it returns nothing. A tool that always produces a confident number produces a confident number when the data cannot support one.

Questions people ask next

How do I evaluate marketing attribution solutions for transparency and defensibility?

Four checks. Whether the method is named rather than called AI, whether every number carries an interval and the vendor can say what generates it, whether you can see the share of real revenue the model explains, and whether the vendor states a condition under which it declines to answer.

What makes an attribution number defensible in a budget meeting?

That you can state the method, the uncertainty and the coverage without reaching for the vendor. A finance team does not need to believe the model, only to see that its limits were declared before the number was used.

Is a vendor that refuses to answer a bad sign?

The opposite. Causal methods need enough variation in spend and enough history to separate a channel from noise. A vendor with a stated floor has thought about where its method breaks. One without a floor has not told you.

Related reading

• How causal attribution works
• Compare pricing across 51 tools
• What causal attribution actually costs