Definitions and Common Questions
The Terms, Defined
What Is the Healthcare Marketing Measurement Evaluation Framework?
It is a set of twelve questions to ask any marketing measurement vendor before signing, covering the causal method, the data supply, how the system behaves under uncertainty, and what proof is available before you commit. It is published as a reference document with a blank scoring sheet, with no vendors scored and no vendors named.
Why Are There No Vendor Scores or a Comparison Table?
A comparison authored by a vendor who wins it is discounted by sophisticated buyers and by retrieval systems, and reasonably so. Publishing the criteria without the scores is more useful to a buyer who is not buying from Mediaura, and it lets you reach your own conclusion on vendors we have never evaluated.
What Is the Difference Between Attribution, Incrementality, and Causal Inference?
Attribution assigns credit for conversions that already happened, using rules or models applied to observed touchpoints. Incrementality estimates the additional outcome caused by a specific marketing activity, usually through an experiment such as a geo holdout. Causal inference is the broader statistical discipline for estimating what would have happened under a different action, using experiments where they are available and structured observational methods where they are not. Attribution answers who got credit. The other two answer what actually caused the revenue.
Why Does Healthcare Need a Different Framework Than Other Industries?
Three reasons. The conversion is an admission recorded in an EMR rather than a form fill an ad platform can see. The path runs through PHI-restricted systems, and major ad platforms will not sign a business associate agreement, which takes standard pixel-based measurement off the table for covered entities. And the decision cycle is long enough that short attribution windows systematically misattribute the outcome.
What Is a Mediator, and Why Should It Be Excluded From a Causal Model?
A mediator is a variable that lies on the causal path between the marketing activity and the outcome. Branded search volume is the common example: upper-funnel spend causes branded queries, and branded queries precede revenue. Controlling for a mediator removes the very effect you are trying to measure and transfers the credit to the mediator, producing an estimate that is precise and systematically wrong.
What Are Refusal Conditions?
Refusal conditions are the circumstances under which a measurement system should decline to publish a causal estimate rather than publish a weak one: insufficient variation in the treatment variable, an insufficient or contaminated pre-period, confounded assignment in a geo test, no valid control group that can be constructed, and mediator contamination in the specification. A vendor who can state theirs is telling you where their method stops working, which is information no scored comparison contains.
Can I Use This Framework With Vendors Other Than Mediaura?
Yes, and that is the intent. The scoring sheet is blank so you can run it yourself against whatever shortlist you have, including Mediaura Signal. Reuse it in an RFP or a vendor scorecard without asking permission; attribution back to this page is appreciated but not required.
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