Articles · Promotional Measurement

How much of your marketing return depends on the marketing

Outcome divided by spend includes the demand that was already forming. Separating the two changes which channels look worth funding.

Promotional measurement practice · 6 min read ·

Every brand has a return figure. Rather fewer can say what share of it would have arrived with no promotion at all. For a mature specialty brand that share routinely runs 70 to 85% of volume, and it is the single largest thing standing between a reported number and a decision.

One brand, two methods
Reported on last touch5.6x
Modelled, demand separated2.6x

Both figures are correct. They answer different questions. The first describes what converted; the second describes what depended on the spend. Budget conversations are better served by the second.

Why the channel ranking changes

Channels that look strongest under last-touch reporting tend to sit closest to a decision already made — branded search, calls into prescribers already writing, retargeting people who arrived deliberately. They convert efficiently, which is worth having. It is simply a different job from creating the demand in the first place.

Both jobs are worth funding. They are easier to fund well once you can see which is which.

Checks worth running on your own numbers

In roughly this order
1
Hold periods back before fitting

Six to eight recent periods, predicted cold. In-sample fit improves with every term you add; out-of-sample error does not.

2
Put baseline share beside the return

Same page, every time. It is a one-line change to a report and it reframes the conversation that follows.

3
Flag channels that move together

Two channels flighting in lockstep cannot be separated by any estimator. Reporting them jointly is more useful than splitting them.

4
Hold one matched region flat

Read it at twelve weeks. That read becomes the opening evidence for the next decision.

The honest limit: this estimates average response across the period modelled. It says nothing about whether the same response holds after a competitor launch or a formulary change — which is exactly why the held-back region matters more than the model.

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