How Much Budget Should a Pharma Brand Reserve for Learning?
A risk-adjusted way to size a pharma media test budget, based on decision value and opportunity cost rather than a universal percentage.
The short answer
There is no correct universal percentage for a pharma media learning budget. The right amount depends on how much money the answer will influence, how uncertain you are today, and what it costs to run a test that can actually produce an answer. A brand spending $30 million on a channel it has never tested causally has a stronger case for a large learning reserve than a brand spending $2 million on a mature, well-measured plan.
Rules like "put 10% into testing" are easy to approve and easy to waste. The money gets spread across small tests that are too underpowered to change anything.
Size the budget from the decision backward
Start with the list of open questions, then work out what each answer is worth.
- Name the decision. "Should we move $3 million from display to CTV next year?" is a decision. "Learn about CTV" is not.
- Estimate the money at stake. How much budget will move depending on the answer?
- Estimate current uncertainty. If you are already fairly confident, a test adds little. If you honestly do not know, the test is worth more.
- Estimate the cost of a test that can answer it. Include working media, measurement fees, staff time, and the opportunity cost of holding out a control group.
- Compare. Fund tests where the value of a better decision clearly exceeds the full cost.
A simple value-of-information check
You do not need a formal model to apply the logic. Ask three questions for each proposed test:
| Question | If the answer is no |
|---|---|
| Will we act differently depending on the result? | Do not run the test |
| Can the test detect an effect of the size that would change our action? | Redesign or increase scale |
| Will the result arrive before the budget decision is made? | Change timing or pick a faster design |
A test that fails any of these is not learning. It is spend with a report attached. Power and sample size matter here. NIST's engineering statistics handbook is a plain reference for how interval width depends on sample size.
Where the learning money comes from
Learning budgets are rarely new money. They come from somewhere in the plan, and it helps to say where.
- Holdout groups cost reach, not cash. A 10% holdout on an HCP list means 10% of prescribers are not exposed. That is a real cost if media works, which is exactly why the test is worth running.
- New partner pilots usually displace an incumbent's budget. Treat the incumbent's forgone delivery as part of the test cost.
- Measurement fees are often the largest line item for small tests. A study that costs more than the media it measures needs a very clear decision to justify it.
Common mistakes
Too many small tests. Five $50,000 tests rarely produce five answers. One well-designed $250,000 test often produces one clear answer.
No stop rule. Decide in advance what result ends the test early, extends it, or scales it. See when to scale, hold, or stop a pilot.
Testing what is easy instead of what matters. Creative variants are easy to test. Channel incrementality is hard to test and usually moves far more money.
Hypothetical example
A brand is deciding whether to renew a $6 million commitment with its largest HCP partner. The team is genuinely unsure whether the partner drives incremental prescriptions or mostly reaches prescribers who would have written anyway. A randomized holdout on 15% of the target list, plus a measurement study, might cost the equivalent of $400,000 in forgone reach and fees. If the result could plausibly shift $2 million or more, the test is easy to justify. If leadership has already decided to renew regardless of outcome, it is not. These figures are illustrative.
Practical takeaway
Replace the fixed learning percentage with a short register: each open question, the decision it informs, the money at stake, the cost of a test that can answer it, and the date the answer is needed. Fund from the top of the list down until the next test is not worth its cost.
Sources
External guidance and platform documentation change. Links were current at publication; check them again before relying on them for a decision.
Editorial note. Analysis and frameworks are the author's own and do not represent Acxiom or any current or former employer, client, or named platform. Examples labeled hypothetical or illustrative are not results from real campaigns. Nothing here is legal, regulatory, or medical advice.
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