SECTOR / PHARMA

Find the launch story that grows peak share.

For launch strategy and commercial teams: test positioning, access, and support decisions before the budget locks. Subconscious turns one plausible launch story into thousands of measured experiments on simulated prescribers, payers, and patients.

2 in 3

New drugs often miss prelaunch expectations. The molecule is only part of the bet. The money is in knowing which story, access path, and support model changes behavior.

Source: McKinsey, The secret of successful drug launches

THE DECISION

Old world

Put access and copay support behind 500,000 patients at $100 each because the team cannot tell who needs it. Total potential cost: $50M, including support for patients who would have started and stayed on therapy anyway.

New world

Identify the 30% movable segment and place the support only where it is expected to change initiation and adherence. Modeled program cost falls to $15M, 70% less, without lowering the addressable patient opportunity.

Stop funding the patients who were already going to start.

How the figure is built

Eligible patients
500,000
Support value per patient
$100
Share whose behavior the support changes
30%

500,000 × $100 = $50M untargeted, versus 150,000 × $100 = $15M targeted

Modeled economics on a representative decision, not customer results. Audited customer records are at subconscious.ai/case-studies.

THE TIMING SHIFT

Today
  1. Evidence
  2. Positioning
  3. Access
  4. Readiness
  5. Launch
Subconscious
  1. Evidence
  2. Positioning
  3. Access
  4. Readiness
  5. Launch
Current launch research arrives after the story is already heavy. Subconscious tests the story while it can still change.

CAUSAL OPERATING LOOP

KPI shift

More launch upside, before the plan hardens.

Peak share

The launch story hardens before behavior is tested.

Positioning, access, and support ranked by causal lift.

Access risk

Payer friction shows up after field plans are locked.

Access tradeoffs are tested before the budget commits.

Review confidence

Claims are defended after the story is chosen.

Every recommendation carries cohort, interval, and trail.

Relative decision leverage. Every read ships with cohort, confidence interval, and experiment trail.
Pharma causal decision graphPrescriber and access context confound the relationship between launch decisions and prescribing, persistence, and peak share. Subconscious isolates the causal effect.Umarket contextunobserveddo(S)positioning / access / supportYRx / persistence / share
Grey arrows are the assumptions hiding in the launch plan. The red arrow is the causal effect of positioning, access, and support on prescribing, persistence, and peak share.

ONE CAUSAL ANSWER

Which story makes prescribers choose you when the market gets crowded?

Walk into launch readiness with the territories, access moves, and support offers ranked by expected lift. Strategy gets the causal answer, not another round of consensus.

MODEL-PREDICTED

Predicted first, then measured.

The method is validated against published randomized trials, blind — the standard of evidence a clinical audience already applies to everything else.

Predicted
Outcomes for published randomized controlled trials, generated blind — without sight of what each trial actually found.
Observed
The real human outcomes those trials reported.
Agreement
0.73 mean Spearman correlation across 43 well-designed replications, peaking at 0.93 in health and policy, across 300 published papers.

Read the audited record: Nº 05 Methodology validation

PROOF

93%

validated against human outcomes

350+

replicated human studies

5 min

to run the next decision

SOC 2

enterprise-ready deployment