Scenario planning for launch

We talk a lot about uncertainty in launch – but frequently, teams focus on one future. Forecasts are locked, the strategy is developed and plans are built, all implicitly assuming a single data outcome, a single label, a single access reality. Then pivotal data read out, or the label language lands, and the rework begins, rewriting the story, the value proposition and the launch plan.

At its core scenario planning is about accepting and making uncertainty work for you instead of against you

Good scenario planning does three things:

• Protects investment by avoiding wasteful re‑work and last‑minute scrambles

• Sharpens strategy by testing whether your choices still hold under different plausible futures

• Aligns stakeholders by giving everyone a shared, realistic view of what might happen and what you’ll do in each case

Why scenarios belong in Phase 2

In late Phase 2, your asset sits at a critical point: you’re starting to commit real money and organisational attention to launch, but key uncertainties on data, label and access remain.

Scenario planning forces you to surface those uncertainties, decide which ones matter, and build a plan that can flex or be refined once the pivotal data and label arrive, avoiding significant rework or rebuilt from scratch. Additionally, earlier scenario planning is even more important for companies managing overlapping launches with limited resources and therefore cannot afford to rebuild strategy at each data milestone.

Core scenarios to model

Effective scenario planning is not about inventing multiple hypothetical worlds. It is about focusing on the uncertainties that matter most for winning at launch. In late Phase 2, three dimensions typically dominate: data strength, label outcome, and market access/timing.

Dimension 1
Data strength versus TPP

Here we explore how close will the pivotal data land to the TPP and typically, construct two scenarios:

• A “meets or exceeds TPP”, where key efficacy and safety endpoints are achieved or surpassed, supporting the positioning and claims you have designed your strategy around

• A “technically positive but weaker” scenario, where primary endpoints are met but with smaller effect sizes, less consistency across subgroups and /or signals in safety or tolerability that require careful handling

For each scenario we examine ‘if this is the data we get, what must change in our value story, segmentation, positioning, and launch priorities? Where does the brand still win, and where do we need to narrow focus.’

Dimension 2
Label outcome versus expectations

This is where we must be explicit about the expected label and then model deviations from it (narrower and broader versus aligned) because label language can change the claims you can make, the audience you can target, and the value story you can defend that would matter commercially. Consider scenarios such as:

• Label aligned with expectations: Indication, line of therapy and key claims broadly match the TPP.

• Label narrower or more constrained than expected: Restricted to a subset population (e.g. biomarker‑positive only, later‑line use); More conservative wording around endpoints or benefits (e.g. no explicit quality‑of‑life claim); additional risk language or requirements (e.g. monitoring, REMS, contraindications).

• Label broader or differentiated in an unexpected way: Additional sub‑populations, earlier lines, or co‑morbid groups included; Extra claims (e.g. specific symptom improvement, certain health‑economic outcomes) that create new opportunities.

For each label scenario, we define what would shift in: the core value proposition and proof points; target patient and prescriber prioritisation and /or required evidence and real‑world data to support or extend the label. This avoids the post‑approval “scramble” when the label lands and suddenly nobody is sure whether the previously agreed strategy still holds.

Dimension 3
Market access and timing

Often the biggest driver of real‑world impact. Even with strong data and a good label, launch success depends on how quickly and broadly payers, HTA bodies and institutions recognise and reward the value of your product. Here, we anchor the thinking on the few critical factors that determine early access and uptake in the therapy area. For example: burden of disease and unmet need in payer and HTA narratives; comparative effectiveness versus current standard of care; budget impact and resource use (hospitalisations, procedures, monitoring) and presence or absence of compelling sub‑population stories (e.g. high‑risk groups). Frequently we examine 2 scenarios which play out very differently in fast-uptake vs. slow-uptake markets.

Favourable access & on‑time approvals

• Pricing and reimbursement align broadly with expectations.

• Time from regulatory approval to reimbursement decision is within historical norms.

• Early access mechanisms or interim funding are possible where relevant.

Challenging access & delayed approvals

• Additional evidence or economic analyses requested, delaying full access.

• More restrictive reimbursement criteria (e.g. step‑through, prior authorisation).

• Conflicting HTA outcomes across major markets.

For each, scenario we map out the consequences: which launch activities have to change sequence or intensity?; where do we need contingency plans (e.g. more emphasis on earlier‑line evidence, real‑world data, or sub‑population value stories)? and what does this mean for forecast shape and resource phasing?

Plan once, refine twice

The power of scenario planning in late Phase 2 is that you only build one coherent launch plan – and then refine it twice at key data milestones. This keeps the work structured and reusable, instead of starting from scratch every time something changes.

Step 1
Build the initial launch plan and assumptions in late Phase 2

Take your three scenario dimensions and define a single, integrated launch strategy that is robust across the most likely combinations of scenarios. This becomes your “spine” – the core value story, segmentation, positioning and strategic choices that you expect to hold.

Make assumptions explicit and link them to scenarios. Identify scenario‑sensitive elements of your plan: claims and messaging that rely on specific data thresholds; tactics contingent on breadth of label or specific sub‑groups and access and pricing strategies that hinge on HTA expectations.

The output is a lean but coherent late Phase 2 launch plan with a visible “assumption spine”: everyone can see which parts are rock‑solid and which depend on how data, label and access land.

Step 2
Refine when pivotal data are available. Revisit your data strength vs TPP scenarios and decide which one (or combination) you are now in

Update only the components of the plan that are directly impacted. For example, re‑rate the credibility and strength of your main value claims; adjust prioritisation of patient segments, prescribers and messages if effect sizes or subgroup patterns differ from expectations. And / or confirm whether additional evidence generation or sub‑analyses are now critical pre‑launch.

The discussion should be very focused, because the assumptions were explicit. You retain most of the spine and refine only what is needed.

Step 3
Refine again when label is confirmed. Map your plan against your label outcome scenarios and access expectations

Confirm where your planned narrative and claims are fully supported, where they need tightening, and where new opportunities or constraints appear.

Consider if/where adjustments are required for example, positioning language and proof points to be strictly label‑compliant; market access plans to reflect any restrictions or special conditions and / or training and launch materials to reflect the final, compliant story.

Again, you are refining the same underlying launch plan – not inventing a new one. The team’s mental model of the brand, and the evidence behind it, evolves coherently across Phase 2, Phase 3 and pre‑launch.

Keeping it lean

A common fear is that scenario planning will explode workload, not reduce it. It doesn’t have to. In our work we find that done well, it is a way to sharpen choices and avoid spinning cycles on low‑value “busy work”.

Top Tips! A lean approach to scenario planning in late Phase 2

• Work with a small number of impactful scenarios – typically 2–3 per dimension, not every theoretical variation.

• Focus on decision‑critical differences, not on cosmetic details. If two scenarios would drive the same launch strategy, they do not need separate plans.

• Use simple tools and visuals: clearly showing what changes and what stays the same

• Integrate into existing processes (e.g. TPP reviews, annual brand planning, launch readiness) rather than adding a parallel track of work.

The aim is not to predict the future perfectly. The aim is to make uncertainty explicit, test your strategy against it, and be ready to adapt with minimal re‑work when critical information arrives. Teams that built explicit assumptions can course-correct faster because they already know which information or signals matter, what it mean and what to do next

Can we help you?

Delphine leads our Life Sciences team, reach out to her to talk about creating a launch plan that sticks.

Contact Delphine