“The downside is the base case minus 15%.”
A real downside changes the drivers: slower access, a later launch, a competitor arriving early. A uniform haircut hides where the risk actually lives.
Commercial forecasting for life sciences
Before you launch, scale, or transact, know what the forecast can really support.
I build, review, and red-team the commercial forecasts behind consequential life-sciences decisions: launches, sales-force moves, acquisitions, licensing deals, and portfolio bets.
You get a clear answer on what holds up, what does not, which assumptions move the answer, and where the decision flips, written so a board or deal committee can act on it.
$1B+ pharmaceutical acquisition modeling. Ten years in biostatistics and pharmaceutical analytics. Founder-led by Stephen McDaniel, with senior analytics work at Microsoft, Netflix, Oracle, Yahoo, Tableau, and SAS.
Illustrative commercial forecastSynthetic data
The question I answerWhich assumptions actually change the decision?
Where forecasts quietly break
Most forecasts I see are plausible. Fewer are ready to bet on.
These patterns show up again and again in life-sciences commercial models, including good ones built by smart teams.
“Europe borrows the US uptake curve.”
Access, launch sequence, and specialist concentration differ by market. Copying the curve tends to pull forward revenue that won’t arrive on schedule.
“Each product is forecast on its own.”
When products share a field force, accounts, and payers, they compete for the same capacity. Separate forecasts quietly count it twice.
“Nobody can say which assumption moved the number.”
If explaining the forecast means opening the model and hunting through tabs, it isn’t ready for the boardroom yet.
Check your own forecast with the seven-question readiness test
Launch. Scale. Transact.
Five decisions where forecast quality changes the outcome.
I keep the practice narrow on purpose. In these five decisions, demand, access, timing, competition, and field capacity interact enough that a simple base case can quietly mislead.
Launch
New Product Launch Modeling
Model demand by indication, geography, and channel before launch assumptions become commitments.
See how I approach thisScale
Sales Force Growth, Alignment & Rationalization
Put field capacity where the marginal return supports it, before headcount hardens into cost.
See how I approach thisTransact
M&A Evaluation
Test the commercial forecast before it becomes the number the valuation rests on.
See how I approach thisTransact
Licensing, BD&L, JV & Co-Commercialization
See how deal value moves when timing, uptake, or partner performance moves.
See how I approach thisScale
Portfolio & Lifecycle Capital Allocation
Put the next dollar behind the product, indication, or market that earns it.
See how I approach thisWays to work together
Start with the smallest scope that answers the decision.
Some decisions need a new model. Some need an independent read on the model already in hand. The highest-stakes decisions may deserve a fully independent second analytical path.
Validate
Forecast Integrity Review
Typical fee: $25K to $50K
An independent review of one important forecast tied to one decision, usually in 5 to 10 business days. You get a direct verdict: rely, repair, redesign, or stop.
See scope and deliverablesBuild
Forecast Modeling & Executive Scenarios
Scope: fixed before work begins
A driver-based model and scenario system built around your decision, with outputs leadership can test without breaking the logic.
- Driver architecture and data design
- Coherent scenarios and sensitivities
- Decision thresholds and executive outputs
- An interactive scenario app when it genuinely helps
Challenge
Forecast Red Team
Most engagements: $125K to $300K
I rebuild the critical economics on a separate analytical path, then show you exactly where and why my answer differs from the internal case. Scope depends on the decision, evidence base, model complexity, and degree of independent reconstruction required.
See when a Red Team fitsBuild your team’s capability
Private Forecasting Team Workshop
Onsite training in the techniques and model structures behind this work, using realistic life-sciences cases. Typically $30K to $50K.
Not sure which fits? Tell me about the decision. I’ll recommend the smallest scope that answers it, and I’ll tell you if you don’t need me at all.
How the work runs
Trace. Model. Challenge. Bound. Decide.
The goal isn’t a bigger spreadsheet. It’s a clear view of what drives the answer, how wide the honest range is, and where the decision would flip.
- 01Trace
Pin down the decision, the value drivers, the evidence, and who owns each assumption.
- 02Model
Build, or independently rebuild, the commercial mechanics that actually move the result.
- 03Challenge
Press on timing, access, adoption, competition, capacity, and the assumptions nobody wrote down.
- 04Bound
Build coherent scenarios and honest ranges tied to real drivers, not percentage haircuts.
- 05Decide
Turn it into thresholds, tradeoffs, and the signals to watch after you commit.
See the work
Judge the thinking before you pick up the phone.
Every example uses synthetic data built on realistic engagement patterns. Client work stays confidential.
Real Work, synthetic data
$1B life-sciences acquisition: repair before reliance.
A multi-product, multi-geography forecast that looks plausible but isn’t safe to rely on yet. See the verdict, the four findings behind it, and exactly what leadership would receive.
See the analysisFree self-check
Is your forecast ready for the boardroom?
Seven questions, about two minutes. Your answers stay in your browser and are never sent anywhere.
Take the readiness test
Who you’ll work with
You work directly with me.
I’m Stephen McDaniel. I build the models, run the challenge, and present the conclusions myself. No hand-off to a junior team.
My background starts with ten years in biostatistics and pharmaceutical analytics, including modeling for a $1B+ pharmaceutical acquisition and work supporting two drugs that became billion-dollar products. I later held senior analytics roles at Microsoft, Netflix, Oracle, Yahoo, Tableau, and SAS, and taught or presented through Princeton, Booth, Brown, the University of Washington, INFORMS, AMA, and TDWI.
I take on a small number of engagements at a time. That’s how I make sure the person you meet is the person doing the work.
Decision firstDriver-based modelsEvidence before narrative
Common questions
Good to know before we talk.
What should I send for the first conversation?
Nothing confidential. A few sentences on the decision, the timing, and what you need the forecast to tell you is plenty. Files only change hands after we’ve agreed scope, confidentiality, and secure handling in writing.
How long does an engagement take?
A Forecast Integrity Review usually takes 5 to 10 business days. A Red Team usually runs 2 to 6 weeks, depending on scope, evidence, and model complexity. New modeling work is scoped to the decision and its deadline.
Is the review truly independent?
Yes. My findings never depend on selling you follow-on work. I don’t resell software or take vendor commissions, and team training never changes the independence of a separately scoped review.
What if we don’t need a full engagement?
Then I’ll tell you. Sometimes the right answer is a smaller scope, a fix your own team can make, or no engagement at all.
Who do you usually work with?
Commercial, finance, BD&L, strategy, and corporate development leaders at biotech, pharma, med device, and diagnostics companies who are facing a consequential commercial decision.
Can leadership use the model themselves?
Yes. Where it genuinely helps, I deliver an interactive executive scenario system so leadership can test approved inputs without breaking the model’s logic or version control.
Next step
Have a decision coming up? Let’s talk it through.
Send me a few non-confidential sentences about the decision and when it lands. I’ll reply personally with an honest read on whether I can help, and the smallest scope that would.