Johnson & Johnson · Tremfya market launch
We didn’t need the whole market. We needed the part of it most likely to move.
For the Tremfya launch, I used market and commercial data to find where focused effort had the best shot at early traction.
Some models here are illustrative or reconstructed. Reported outcomes are labeled separately from my analysis.
Problem
My role
Key decision
Business value
Start focused. Learn quickly. Expand what works.
Don’t treat every market the same. Start where volume, prescribing frequency, and expected conversion line up. Measure what happens, then expand if the results hold up.
Find the strongest opportunities
Focus resources
Measure conversion
Expand successful routes
What the team could actually use
The analysis only mattered if someone could act on it. I turned the model into opportunity maps, priority segments, funnel views, and forecast scenarios so stakeholders could see where the recommendation came from.
I analyzed
Market, territory, volume, frequency, conversion, and competitive data.
I built
The prioritization model, conversion funnel, forecast scenarios, and stakeholder visuals.
I influenced
Where the team focused first, where coverage got pulled back, and how the launch could expand once it learned more.
I supported the analytics and recommendation. I didn’t make the national commercial decisions on my own.
The model narrowed the market to the opportunities worth testing first
These reconstructed views show the logic, not Johnson & Johnson’s actual numbers.
- Total market
No allocation decision yet — sizing only.
- Targetable market
Exclude accounts we cannot reach or serve.
- High-volume segments
Concentrate where prescribing volume exists.
- High-frequency opportunities
Favour repeat prescribing over one-offs.
- High-confidence targets
Rank by opportunity score; fund the top band.
- Expected conversion
Apply historical conversion by segment.
- Expected volume
Translate conversion into expected demand.
- Market share
Compare against the coverage-first alternative.
Each filter was a resource-allocation decision.
Expected volume index by scenario
Low case
- ~1,200
- 34 (index)
- Low single-digit early share
Base case
- ~1,600
- 56 (index)
- Mid single-digit early share
Upside case
- ~1,900
- 76 (index)
- High single-digit early share
The results were strong. They weren’t mine alone.
The broader launch reached more than 30% market share within six months, and around 85% longer term. My analysis was one part of a much larger national effort, so these are reported project outcomes, not a claim of individual credit.
>30%
Market share within six months
~85%
Market share, longer term
Read the full analysis
The recommendation came first. This is the work behind it.
Discovery and regional input
- Sales leadership
- Regional teams
- Territory data
- Commercial assumptions
- National launch model
We gathered input from sales leaders and regional teams across the U.S. I wanted the model grounded in territory-level reality, not a single national average.
- Prescribing patterns
- Competitive intensity
- Territory opportunity
- Existing commercial routes
- Expected conversion
- Volume potential
Market segmentation
Broad coverage
More accounts, more spend, including lower-confidence bets.
Focused entry
Prioritize volume, frequency, and expected conversion. Prove it, then expand it.
Opportunity scoring
Inputs
- Market volume
- Prescription frequency
- Historical conversion
- Competitive intensity
- Regional opportunity
- Commercial coverage
- Account / territory characteristics
Prioritization model
I combined volume, frequency, historical conversion, competitive intensity, and strategic fit so territories could be compared consistently.
Outputs
- Priority targets
- Expected conversion
- Expected volume
- Resource requirement
- Forecast market impact
Funnel design
Each stage cut weaker opportunities and made the allocation call explicit. The goal wasn’t a prettier funnel. It was a smaller, more useful starting set.
- Total market
No allocation decision yet — sizing only.
- Targetable market
Exclude accounts we cannot reach or serve.
- High-volume segments
Concentrate where prescribing volume exists.
- High-frequency opportunities
Favour repeat prescribing over one-offs.
- High-confidence targets
Rank by opportunity score; fund the top band.
- Expected conversion
Apply historical conversion by segment.
- Expected volume
Translate conversion into expected demand.
- Market share
Compare against the coverage-first alternative.
Each filter was a resource-allocation decision.
Forecast scenarios
I used low, base, and upside scenarios to connect target count, expected conversion, and volume. Exact internal forecast data isn’t shown.
Expected volume index by scenario
Low case
- ~1,200
- 34 (index)
- Low single-digit early share
Base case
- ~1,600
- 56 (index)
- Mid single-digit early share
Upside case
- ~1,900
- 76 (index)
- High single-digit early share
Resource tradeoffs
Traditional route model
25%
- High number of targets
- Lower confidence per target
- Higher resource requirement
Focused route model
35–40%
- Fewer initial targets
- Higher expected confidence
- Higher expected volume per commercial resource
These aren’t measured J&J conversion rates. They show the tradeoff between broad coverage and a more focused starting point.
Stakeholder alignment
- Data
- Model
- Visuals
- Discussion
- Decision
I turned the analysis into a story regional and national stakeholders could respond to. Their questions helped pressure-test the assumptions before the recommendation moved forward.
What I learned
Start with the decision, not the dashboard.
Make tradeoffs visible so stakeholders can challenge them.
Treat early results as evidence for what to expand next.
Segment before you scale. Not every opportunity deserves the same investment.
Analytics should change a decision. If the allocation stays the same, the analysis didn’t do enough.
Clear communication is part of the product. A model only matters if people know what to do with it.