A decision map the platform was scoped against
Four pillars and four development stages explain what a drug program must prove before human testing. Built from regulatory guidance first, then stress-tested with scientific advisory board members.
Account teams used its value case in a seven-figure renewal that closed with expansion; product scoped the next-generation platform against it; go-to-market standardized on its outcome labels.
- Role
- Product Marketing Manager, Platform Solutions
- Timeline
- 4 months, research to first adoption
- Product
- Enterprise B2B SaaS Platform
- Worked with
- Product and engineering teams, in-house scientists and subject matter experts, scientific liaisons, account teams, former VP-level pharma executives
- The problemProduct and go-to-market defined preclinical R&D success differently, and neither definition came from the customer.
- The solutionA map of what customers decide at each R&D stage, built from regulatory guidance and stress-tested with former VP-level pharma executives.
- The resultAccount teams used its value case in a seven-figure renewal that closed with expansion; product scoped the next-generation platform against it; go-to-market standardized on its outcome labels.
Challenge
Account teams carried renewal and expansion targets, but go-to-market and product defined success in preclinical R&D differently. The differences were subtle, but significant enough that a positioning claim based on one definition wouldn't resonate with someone using the other. My goal was to align our terminology and recognize that neither definition reflected the customer's perspective; both were internal.
What decisions does a preclinical drug discovery company make at each R&D stage, and where is market intelligence most relevant?
To answer this, I mapped the customer's decisions rather than focusing on our product's capabilities.
Strategy
Establish the baseline
Structured research across regulatory guidance, scientific literature, and competitor claims produced an initial taxonomy of desired outcomes in preclinical R&D, developed before bringing in internal perspectives.
Key Outputs
- Non-linear preclinical phases: Ideation and discovery, Early optimization, Late optimization, IND submission readiness
- Outcome pillars: Strategic value, Foundational evidence and scientific due diligence, Experimental validation
- Decision criteria → Desired outcome architecture
Validate against internal expertise.
Working sessions with in-house scientists and subject matter experts revealed where regulatory standards and practical realities diverged.
Key Outputs
- Additional outcome pillar: Actionable insights & operational decisions
- Clinical translation embedded within each pillar
Stress-test with real-world executives
Interviews with former VP-level pharmaceutical executives tested the framework's phase boundaries and confirmed it held up under real-world decision-making.
Key Outputs
- Embedding of patient-centricity into the strategic value pillar → Strategic and patient-centric value pillar
- Outcomes dedicated to evidence integration
Ship it as a reference model.
The final framework included definitions, phase criteria, and evidence notes, enabling teams to use it directly in roadmap reviews, sales strategies, and feedback collection.
Key Outputs
- A framework built for intentional use with AI tools: Criterion ID codes, standardized terminology, defined variables and guardrails.
The build
A set of three inclusion rules informed the criteria:
It names a state, not an activity.
Details
Every criterion is written as a condition that's already true: defined, quantified, documented, confirmed, characterized, established, demonstrated. None of them name work to be done. The criterion is the proof, never the task that produced it.
It can come back negative.
Details
Freedom to operate can be unfavourable. A safety margin might not support the proposed first-in-human dose. Efficacy might not replicate across models. A statement that can't fail is a fact, and facts don't belong at a gate.
It's owned at the stage where the answer is first required, not the stage where it first becomes available.
Details
This keeps the framework stage-specific rather than turning it into a checklist.
Methodology notes
Uncertainty counts as a satisfying condition. Several criteria are met by documenting evidence quality, known gaps, the limits of a model's relevance, or the boundary conditions under which a causal claim holds. Knowing exactly what you do not know clears the gate.
The exclusion rule follows from Rule 2. Anything that could not come back negative described something true about the preclinical program, rather than something the program had to decide.
Impact
Reference model
Adopted for the next-generation platform build
Renewal with expansion
A seven-figure enterprise renewal built on the framework's strategic-value case
Shared language
One vocabulary across product, sales, and customer teams
Product and Engineering teams used the framework to scope the next-generation platform, aligning capabilities with outcomes instead of feature parity.
Account teams struggled to make the case for the platform's strategic value at renewal. The framework's strategic and patient-centric value pillar gave them that case, and I turned it into QBR slides, talking points, and objection handling for an enterprise renewal that closed with expansion.
Go-to-market teams adopted the same outcome labels for customer feedback, aligning product and field conversations.
Personal takeaway
Driving adoption required ongoing effort. I led enablement sessions, integrated the framework into existing AI tools, and built relationships across product and go-to-market teams that documentation alone couldn't achieve.
If I did this again, I'd prioritize building the adoption path first and let it guide the framework's design. A less perfect model that's actively used is more valuable than a superior one that remains unused.
Deliverable
One desired outcome, opened to its decision criteria
- 4 PillarsFoundational evidence & scientific due diligence1 of 4
- 18 CategoriesClinical translatability prediction1 of 5 in this pillar
- Stage-gated outcomesPredicted clinical effect size & success probabilityStage 2 of 4 · Early optimization
- 88 Decision criteria7 decision criteriaListed in full below
I chose this path because it makes the framework's differentiator visible: each criterion names a scientifically defensible evidentiary state, while the hierarchy lets teams trace a specific research decision to the category, stage, and business outcome it serves.