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Case Study · TPP

How AI Powered TPP Testing Called the Market Before the Research Confirmed It

At a Glance

Client
Global Top-10 Pharma
Research Type
AI-Powered TPP Testing & Predictive Synthesis
Audience
Specialist Physicians
Geography
Global (US, Europe, Japan)
Phase
Pre-Launch, Portfolio Planning
Deliverable
Stellaverse-Powered TPP Prediction Model

The Business Challenge

A global oncology portfolio team was advancing three pipeline assets into a crowded, fast-evolving treatment category, each with a distinct — and still-blinded — target product profile. Before committing to a full multi-market fielding program, the team needed an early, credible read on how physicians would actually react to each profile and how adoption might vary across specialties. The stakes were real: a costly, multi-market research program was riding on getting the right questions in front of the right physicians the first time.

Context

  • Three blinded target product profiles advancing within the same fast-evolving oncology category
  • A full multi-market primary fielding program had not yet been committed
  • Prior primary research existed across several earlier studies but had not been synthesized into a forward-looking view
  • Portfolio decisions carried significant cost and timeline risk if the wrong questions were fielded
  • No physician-facing data yet existed comparing how the three profiles would be received

The S+R Approach

Rather than start from a blank page, S+R used clients’ prior primary research, then added our Stellaverse™, a highly curated view of the entire therapeutic area, spanning physician demand, opportunity assessment, and market context — alongside broader clinical, competitive, and digital-conversation intelligence — to answer the question in-depth with credentialed data to enable client commercial teams to make a very confident go-decision. Stella used this foundation to generate structured, forward-looking hypotheses on how physicians would evaluate each of the three blinded target product profiles against each other and the competitive landscape, including where specialty-level differences in physician engagement might shape adoption. This was synthesis and prediction only — no new primary interviews had yet been fielded. The approach let the team pressure-test its portfolio thinking in days rather than the months a traditional qualitative-first design would have required, while preserving the option to validate directionally with real physicians before finalizing strategy.

Research Objectives

  • 01

    Determine how physicians would likely differentiate among the three blinded target product profiles

  • 02

    Identify which patient scenarios and treatment contexts each profile was best positioned to address

  • 03

    Assess whether specialty-level differences in physician engagement would shape future adoption patterns

Insights Delivered

Key Findings

  • The AI-generated synthesis produced clear, differentiated hypotheses across all three target product profiles

  • Distinct adoption patterns emerged among the profiles, shaped by differences in clinical and practical positioning

  • Specialty-level differences in physician engagement surfaced as a meaningful factor in future adoption

  • The analysis identified where each profile was likely to compete most directly against existing treatment approaches

  • When benchmarked against the primary research that followed, the AI-generated hypotheses scored approximately 4.5 out of 5 on directional accuracy

Strategic Impact

When the client subsequently fielded independent qualitative and quantitative research with physicians across multiple global markets, the AI-generated hypotheses held up — scoring approximately 4.5 out of 5 in a post-project accuracy comparison against the primary findings. That gave the team confidence in its portfolio thesis before a single new interview had taken place. Rather than redirecting the client’s strategy, the primary research largely confirmed and refined it, validating that the AI-powered synthesis phase had given the team a reliable, quantifiably accurate head start on planning, resourcing, and investment-committee-readiness conversations.

Key Deliverables

  • Stellaverse-powered TPP prediction model synthesizing the client's existing primary research

  • Forward-looking adoption hypotheses across three blinded target product profiles

  • Specialty-level physician engagement analysis

  • A directional validation checkpoint ahead of primary fielding

TPP testing decisions carry real portfolio risk when they happen too late or too slowly. Talk to us about how AI-powered synthesis can give your team a validated head start before you commit to a full fielding program.

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