opportunity-assessment

Synthesize diverse research into a prioritized opportunity map by clustering insights across market, competitor, and user research. It enforces explicit data sources and evidence levels to guide product strategy with structured scoring matrices.

Prioritizing product opportunities requires clustering multi-source research insights and applying a scoring matrix with evidence levels. This process generates a structured opportunity map with clear keep or kill decisions and human-friendly review notes.

Cluster and score research insights by feeding upstream market and user research outputs into a structured workflow. The system extracts insights, groups them into five or more opportunity directions, and applies cross-validation to form traceable decision records.

Automated opportunity assessment supports cross-validation by enforcing explicit evidence levels on multi-source research inputs. It produces a templated output with scoring matrices and elimination records, ensuring human PM decisions are fully traceable and evidence-based.

Generating a structured opportunity map requires upstream research outputs from market, competitor, and user research. Providing these diverse data sources allows the system to extract insights, enforce evidence levels, and output prioritized directions for review.

Opportunity prioritization requires explicit evidence levels and data sources to ensure cross-validation and traceable decision records. This constraint guarantees that clustered insights and keep or kill decisions are grounded in verified research rather than assumptions.