Founders miss why users leave — identify user intent, not vanity metrics

Founders and product teams at roughly 200,000 product-driven companies routinely misdiagnose churn because they look at vanity metrics (DAU, funnel drop-offs) rather than per-user intent signals that explain why people leave, so they spend weeks on qualitative research without clear, scalable answers. The result is wasted product cycles and missed growth opportunities that are hard to justify to boards or investors. You could build an AI-first analytics layer that converts raw event streams and session replays into concise, per-user intent narratives and intent cohorts, surfaces causal hypotheses tied to conversion metrics, and plugs into first-party identity systems for actionable downstream automation. The core product would be LLM-driven summarization, deterministic intent-labeling rules, and prioritization workflows that hand off to product managers and growth teams. This market looks timely: TAM ~ $8.0B (200,000 companies × $40K ACV), a market score of 92/100 and revenue potential 88/100 reflect strong willingness to pay as companies double down on product-led growth and need differentiated usage insights. AI summarization lowers analysis time, product-led adoption raises demand for per-user intent tied to conversion, and privacy/ID shifts make first-party, consented intent data more valuable. To stand out you must pair high-precision, auditable intent extraction with privacy-by-design architecture and clear human-in-the-loop workflows to mitigate LLM hallucination and demonstrate causality rather than correlation; strengths will be speed and actionability, while challenges include instrumentation burden, cost of large-scale replay processing, and competing against established analytics incumbents in a medium-competition field.