Track user intent + goal-abandonment to explain why conversions fail

Executive Summary

Many product-led and digital businesses—roughly 2,000,000 companies at an average of $6,000/year in analytics/CRO spend—struggle to answer not just what conversion rates are, but why users abandon goals mid-flow; PMs, growth, CRO and UX teams waste time on manual session replays and disconnected metrics that rarely surface intent. The result is stalled experimentation, mis-prioritized fixes, and revenue leakage that teams cannot reliably attribute to specific user intentions. You could build a privacy-first analytics layer that combines first-party telemetry and optional server-side events with an AI-driven intent classifier and goal-abandonment taxonomy to auto-summarize why journeys fail at scale. Core features would include deterministic instrumentation, automated root-cause hypotheses (with confidence scores), cohort-level intent funnels, and one-click experiment suggestions, plus connectors to existing CDPs and experiment platforms to drive action. This market is attractive now: a $12.0B addressable market, migration away from third-party cookies to first-party telemetry, and maturing AI summarization (Market Score 92/100; Revenue Potential 88/100) create a real window to sell new types of value—actionable “why” rather than raw “what.” At the same time, competition is medium and challenges are real: building reliable intent models requires quality labels, explainability to earn trust, and engineering to minimize infrastructure costs and integration friction. To stand out you need demonstrable accuracy and explainability (e.g., precision/recall benchmarks and human-review workflows), turnkey integrations that replace time-consuming instrumentation, and pricing tied to realized uplift so buyers can justify spend; the upside is clear ROI through faster experiments and reduced churn, but success will demand early case studies and tight collaboration with privacy and data teams.