Expose device & browser speed bottlenecks — crowd-sourced leaderboards

Many digital product teams—roughly 1.5 million globally—struggle to prioritize performance work because regressions often affect only specific device/browser combinations amid rapid OS, browser, and silicon churn. Existing tooling tends to offer synthetic tests or per-app RUM dashboards, but lacks cross-team comparative context that would quickly reveal which devices or browsers are consistently worst for latency, errors, or conversion impact. You could build a crowd-sourced, privacy-preserving leaderboard platform that aggregates anonymized RUM telemetry from consenting teams to rank device and browser combos by key signals (latency, error rate, conversion delta). Deliver a lightweight SDK and serverless ingestion, expose sample traces and impact scores, support private team benchmarks, and provide automated alerts and remediation suggestions that map directly to engineering workflows. The market dynamics make this timely: the performance tooling and analytics market is roughly $6.0B (1.5M teams spending about $4,000/year), RUM adoption is growing, and developer-first SaaS patterns lower friction for adoption. Device and browser fragmentation continue to increase the value of comparative signals that help teams prioritize scarce engineering capacity toward fixes that move business metrics. To stand out you’ll need rigorous statistical methods for fair cross-team comparisons, strong privacy defaults (aggregation thresholds or differential privacy), and tight integrations into developer tooling to convert insights into work. Strengths include clear product-market fit and revenue potential, while the biggest challenges are reaching critical mass of contributors, ensuring data quality and trust in crowd-sourced signals, and managing privacy and vendor pushback—each of which requires focused technical, legal, and partnership effort.