Measure a Bluesky handle's like frequency (per min/hr/day) via public data

Executive Summary

Brands, agencies, and moderation teams currently lack reliable, per-handle engagement rates (likes per minute/hour/day) on emerging decentralized platforms like Bluesky because public data is fragmented, streaming endpoints are inconsistent, and native analytics are limited or nonexistent. That gap prevents real-time moderation, influencer evaluation, and campaign optimization at the cadence modern teams expect. You could build a privacy-respecting analytics engine that computes likes-per-min/hr/day for Bluesky handles using only public endpoints, distributed indexers, and probabilistic rate-estimation techniques, exposing near-real-time dashboards, an API, and normalized per-1,000-follower benchmarks. Position the product as an enterprise-grade plugin for existing social monitoring stacks with exportable data for reporting and moderation workflows. The timing is favorable: decentralized-social networks and the Fediverse are growing, driving demand for platform-specific measurement tools, and the enterprise social analytics market is large—about $10.0B using a 500,000 brands/agencies × $20K ACV assumption; the opportunity has a market score of 78/100 and a revenue potential of 90/100 while competition remains low. To stand out you’ll need engineering rigor (low-latency collectors, transparent sampling/error bars), a clear privacy-first stance, and early validation partnerships with measurement vendors or Bluesky aggregators. Be honest about challenges: platform policy changes, rate limits, and data gaps from decentralization will force conservative SLAs and continuous validation; mitigate these with an open validation playbook and a pivot path to aggregated or inferred metrics if direct counts become restricted.