Executive Summary

Brands, agencies, and social analytics vendors increasingly lack reliable, platform-specific engagement signals on emergent networks like Bluesky; they need to know not just raw follower or like counts but the tempo of engagement — how often a handle receives likes per minute/hour — to allocate ad spend, detect virality, and validate influencer behaviour. This problem is acute for enterprise teams and media agencies that manage campaigns across 90,000 potential customers in the addressable market, where platform diversification forces them to measure performance on channels that aren’t yet supported by legacy suites. You could build a privacy-preserving analytics product that harvests public likes over time and converts them into a normalized like-frequency metric per handle, exposing dashboards, real-time alerts, and a developer API with minute- and hour-level granularity. Implementation would focus on aggregated, non-content-based signals to avoid PII and content scraping, using respectful rate-limited polling, resilient deduplication to mitigate bot noise, and statistical smoothing to handle missing samples. An enterprise-tier offering (target ACV $80K) would bundle SLAs, historical baselines, and integration connectors for existing social analytics platforms. The timing is favorable: we estimate a $7.2B addressable market (90,000 enterprises x $80K ACV), a market score of 88/100, and strong trends toward platform diversification, privacy-preserving analytics, and real-time monitoring that collectively raise willingness to pay. Competition is low today, which reduces go-to-market friction, but the revenue potential score of 76/100 reflects that enterprise sales cycles and integration complexity will slow monetization. This product can stand out by delivering a single, verifiable engagement tempo metric with transparent methodology, enterprise SLAs, and privacy guarantees, rather than attempting full content-level listening. Challenges are real: API constraints, sampling bias, bot-driven noise, and the risk that larger analytics incumbents extend to Bluesky quickly; a clear validation plan and early enterprise pilots will be essential to prove signal quality before scaling.

Market Opportunity

Track a Bluesky handle’s like-frequency by harvesting public likes over time targets a $7.2B = 90,000 enterprises (agencies/brands/data vendors) x $80K ACV (enterprise social analytics + listening suites) total addressable market with low saturation and a year-over-year growth rate of 18% CAGR (social listening & influencer analytics growth as new channels emerge).

Key trends driving demand: Platform diversification -- brands shift budgets to emerging platforms (creating demand for platform-specific metrics).; Privacy-preserving analytics -- demand for aggregate metrics that avoid content-level scraping or PII exposure.; Real-time monitoring -- advertisers and ops teams need minute/hour-level signals to respond quickly to trends.; AI-assisted behavioral signals -- lightweight ML to surface abnormal liking patterns and classify engagement types..

Key competitors include Brandwatch (Cision), Sprout Social, Social Blade, DIY / Open-source scripts (community tools).

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Track a Bluesky handle’s like-frequency by harvesting public likes over time

Executive Summary

Market Validation

Market Opportunity

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