How to turn your data into content with PostHog

PostHog describes how it produced a first-party data study after earlier attempts stalled because collecting, validating, analyzing, and presenting product data seemed too resource-intensive. The team narrowed its ambitions to a practical minimum viable study and used improved internal tools, including PostHog AI, an MCP integration, notebooks, and a Slack bot, to reduce manual data work and focus on editorial decisions. The author selected Session Replay and Replay Vision as the topic, used AI to identify viable data angles and generate SQL queries across the prior 90 days, gathered product context from internal documentation and pull requests, and drafted the study around metrics such as replay viewing behavior, recording duration, capture rates, and playback speeds. Claude and the MCP were then used to rerun queries, validate results, and explore visualization options before the graphics team finalized the charts. The process took a few days, demonstrating that modern AI-assisted analytics tools can make first-party research studies more feasible for teams with limited time.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.