Testing my content metrics based on my January 2026 numbers with the new Count AI Agent. скачать в хорошем качестве

Testing my content metrics based on my January 2026 numbers with the new Count AI Agent.

How to Track Content Performance with Metrics, BigQuery Data, and AI In this episode of “How can I actually track my content performance?”, the presenter shows a practical setup for thinking about content metrics, insights, and how to use AI on top of real data rather than promising automatic growth. They recap using a metric tree to move from isolated numbers (like website sessions) to more useful breakdowns by channel (LinkedIn, YouTube, SEO, AI) and ultimately conversions such as newsletter signups. The workflow shifts into implementation: connecting a BigQuery instance to Count, importing three historically-separated metric tables (signals, anon analytics, and product analytics), and noting that combining them into one date-grained table would simplify analysis since identity resolution and richer dimensions aren’t in place yet. The presenter demonstrates classic querying and visualization in Count (including fixing an apparent anomaly caused by a monthly vs daily grain), and explains why incomplete historical coverage across metrics can distort analysis. They then enable and use Count’s AI agent, add workspace context, and use voice prompting to brainstorm operational dashboard ideas and identify January 2026 patterns. The AI produces analysis assets including XMR charts, a funnel-like view (with caveats due to metric definitions), and highlights insights such as an upper-control breach on January 5, Monday outperforming and weekends being lower (aligned with reduced LinkedIn posting), and a tracking outage that appears as a data drop. The episode concludes with planned next steps: consolidate tables, define metrics in a catalog and add better semantic context, then do a deeper dive by cross-referencing spikes (like January 5) with LinkedIn posts, YouTube videos, and newsletters to understand what content drives traffic and conversions. 00:00 Metrics Not Magic 02:53 Connecting BigQuery Data 03:16 Why Three Tables 05:50 Classic Query Workflow 09:57 Switching To AI Brainstorming 10:44 Enabling AI And Context 12:14 Prompting The Agent 17:25 How The Agent Analyzes 18:32 XMR Charts Explained 23:51 Insights From January 27:08 Fixing Metrics With Catalog 30:14 What To Improve Next 32:25 Wrap Up And Next Video