Reading Your Analytics with AI
Paste your YouTube Studio export and ask AI to find patterns across your best and worst performing videos — topic, length, hook style, and thumbnail pattern..
The prompt
Here is a table of my last 15 videos with views, average view duration %, and CTR: [paste data]. Identify patterns: what do my top 3 videos have in common (topic, length, hook style)? What do my bottom 3 have in common? Give me one concrete change to try next.
What to replace
Swap these placeholders for your own details before running the prompt:
[paste data]your own value
Pro tip: Look for patterns across at least 10-15 videos — analyzing 2-3 videos leads to overfitting on noise, not a real signal.
How to use this prompt
- Copy the prompt using the button above.
- Replace [paste data] with your own details — the more specific you are, the better the output.
- Paste it into ChatGPT and run it.
- If the answer feels generic, add constraints: audience, length, tone, and what to avoid. That single change fixes most weak output.
Learn the technique
Reading Your Analytics with AI
Module 3 — Channel Strategy · AI YouTube Management
Related prompts
Hook-First Script Structure
The first 5-10 seconds decide retention.
ClaudeTitle & Thumbnail Text Pairing — Claude
Titles and thumbnail text should never repeat the same words — they should work together to create curiosity.
PerplexityTitle & Thumbnail Text Pairing — Perplexity
Titles and thumbnail text should never repeat the same words — they should work together to create curiosity.
ChatGPTScripting for the Retention Graph
Retention drops happen at predictable points: the 30-second mark (promise not yet paid), mid-video (a segment overstays), and any moment of throat-clearing ("before we start…").
ClaudeSeries & Format Design
Channels grow on repeatable formats, not one-off videos: a recognizable structure (same open, same segments, same visual grammar) lowers production cost and trains the audience to return.
ChatGPTProject: One Video, Fully Engineered
Deliverable: a complete production package for one real video on your (or an invented) channel. Steps: 1.
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Fine-tuning continues training a pre-trained model on your own examples to specialise its behaviour. It is the right tool for teaching consistent style, format or classification behaviour — not for adding knowledge.
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