What 300 YouTube Titles Taught Me About CTR (Data from Building TitleScore)

Background I built TitleScore — a tool that scores YouTube titles 0–100 using a rubric fed into the Claude API. Since launching, I've had the chance to see a lot of real titles come through. Patterns emerge fast. This post is about what those patterns actually look like — the structural mistakes that reliably tank scores, and what the high-scoring alternatives have in common. The Scoring Dimensions TitleScore evaluates titles across five dimensions: Curiosity gap — does the title withhold something the viewer needs to click to get? Front-load strength — are the first 3–4 words doing real work? Emotional stakes — is something at risk, or is this just information delivery? Specificity — numbers, names, concrete outcomes vs. vague generalities Action orientation — does the title sell the click, or describe the content? Each dimension scores 0–10. The weighted total becomes the 0–100 score. The Patterns Pattern 1: Describing Instead of Selling This is the most common low-score pattern. Tit