Virality Score Explained: How GapLoom Predicts Video Performance

Why We Built the Virality Score

When you're analyzing a competitor's channel with 500 videos, how do you know which ones are worth studying? Sorting by view count is misleading — a video from 5 years ago with 10M views might have gotten most of those from the algorithm pushing old content.

What matters is: how did this video perform relative to the channel's typical performance? That's what the virality score measures.

How It Works

The virality score is a relative performance metric. A score of 1.0 means the video performed exactly at the channel's median. Above 1.0 = overperformer. Below 1.0 = underperformer.

We calculate it using three weighted signals:

Why Median, Not Average?

This is important. We use the median, not the average. Here's why:

Imagine a channel with 100 videos. 99 of them get 50K views. One viral hit gets 50M views. The average is ~550K — which means 99% of their videos look like underperformers against their own average. That's useless.

The median is 50K — the actual "normal" for this channel. Now that 50M video shows as a massive outlier (score: 1000x), and the rest cluster around 1.0. This gives you a realistic picture of what's typical and what's exceptional.

What the Scores Mean

How to Use It

In GapLoom's Ideation Lab, when you select videos for analysis, use the virality score to pick the right ones:

• For content gaps: Analyze overperformers (1.5+) — these are proven topics with audience demand
• For anti-patterns: Compare overperformers vs. underperformers on the same topic to see what makes the difference
• For opportunity sizing: If multiple competitors have overperformers on the same topic, there's sustained demand

The virality score turns a wall of videos into an actionable priority list. Focus your analysis time on the videos that actually matter.