Normalising Subscriber Growth
To measure one’s impact on YouTube and determine comparative value, it is more effective to use normalised metrics. The first key metric is normalised subscriber growth per video.
Subscriber growth reflects how much viewers value an educator’s content and whether they’re interested in seeing more—making it a meaningful indicator of impact and engagement. However, raw subscriber counts can be misleading. A video on a trending topic might gain thousands of subscribers, while a highly specialised postgraduate lecture might only gain a few—despite having deep impact within its niche. Because different videos reach different audiences and scales, subscriber counts alone are not a reliable measure of educational effectiveness.
To enable fairer comparisons, subscriber growth can be normalised per 1,000 views using the following formula: A content creator on YouTube can be measured at two levels: per video and per channel.
This metric shows how effectively a video converts viewers into subscribers—an important signal of sustained interest. It can help educators identify which videos attract loyal viewers and which ones may be under-performing or even resulting in disengagement. These insights can guide content improvement based on what genuinely resonates with the audience. Example:
- Video A gains 100 subscribers from 10,000 views = 10 subscribers per 1,000 views
- Video B gains 2,000 subscribers from 2 million views = 1 subscriber per 1,000 views
While Video B has the higher total subscriber gain, the normalised rate shows that Video A had a stronger relative impact. Without normalisation, it would be easy to assume Video B was more effective, but the metric reveals deeper engagement in Video A’s audience.
By adjusting for view count, the subscribers-per-1,000-views metric provides more meaningful comparisons across different educators, topics, and audience sizes. It serves as a scalable and fair indicator of educator impact on video-sharing platforms.
This following video outlines the importance of using normalised subscriber metrics for fair comparison:
Normalising Likes
It is reasonable to assume that viewers who find a YouTube video valuable are more inclined to like it, while fewer likes—or the presence of dislikes—can indicate dissatisfaction. However, raw like counts lack context. To enable fair comparisons, the number of likes can be normalised by calculating likes per 1,000 views:
For instance, a video with 150 likes and 10,000 views yields: (150 / 10,000) × 1,000 = 15 likes per 1,000 views This normalised metric allows comparisons across videos with different view counts and topic appeal. It reduces bias that favours educators covering popular or trending topics and provides a fairer measure of content quality and viewer approval.
High view counts can sometimes reflect visibility rather than quality—such as when a video is the first available on a topic. However, if it only accrues a few likes, the limitations of such popularity become clear. In contrast, high likes per 1,000 views suggest content that is not only seen but also valued.
Although YouTube does not currently offer this normalised metric by default, it is easily calculated using publicly available data. This enables independent evaluation of an educator’s content without requiring access to the channel’s internal analytics (i.e., YouTube Studio). View and like counts are usually visible on the video page, thereby allowing stakeholders—such as institutions, students, or collaborators—to make informed assessments of video quality and educator impact.
The following video explains how the normalising of likes works:
The YouTube Video Impact Score (YVIS) Formula
The YouTube Video Impact Score (YVIS) offers a way to assess a video’s impact while adjusting for differences in audience size. Some videos attract large viewerships simply because the topic is broadly popular, which can make them appear more influential than they actually are. In contrast, videos on niche or less popular topics may have fewer views but still deliver significant value to their specific audience. YVIS helps resolve this by focusing on viewer interaction and perceived value, rather than just total views. This makes it a more accurate measure of a video’s true educational or communicative impact.
To compare different YouTube videos with each other to ascertain comparative impact, the following formula can be used:
Video-level Impact can be determined using the YouTube Video Impact Score (YVIS) Formula:
If the Likes and Subscribers are normalised then the formula can also be written as:
Where:
- YVIS: YouTube Video Impact Score – the composite score indicating overall impact.
- APV: Average Percentage Viewed – average portion of the video watched.
- LKV: Likes per 1,000 Views – engagement through likes.
- SKV: Subscribers Gained per 1,000 Views – effectiveness at converting viewers into subscribers.
- max(0, SKV): Ensures that negative subscriber growth does not produce negative YVIS scores.
- LP: Like Percentage – proportion of positive viewer ratings.
- DUR: Duration (in minutes) – total video length.
To determine your YouTube Video Impact Score (YVIS), please complete the fields below: