What Is SPKV?

Views tell you that a video reached people. That matters, and YouTube's own performance docs treat views as a core way to understand content reach. But views alone do not tell you whether the video changed the relationship between the viewer and the channel.

A video can get many views because the topic is broad, the thumbnail is strong, or the algorithm found a temporary audience. That same video may still bring very few long-term subscribers. Another video can look smaller in raw views but bring a higher share of people into the channel.

SPKV separates those questions. Views answer, "How much attention did this get?" SPKV answers, "How efficiently did this turn attention into subscribers?"

The formula is deliberately simple: divide subscribers gained by views, then multiply by 1,000. The result says how many subscribers a video gained for every thousand views. It is derived from creator-owned analytics data, using underlying metrics like views and subscribers gained, not a public cross-channel score.

If a video gets 10,000 views and gains 50 subscribers, its SPKV is 5.0. That means every thousand views produced about five subscribers. If another video gets 2,000 views and gains 30 subscribers, its SPKV is 15.0. The second video reached fewer people, but the viewers it reached were more likely to subscribe.

That distinction matters because creators often learn from the wrong winner. The biggest video is not always the most repeatable video. Sometimes it is only the broadest topic, the strongest thumbnail, or the luckiest recommendation moment.

SPKV is useful when you are comparing videos inside the same channel and asking what to make next. It can help surface subscriber drivers, niche topics that bring serious viewers, and formats that may deserve more attempts. It should sit beside audience context, because YouTube also gives creators ways to understand who is watching through the Audience tab.

It is especially helpful when a channel has enough videos to show patterns. One high-SPKV video can be luck. Several high-SPKV videos with related topics, formats, titles, or audience promises are a better signal.

SPKV does not replace retention, traffic sources, returning viewers, revenue, or creator judgment. For example, YouTube's audience retention report answers a different question: where viewers stayed or dropped off inside a video. SPKV is only one additional lens. It also needs sample-size caution. A small video with two new subscribers can look strong mathematically while still being weak evidence.

Subtrack treats SPKV as one lens in a broader workflow. The goal is not to crown a single "best" video. The goal is to find useful patterns: which topics bring committed viewers, which videos create misleading reach, and which ideas are worth testing again.

Subtrack starts from the creator's own YouTube Analytics data, then organizes videos around subscriber growth instead of only views. That makes it easier to see where reach and conversion disagree.

In practice, Subtrack uses SPKV to help answer three questions: which videos bring subscribers, which high-view videos may be weaker than they look, and which patterns might inform the next upload.

This is why Subtrack is not framed as "more analytics" for its own sake. The point is to reduce the distance between the data and the creator decision. A good analytics workflow should end with a clearer next experiment, not only a bigger dashboard.