ChatGPT answers, unedited

Yes. If you mean YouTube, YouTube Studio plus the YouTube Analytics API can measure most of this, although there isn't one built-in metric literally called “session watch time from end screens.”

What you can measure

• Playlists: YouTube exposes playlistStarts, playlistViews, playlistEstimatedMinutesWatched, averageTimeInPlaylist, and viewsPerPlaylistStart. These are particularly useful for measuring whether starting a playlist leads to deeper viewing. Google for Developers Google for Developers
• End screens: YouTube exposes end-screen impressions, clicks, and click-through rate, broken down by video and end-screen element. Google for Developers Google for Developers
• Downstream watch time: You can combine those with video-level views, watch_time_minutes/estimatedMinutesWatched, and average view duration to analyze what happens after someone clicks. Google for Developers
• Traffic/source breakdowns: The API can break playlist activity down by traffic source, playback location, device, etc., which is useful for separating organic behavior from traffic driven by particular surfaces. Google for Developers

The key limitation

If your actual question is:

“When someone finishes Video A, does an end-screen recommendation cause them to watch Video B, and how much additional session watch time does that generate?”

YouTube's standard Analytics reports don't give you a clean per-viewer causal chain like:

Video A → end-screen click → Video B → 18.4 additional minutes watched → Video C

The end-screen report gives you the click/impression layer, while the other reports give you watch-time and viewing behavior. You generally have to join/analyze those datasets yourself. Google for Developers Google for Developers

A useful dashboard would therefore calculate something like:

YouTube Studio's Advanced Mode can also be used to compare and export analytics, while the API is better if you want an automated measurement system. Google Help

If you're trying to build a “which playlists/end screens actually increase session duration?” measurement system, I can lay out the exact YouTube Analytics API queries and a methodology for calculating incremental next-video rate and session watch-time lift.

Yes. If you mean YouTube, the main tools are:

1. YouTube Studio Analytics (built in)

This is the first place to measure playlist and end-screen impact. Google Help

For end screens

Track:

• End screen element click rate (CTR) — how often viewers click the recommended video/playlist/end-screen element. Google Help Google for Developers
• End screen element impressions
• End screen element clicks
• Which videos generate the most downstream clicks Google for Developers

Path:
YouTube Studio → Content → select video → Analytics → Engagement → End screen element click rate Google Support

For playlists

Track:

• Playlist starts
• Playlist views
• Playlist estimated minutes watched
• Average time in playlist
• Views per playlist start (a useful proxy for "did this playlist keep people watching?") Google for Developers Google for Developers

2. YouTube Analytics API (for deeper analysis)

If you want a dashboard or cohort analysis, the API exposes playlist and end-screen data. Google for Developers Google for Developers

Useful calculations:

End-screen continuation rate

End-screen clicks ÷ End-screen impressions

Playlist efficiency

Playlist views ÷ Playlist starts

Incremental session lift

Average watch time per viewer after adding end screens/playlists
-
Average watch time per viewer before changes

Note: YouTube does not provide a simple "session watch time caused by this end screen" metric. You usually infer impact by combining end-screen clicks with subsequent watch-time behavior. Google for Developers Google for Developers

3. Third-party tools

Some creator analytics platforms can make this easier by combining YouTube data into dashboards, but they generally rely on YouTube Analytics data rather than having a hidden session-level metric.

A practical setup many channels use:

• Export YouTube Analytics data
• Join:

• video ID
• end-screen clicks
• playlist starts
• watch time
• returning viewers
• traffic source
• Compare before/after changes to playlists or end-screen templates

A/B test approach

If you want to know whether a playlist or end screen is actually increasing viewing:

1. Pick a group of similar videos.
2. Change only one variable:

• add a playlist end screen, or
• change end-screen recommendation logic.
• Compare:

• end-screen CTR
• views per viewer
• average watch time per viewer
• returning viewers
• next-video starts

For a channel-level "do my end screens create longer viewing sessions?" question, YouTube Studio + a spreadsheet/dashboard built from Analytics API data is usually the most direct approach. Google for Developers Google for Developers

3. Third-party tools

Some creator analytics platforms can make this easier by combining YouTube data into dashboards, but they generally rely on YouTube Analytics data rather than having a hidden session-level metric.

A practical setup many channels use:

• Export YouTube Analytics data
• Join:

• video ID
• end-screen clicks
• playlist starts
• watch time
• returning viewers
• traffic source
• Compare before/after changes to playlists or end-screen templates

A/B test approach

If you want to know whether a playlist or end screen is actually increasing viewing:

Yes. If you mean YouTube, there are built-in tools in YouTube Studio Analytics and the YouTube Analytics API that can measure much of this, although there is not one single “playlist + end screen → session watch time” dashboard. You typically combine several reports. Google for Developers Google for Developers

1. YouTube Studio Analytics (no coding)

End screens

In YouTube Studio you can measure:

• End screen element impressions
• End screen element clicks
• End screen click-through rate (CTR)
• Which videos generate the most end-screen clicks WebFiddle Google Help

This tells you whether your end screens are successfully causing viewers to choose another video.

Playlists

Playlist analytics can show:

• Playlist starts
• Playlist views
• Views per playlist start
• Average time in playlist
• Playlist estimated minutes watched Google for Developers

Those are the closest native measurements for “did this playlist extend the viewing session?”

2. YouTube Analytics API (for deeper analysis)

If you want to build a dashboard, the API lets you combine:

Playlist impact

• playlistStarts
• playlistViews
• viewsPerPlaylistStart
• playlistEstimatedMinutesWatched
• averageTimeInPlaylist Google for Developers Google for Developers

End screen impact

• end_screen_element_impressions
• end_screen_element_clicks
• end_screen_element_click_rate Google for Developers Google for Developers

A useful derived metric is:

Incremental session lift = watch time after recommendation action − watch time without recommendation action

For example:

• Viewers who click an end screen → average 22 minutes of additional channel watch time
• Viewers who do not click → average 5 minutes afterward
• Estimated lift → +17 minutes

3. What many creators track as a “session funnel”

A practical dashboard might look like:

4. Third-party tools

Some creator analytics platforms can visualize these relationships, but they generally rely on YouTube’s available data rather than having a hidden session-level dataset. For serious analysis, many teams export YouTube Analytics API data into:

• Google Looker Studio
• BigQuery
• Tableau
• Power BI

If you’re trying to answer “Which playlists and end screens actually increase total channel watch time?”, a custom dashboard using YouTube Analytics API data is usually the best approach. Google for Developers Google for Developers

4. Third-party tools

Some creator analytics platforms can visualize these relationships, but they generally rely on YouTube’s available data rather than having a hidden session-level dataset. For serious analysis, many teams export YouTube Analytics API data into:

• Google Looker Studio
• BigQuery
• Tableau
• Power BI

If you’re trying to answer “Which playlists and end screens actually increase total channel watch time?”, a custom dashboard using YouTube Analytics API data is usually the best approach. Google for Developers Google for Developers

Yes—if you mean YouTube, YouTube Studio has built-in analytics that can get fairly close to this.

• Playlists: YouTube Studio provides playlist-level analytics, including aggregated watch time and viewer-behavior metrics such as views from playlist. You can compare multiple playlists in Advanced Mode. Google Help Google Help
• End screens: For individual videos, Studio reports End screen element click rate, so you can see how often viewers click the recommended video/playlist/etc. at the end. Google Help
• Next-video behavior: You can analyze traffic sources such as Playlists, Suggested videos, and end-screen-driven traffic, and use Advanced Mode to compare content and metrics. Google Help Google Help
• Session watch time: YouTube doesn't appear to expose a single clean metric called something like “session watch time generated by this end screen.” You generally have to infer the downstream effect from clicks, subsequent views, watch time, and traffic-source data.

support.google.com

If you're trying to answer “Did adding this playlist/end screen actually cause people to watch another video and extend their session?”, I can also lay out the exact metrics + analysis methodology I'd use, including how to estimate incremental next-video plays rather than just correlation.

Yes. If you mean YouTube, the main tool is YouTube Studio Analytics → Advanced Mode.

It can measure most of what you’re after:

• Playlists: playlist views, playlist watch time, and playlist average duration. These specifically measure viewing in the context of the playlist. Google Help
• End screens: end-screen element click rate, plus traffic attributed to End screens. Google Help Google Help
• Next-video behavior: You can use traffic-source data to see how much viewing comes from end screens and playlists, then compare resulting watch time and average view duration. Google Help
• Experiments/comparisons: Advanced Mode lets you compare videos, groups, playlists, date ranges, and metrics, and export the underlying data. Google Help Google Help

The important distinction

There isn't, as far as YouTube's standard Studio reporting goes, a single metric called "session watch time uplift from playlists/end screens." You generally have to construct that analysis from the available metrics.

For example, I'd build a dashboard around:

If you're trying to answer "Did adding playlists/end screens causally increase session watch time and next-video play rate?", that's a slightly more sophisticated analysis. You'd ideally export YouTube Analytics data and build a pre/post or controlled cohort analysis rather than just looking at raw clicks.

If you tell me whether this is YouTube, Vimeo, or another video platform, I can lay out the exact metrics and analysis I'd use.

Short answer: not directly.

There is no public YouTube Analytics metric or third-party tool that measures the causal impact of playlists or end screens on overall session watch time or next-video play rate. Those are internal recommendation metrics that YouTube uses, but they are not exposed to creators via Studio or the Analytics API. Google for Developers Google Support

What you can measure is:

If you want to estimate the impact

Many growth teams build their own proxy dashboard using the YouTube Analytics API and Studio metrics:

• End screen CTR
• Playlist starts
• Views per playlist start
• Average time in playlist
• Watch time from playlist traffic
• Traffic source = End screens
• Suggested video traffic growth

These don't measure session watch time directly, but together they provide a reasonable indication of whether viewers continue watching after your videos. Google for Developers Google for Developers Google for Developers

Third-party tools

Tools like:

• TubeBuddy
• vidIQ
• ChannelMeter
• Social Blade

can help visualize YouTube analytics or benchmark performance, but none of them have access to YouTube's internal session watch time or next-video play metrics because those data are not exposed through the API. Google for Developers

If you're running experiments

The closest approach is an A/B test:

1. Create two cohorts of similar videos.
2. Cohort A: end screen links to a playlist.
3. Cohort B: end screen links to a single related video.
4. Compare: - End screen CTR
5. Playlist starts
6. Average time in playlist
7. Watch time from end-screen traffic
8. Subsequent views to the target video(s)

This won't give true session watch time, but it's the best creator-accessible approximation of whether one strategy keeps viewers watching longer.

If you're building analytics software, I can also suggest a derived "Session Continuation Score" that estimates next-video consumption from the metrics YouTube does expose.

Short answer: not directly.

There is no public YouTube Analytics metric or third-party tool that measures the causal impact of playlists or end screens on overall session watch time or next-video play rate. Those are internal recommendation metrics that YouTube uses, but they are not exposed to creators via Studio or the Analytics API. Google for Developers Google Support

What you can measure is:

If you want to estimate the impact

Many growth teams build their own proxy dashboard using the YouTube Analytics API and Studio metrics:

• End screen CTR
• Playlist starts
• Views per playlist start
• Average time in playlist
• Watch time from playlist traffic
• Traffic source = End screens
• Suggested video traffic growth

These don't measure session watch time directly, but together they provide a reasonable indication of whether viewers continue watching after your videos. Google for Developers Google for Developers Google for Developers

Third-party tools

Tools like:

• TubeBuddy
• vidIQ
• ChannelMeter
• Social Blade

can help visualize YouTube analytics or benchmark performance, but none of them have access to YouTube's internal session watch time or next-video play metrics because those data are not exposed through the API. Google for Developers

If you're running experiments

The closest approach is an A/B test:

1. Create two cohorts of similar videos.
2. Cohort A: end screen links to a playlist.
3. Cohort B: end screen links to a single related video.
4. Compare: - End screen CTR
5. Playlist starts
6. Average time in playlist
7. Watch time from end-screen traffic
8. Subsequent views to the target video(s)

This won't give true session watch time, but it's the best creator-accessible approximation of whether one strategy keeps viewers watching longer.

If you're building analytics software, I can also suggest a derived "Session Continuation Score" that estimates next-video consumption from the metrics YouTube does expose.

Short answer: not directly.

There is no public YouTube Analytics metric or third-party tool that measures the causal impact of playlists or end screens on overall session watch time or next-video play rate. Those are internal recommendation metrics that YouTube uses, but they are not exposed to creators via Studio or the Analytics API. Google for Developers Google Support

What you can measure is:

If you want to estimate the impact

Many growth teams build their own proxy dashboard using the YouTube Analytics API and Studio metrics:

• End screen CTR
• Playlist starts
• Views per playlist start
• Average time in playlist
• Watch time from playlist traffic
• Traffic source = End screens
• Suggested video traffic growth

These don't measure session watch time directly, but together they provide a reasonable indication of whether viewers continue watching after your videos. Google for Developers Google for Developers Google for Developers

Third-party tools

Tools like:

• TubeBuddy
• vidIQ
• ChannelMeter
• Social Blade

can help visualize YouTube analytics or benchmark performance, but none of them have access to YouTube's internal session watch time or next-video play metrics because those data are not exposed through the API. Google for Developers

If you're running experiments

The closest approach is an A/B test:

1. Create two cohorts of similar videos.
2. Cohort A: end screen links to a playlist.
3. Cohort B: end screen links to a single related video.
4. Compare: - End screen CTR
5. Playlist starts
6. Average time in playlist
7. Watch time from end-screen traffic
8. Subsequent views to the target video(s)

This won't give true session watch time, but it's the best creator-accessible approximation of whether one strategy keeps viewers watching longer.

If you're building analytics software, I can also suggest a derived "Session Continuation Score" that estimates next-video consumption from the metrics YouTube does expose.

Short answer: not directly.

There is no public YouTube Analytics metric or third-party tool that measures the causal impact of playlists or end screens on overall session watch time or next-video play rate. Those are internal recommendation metrics that YouTube uses, but they are not exposed to creators via Studio or the Analytics API. Google for Developers Google Support

What you can measure is:

If you want to estimate the impact

Many growth teams build their own proxy dashboard using the YouTube Analytics API and Studio metrics:

• End screen CTR
• Playlist starts
• Views per playlist start
• Average time in playlist
• Watch time from playlist traffic
• Traffic source = End screens
• Suggested video traffic growth

These don't measure session watch time directly, but together they provide a reasonable indication of whether viewers continue watching after your videos. Google for Developers Google for Developers Google for Developers

Third-party tools

Tools like:

• TubeBuddy
• vidIQ
• ChannelMeter
• Social Blade

can help visualize YouTube analytics or benchmark performance, but none of them have access to YouTube's internal session watch time or next-video play metrics because those data are not exposed through the API. Google for Developers

If you're running experiments

The closest approach is an A/B test:

1. Create two cohorts of similar videos.
2. Cohort A: end screen links to a playlist.
3. Cohort B: end screen links to a single related video.
4. Compare: - End screen CTR
5. Playlist starts
6. Average time in playlist
7. Watch time from end-screen traffic
8. Subsequent views to the target video(s)

This won't give true session watch time, but it's the best creator-accessible approximation of whether one strategy keeps viewers watching longer.

If you're building analytics software, I can also suggest a derived "Session Continuation Score" that estimates next-video consumption from the metrics YouTube does expose.

Short answer: not directly.

There is no public YouTube Analytics metric or third-party tool that measures the causal impact of playlists or end screens on overall session watch time or next-video play rate. Those are internal recommendation metrics that YouTube uses, but they are not exposed to creators via Studio or the Analytics API. Google for Developers Google Support

What you can measure is:

If you want to estimate the impact

Many growth teams build their own proxy dashboard using the YouTube Analytics API and Studio metrics:

• End screen CTR
• Playlist starts
• Views per playlist start
• Average time in playlist
• Watch time from playlist traffic
• Traffic source = End screens
• Suggested video traffic growth

These don't measure session watch time directly, but together they provide a reasonable indication of whether viewers continue watching after your videos. Google for Developers Google for Developers Google for Developers

Third-party tools

Tools like:

• TubeBuddy
• vidIQ
• ChannelMeter
• Social Blade

can help visualize YouTube analytics or benchmark performance, but none of them have access to YouTube's internal session watch time or next-video play metrics because those data are not exposed through the API. Google for Developers

If you're running experiments

The closest approach is an A/B test:

1. Create two cohorts of similar videos.
2. Cohort A: end screen links to a playlist.
3. Cohort B: end screen links to a single related video.
4. Compare: - End screen CTR
5. Playlist starts
6. Average time in playlist
7. Watch time from end-screen traffic
8. Subsequent views to the target video(s)

This won't give true session watch time, but it's the best creator-accessible approximation of whether one strategy keeps viewers watching longer.

If you're building analytics software, I can also suggest a derived "Session Continuation Score" that estimates next-video consumption from the metrics YouTube does expose.

Short answer: not directly.

There is no public YouTube Analytics metric or third-party tool that measures the causal impact of playlists or end screens on overall session watch time or next-video play rate. Those are internal recommendation metrics that YouTube uses, but they are not exposed to creators via Studio or the Analytics API. Google for Developers Google Support

What you can measure is:

If you want to estimate the impact

Many growth teams build their own proxy dashboard using the YouTube Analytics API and Studio metrics:

• End screen CTR
• Playlist starts
• Views per playlist start
• Average time in playlist
• Watch time from playlist traffic
• Traffic source = End screens
• Suggested video traffic growth

These don't measure session watch time directly, but together they provide a reasonable indication of whether viewers continue watching after your videos. Google for Developers Google for Developers Google for Developers

Third-party tools

Tools like:

• TubeBuddy
• vidIQ
• ChannelMeter
• Social Blade

can help visualize YouTube analytics or benchmark performance, but none of them have access to YouTube's internal session watch time or next-video play metrics because those data are not exposed through the API. Google for Developers

If you're running experiments

The closest approach is an A/B test:

1. Create two cohorts of similar videos.
2. Cohort A: end screen links to a playlist.
3. Cohort B: end screen links to a single related video.
4. Compare: - End screen CTR
5. Playlist starts
6. Average time in playlist
7. Watch time from end-screen traffic
8. Subsequent views to the target video(s)

This won't give true session watch time, but it's the best creator-accessible approximation of whether one strategy keeps viewers watching longer.

If you're building analytics software, I can also suggest a derived "Session Continuation Score" that estimates next-video consumption from the metrics YouTube does expose.

Short answer: not directly.

There is no public YouTube Analytics metric or third-party tool that measures the causal impact of playlists or end screens on overall session watch time or next-video play rate. Those are internal recommendation metrics that YouTube uses, but they are not exposed to creators via Studio or the Analytics API. Google for Developers Google Support

What you can measure is:

If you want to estimate the impact

Many growth teams build their own proxy dashboard using the YouTube Analytics API and Studio metrics:

• End screen CTR
• Playlist starts
• Views per playlist start
• Average time in playlist
• Watch time from playlist traffic
• Traffic source = End screens
• Suggested video traffic growth

These don't measure session watch time directly, but together they provide a reasonable indication of whether viewers continue watching after your videos. Google for Developers Google for Developers Google for Developers

Third-party tools

Tools like:

• TubeBuddy
• vidIQ
• ChannelMeter
• Social Blade

can help visualize YouTube analytics or benchmark performance, but none of them have access to YouTube's internal session watch time or next-video play metrics because those data are not exposed through the API. Google for Developers

If you're running experiments

The closest approach is an A/B test:

1. Create two cohorts of similar videos.
2. Cohort A: end screen links to a playlist.
3. Cohort B: end screen links to a single related video.
4. Compare: - End screen CTR
5. Playlist starts
6. Average time in playlist
7. Watch time from end-screen traffic
8. Subsequent views to the target video(s)

This won't give true session watch time, but it's the best creator-accessible approximation of whether one strategy keeps viewers watching longer.

If you're building analytics software, I can also suggest a derived "Session Continuation Score" that estimates next-video consumption from the metrics YouTube does expose.

Yes, but with an important limitation: there isn't a first-party YouTube metric that directly attributes session watch time or next-video play rate to playlists or end screens. You can, however, get close using a combination of YouTube Studio analytics and the YouTube Analytics API.

Here's what's available:

What you can measure

For playlists

• Playlist starts
• Average time spent in the playlist
• Playlist watch time
• Views per playlist start
• Playlist traffic sources Google for Developers Google for Developers

These tell you whether playlists are keeping viewers engaged once they begin watching.

For end screens
YouTube Studio provides:

• End screen element click rate
• Performance by end screen element
• Top videos by end screen clicks Google Help

If you're trying to optimize session growth

Many creator analytics teams calculate custom metrics such as:

• Playlist continuation rate = viewers who watched ≥2 videos after entering a playlist
• End-screen continuation rate = end-screen clicks ÷ viewers who reached the end screen
• Next-video play rate = viewers who watched another channel video within a defined window after finishing the first
• Incremental watch time generated after an end-screen click

These are derived metrics, not native YouTube metrics. They require exporting analytics data or combining YouTube Analytics API data with your own event processing.

Third-party tools

Most creator tools (TubeBuddy, vidIQ, Viewstats, etc.) focus on SEO, competitors, and channel performance. None can directly measure YouTube's internal session watch time, because YouTube does not expose that data publicly.

If you're building a measurement system

A practical dashboard could estimate:

• End-screen CTR
• Playlist entry rate
• Playlist completion rate
• Average videos watched per playlist session
• Estimated incremental watch minutes after an end-screen click
• Estimated next-video play rate

This won't equal YouTube's internal session metrics, but it's often the closest proxy available and is sufficient for A/B testing playlist layouts and end-screen strategies.

Yes, but with an important limitation: there isn't a first-party YouTube metric that directly attributes session watch time or next-video play rate to playlists or end screens. You can, however, get close using a combination of YouTube Studio analytics and the YouTube Analytics API.

Here's what's available:

What you can measure

For playlists

• Playlist starts
• Average time spent in the playlist
• Playlist watch time
• Views per playlist start
• Playlist traffic sources Google for Developers Google for Developers

These tell you whether playlists are keeping viewers engaged once they begin watching.

For end screens
YouTube Studio provides:

• End screen element click rate
• Performance by end screen element
• Top videos by end screen clicks Google Help

If you're trying to optimize session growth

Many creator analytics teams calculate custom metrics such as:

• Playlist continuation rate = viewers who watched ≥2 videos after entering a playlist
• End-screen continuation rate = end-screen clicks ÷ viewers who reached the end screen
• Next-video play rate = viewers who watched another channel video within a defined window after finishing the first
• Incremental watch time generated after an end-screen click

These are derived metrics, not native YouTube metrics. They require exporting analytics data or combining YouTube Analytics API data with your own event processing.

Third-party tools

Most creator tools (TubeBuddy, vidIQ, Viewstats, etc.) focus on SEO, competitors, and channel performance. None can directly measure YouTube's internal session watch time, because YouTube does not expose that data publicly.

If you're building a measurement system

A practical dashboard could estimate:

• End-screen CTR
• Playlist entry rate
• Playlist completion rate
• Average videos watched per playlist session
• Estimated incremental watch minutes after an end-screen click
• Estimated next-video play rate

This won't equal YouTube's internal session metrics, but it's often the closest proxy available and is sufficient for A/B testing playlist layouts and end-screen strategies.

Yes, but with an important limitation: there isn't a first-party YouTube metric that directly attributes session watch time or next-video play rate to playlists or end screens. You can, however, get close using a combination of YouTube Studio analytics and the YouTube Analytics API.

Here's what's available:

What you can measure

For playlists

• Playlist starts
• Average time spent in the playlist
• Playlist watch time
• Views per playlist start
• Playlist traffic sources Google for Developers Google for Developers

These tell you whether playlists are keeping viewers engaged once they begin watching.

For end screens
YouTube Studio provides:

• End screen element click rate
• Performance by end screen element
• Top videos by end screen clicks Google Help

If you're trying to optimize session growth

Many creator analytics teams calculate custom metrics such as:

• Playlist continuation rate = viewers who watched ≥2 videos after entering a playlist
• End-screen continuation rate = end-screen clicks ÷ viewers who reached the end screen
• Next-video play rate = viewers who watched another channel video within a defined window after finishing the first
• Incremental watch time generated after an end-screen click

These are derived metrics, not native YouTube metrics. They require exporting analytics data or combining YouTube Analytics API data with your own event processing.

Third-party tools

Most creator tools (TubeBuddy, vidIQ, Viewstats, etc.) focus on SEO, competitors, and channel performance. None can directly measure YouTube's internal session watch time, because YouTube does not expose that data publicly.

If you're building a measurement system

A practical dashboard could estimate:

• End-screen CTR
• Playlist entry rate
• Playlist completion rate
• Average videos watched per playlist session
• Estimated incremental watch minutes after an end-screen click
• Estimated next-video play rate

This won't equal YouTube's internal session metrics, but it's often the closest proxy available and is sufficient for A/B testing playlist layouts and end-screen strategies.

Yes, but with an important limitation: there isn't a first-party YouTube metric that directly attributes session watch time or next-video play rate to playlists or end screens. You can, however, get close using a combination of YouTube Studio analytics and the YouTube Analytics API.

Here's what's available:

What you can measure

For playlists

• Playlist starts
• Average time spent in the playlist
• Playlist watch time
• Views per playlist start
• Playlist traffic sources Google for Developers Google for Developers

These tell you whether playlists are keeping viewers engaged once they begin watching.

For end screens
YouTube Studio provides:

• End screen element click rate
• Performance by end screen element
• Top videos by end screen clicks Google Help

If you're trying to optimize session growth

Many creator analytics teams calculate custom metrics such as:

• Playlist continuation rate = viewers who watched ≥2 videos after entering a playlist
• End-screen continuation rate = end-screen clicks ÷ viewers who reached the end screen
• Next-video play rate = viewers who watched another channel video within a defined window after finishing the first
• Incremental watch time generated after an end-screen click

These are derived metrics, not native YouTube metrics. They require exporting analytics data or combining YouTube Analytics API data with your own event processing.

Third-party tools

Most creator tools (TubeBuddy, vidIQ, Viewstats, etc.) focus on SEO, competitors, and channel performance. None can directly measure YouTube's internal session watch time, because YouTube does not expose that data publicly.

If you're building a measurement system

A practical dashboard could estimate:

• End-screen CTR
• Playlist entry rate
• Playlist completion rate
• Average videos watched per playlist session
• Estimated incremental watch minutes after an end-screen click
• Estimated next-video play rate

This won't equal YouTube's internal session metrics, but it's often the closest proxy available and is sufficient for A/B testing playlist layouts and end-screen strategies.

Yes, but with an important limitation: there isn't a first-party YouTube metric that directly attributes session watch time or next-video play rate to playlists or end screens. You can, however, get close using a combination of YouTube Studio analytics and the YouTube Analytics API.

Here's what's available:

What you can measure

For playlists

• Playlist starts
• Average time spent in the playlist
• Playlist watch time
• Views per playlist start
• Playlist traffic sources Google for Developers Google for Developers

These tell you whether playlists are keeping viewers engaged once they begin watching.

For end screens
YouTube Studio provides:

• End screen element click rate
• Performance by end screen element
• Top videos by end screen clicks Google Help

If you're trying to optimize session growth

Many creator analytics teams calculate custom metrics such as:

• Playlist continuation rate = viewers who watched ≥2 videos after entering a playlist
• End-screen continuation rate = end-screen clicks ÷ viewers who reached the end screen
• Next-video play rate = viewers who watched another channel video within a defined window after finishing the first
• Incremental watch time generated after an end-screen click

These are derived metrics, not native YouTube metrics. They require exporting analytics data or combining YouTube Analytics API data with your own event processing.

Third-party tools

Most creator tools (TubeBuddy, vidIQ, Viewstats, etc.) focus on SEO, competitors, and channel performance. None can directly measure YouTube's internal session watch time, because YouTube does not expose that data publicly.

If you're building a measurement system

A practical dashboard could estimate:

• End-screen CTR
• Playlist entry rate
• Playlist completion rate
• Average videos watched per playlist session
• Estimated incremental watch minutes after an end-screen click
• Estimated next-video play rate

This won't equal YouTube's internal session metrics, but it's often the closest proxy available and is sufficient for A/B testing playlist layouts and end-screen strategies.

Yes, but with an important limitation: there isn't a first-party YouTube metric that directly attributes session watch time or next-video play rate to playlists or end screens. You can, however, get close using a combination of YouTube Studio analytics and the YouTube Analytics API.

Here's what's available:

What you can measure

For playlists

• Playlist starts
• Average time spent in the playlist
• Playlist watch time
• Views per playlist start
• Playlist traffic sources Google for Developers Google for Developers

These tell you whether playlists are keeping viewers engaged once they begin watching.

For end screens
YouTube Studio provides:

• End screen element click rate
• Performance by end screen element
• Top videos by end screen clicks Google Help

If you're trying to optimize session growth

Many creator analytics teams calculate custom metrics such as:

• Playlist continuation rate = viewers who watched ≥2 videos after entering a playlist
• End-screen continuation rate = end-screen clicks ÷ viewers who reached the end screen
• Next-video play rate = viewers who watched another channel video within a defined window after finishing the first
• Incremental watch time generated after an end-screen click

These are derived metrics, not native YouTube metrics. They require exporting analytics data or combining YouTube Analytics API data with your own event processing.

Third-party tools

Most creator tools (TubeBuddy, vidIQ, Viewstats, etc.) focus on SEO, competitors, and channel performance. None can directly measure YouTube's internal session watch time, because YouTube does not expose that data publicly.

If you're building a measurement system

A practical dashboard could estimate:

• End-screen CTR
• Playlist entry rate
• Playlist completion rate
• Average videos watched per playlist session
• Estimated incremental watch minutes after an end-screen click
• Estimated next-video play rate

This won't equal YouTube's internal session metrics, but it's often the closest proxy available and is sufficient for A/B testing playlist layouts and end-screen strategies.

Yes, but with an important limitation: there isn't a first-party YouTube metric that directly attributes session watch time or next-video play rate to playlists or end screens. You can, however, get close using a combination of YouTube Studio analytics and the YouTube Analytics API.

Here's what's available:

What you can measure

For playlists

• Playlist starts
• Average time spent in the playlist
• Playlist watch time
• Views per playlist start
• Playlist traffic sources Google for Developers Google for Developers

These tell you whether playlists are keeping viewers engaged once they begin watching.

For end screens
YouTube Studio provides:

• End screen element click rate
• Performance by end screen element
• Top videos by end screen clicks Google Help

If you're trying to optimize session growth

Many creator analytics teams calculate custom metrics such as:

• Playlist continuation rate = viewers who watched ≥2 videos after entering a playlist
• End-screen continuation rate = end-screen clicks ÷ viewers who reached the end screen
• Next-video play rate = viewers who watched another channel video within a defined window after finishing the first
• Incremental watch time generated after an end-screen click

These are derived metrics, not native YouTube metrics. They require exporting analytics data or combining YouTube Analytics API data with your own event processing.

Third-party tools

Most creator tools (TubeBuddy, vidIQ, Viewstats, etc.) focus on SEO, competitors, and channel performance. None can directly measure YouTube's internal session watch time, because YouTube does not expose that data publicly.

If you're building a measurement system

A practical dashboard could estimate:

• End-screen CTR
• Playlist entry rate
• Playlist completion rate
• Average videos watched per playlist session
• Estimated incremental watch minutes after an end-screen click
• Estimated next-video play rate

This won't equal YouTube's internal session metrics, but it's often the closest proxy available and is sufficient for A/B testing playlist layouts and end-screen strategies.

Yes, but with an important limitation: there isn't a first-party YouTube metric that directly attributes session watch time or next-video play rate to playlists or end screens. You can, however, get close using a combination of YouTube Studio analytics and the YouTube Analytics API.

Here's what's available:

What you can measure

For playlists

• Playlist starts
• Average time spent in the playlist
• Playlist watch time
• Views per playlist start
• Playlist traffic sources Google for Developers Google for Developers

These tell you whether playlists are keeping viewers engaged once they begin watching.

For end screens
YouTube Studio provides:

• End screen element click rate
• Performance by end screen element
• Top videos by end screen clicks Google Help

If you're trying to optimize session growth

Many creator analytics teams calculate custom metrics such as:

• Playlist continuation rate = viewers who watched ≥2 videos after entering a playlist
• End-screen continuation rate = end-screen clicks ÷ viewers who reached the end screen
• Next-video play rate = viewers who watched another channel video within a defined window after finishing the first
• Incremental watch time generated after an end-screen click

These are derived metrics, not native YouTube metrics. They require exporting analytics data or combining YouTube Analytics API data with your own event processing.

Third-party tools

Most creator tools (TubeBuddy, vidIQ, Viewstats, etc.) focus on SEO, competitors, and channel performance. None can directly measure YouTube's internal session watch time, because YouTube does not expose that data publicly.

If you're building a measurement system

A practical dashboard could estimate:

• End-screen CTR
• Playlist entry rate
• Playlist completion rate
• Average videos watched per playlist session
• Estimated incremental watch minutes after an end-screen click
• Estimated next-video play rate

This won't equal YouTube's internal session metrics, but it's often the closest proxy available and is sufficient for A/B testing playlist layouts and end-screen strategies.

Yes, but with an important limitation: there isn't a first-party YouTube metric that directly attributes session watch time or next-video play rate to playlists or end screens. You can, however, get close using a combination of YouTube Studio analytics and the YouTube Analytics API.

Here's what's available:

What you can measure

For playlists

• Playlist starts
• Average time spent in the playlist
• Playlist watch time
• Views per playlist start
• Playlist traffic sources Google for Developers Google for Developers

These tell you whether playlists are keeping viewers engaged once they begin watching.

For end screens
YouTube Studio provides:

• End screen element click rate
• Performance by end screen element
• Top videos by end screen clicks Google Help

If you're trying to optimize session growth

Many creator analytics teams calculate custom metrics such as:

• Playlist continuation rate = viewers who watched ≥2 videos after entering a playlist
• End-screen continuation rate = end-screen clicks ÷ viewers who reached the end screen
• Next-video play rate = viewers who watched another channel video within a defined window after finishing the first
• Incremental watch time generated after an end-screen click

These are derived metrics, not native YouTube metrics. They require exporting analytics data or combining YouTube Analytics API data with your own event processing.

Third-party tools

Most creator tools (TubeBuddy, vidIQ, Viewstats, etc.) focus on SEO, competitors, and channel performance. None can directly measure YouTube's internal session watch time, because YouTube does not expose that data publicly.

If you're building a measurement system

A practical dashboard could estimate:

• End-screen CTR
• Playlist entry rate
• Playlist completion rate
• Average videos watched per playlist session
• Estimated incremental watch minutes after an end-screen click
• Estimated next-video play rate

This won't equal YouTube's internal session metrics, but it's often the closest proxy available and is sufficient for A/B testing playlist layouts and end-screen strategies.

Yes, but with an important limitation: there isn't a first-party tool that directly attributes "session watch time" or "next-video play rate" to playlists or end screens the way Google Analytics attributes conversions. YouTube intentionally keeps much of session-level recommendation data opaque.

What you can measure is:

What you can't measure directly

YouTube does not expose metrics such as:

• Session watch time generated by a specific end screen
• "Next-video play rate" after an end screen click
• Incremental watch time caused by adding a playlist vs. linking a single video
• Cross-session attribution from one video to later recommendations

These metrics are used internally by YouTube but are not available through Studio or the Analytics API. Google for Developers Google for Developers

Practical workaround

Many growth teams approximate these effects by running A/B tests:

1. Split similar videos into two groups.
2. Group A links to a playlist.
3. Group B links to a single "next" video.
4. Compare: - End screen CTR
5. Views from end screens
6. Playlist starts
7. Views per playlist start
8. Total watch time per viewer over the following 7–28 days

This won't tell you true session watch time, but it gives a strong directional signal.

If you need enterprise-level analysis

If you're comfortable with data engineering, you can combine:

• YouTube Analytics API exports
• BigQuery
• Looker Studio or Tableau

to build custom dashboards showing funnel metrics such as:

Video A
  ↓
End screen CTR
  ↓
Playlist starts
  ↓
Views per playlist start
  ↓
Average time in playlist
  ↓
Estimated watch time generated

This is about as close as you can get to measuring the impact of playlists on viewing sessions without access to YouTube's internal recommendation data.

If you're asking because you're building an analytics product for creators, I can also suggest proxy metrics that correlate surprisingly well with true session watch time even though YouTube doesn't expose it directly.

Yes, but with an important limitation: there isn't a first-party tool that directly attributes "session watch time" or "next-video play rate" to playlists or end screens the way Google Analytics attributes conversions. YouTube intentionally keeps much of session-level recommendation data opaque.

What you can measure is:

What you can't measure directly

YouTube does not expose metrics such as:

• Session watch time generated by a specific end screen
• "Next-video play rate" after an end screen click
• Incremental watch time caused by adding a playlist vs. linking a single video
• Cross-session attribution from one video to later recommendations

These metrics are used internally by YouTube but are not available through Studio or the Analytics API. developers.google.com

Practical workaround

Many growth teams approximate these effects by running A/B tests:

1. Split similar videos into two groups.
2. Group A links to a playlist.
3. Group B links to a single "next" video.
4. Compare:
   • End screen CTR
   • Views from end screens
   • Playlist starts
   • Views per playlist start
   • Total watch time per viewer over the following 7–28 days

This won't tell you true session watch time, but it gives a strong directional signal.

If you need enterprise-level analysis

If you're comfortable with data engineering, you can combine:

• YouTube Analytics API exports
• BigQuery
• Looker Studio or Tableau

to build custom dashboards showing funnel metrics such as:

Video A
  ↓
End screen CTR
  ↓
Playlist starts
  ↓
Views per playlist start
  ↓
Average time in playlist
  ↓
Estimated watch time generated

This is about as close as you can get to measuring the impact of playlists on viewing sessions without access to YouTube's internal recommendation data.

If you're asking because you're building an analytics product for creators, I can also suggest proxy metrics that correlate surprisingly well with true session watch time even though YouTube doesn't expose it directly.

Yes, but with an important limitation: there isn't a first-party tool that directly attributes "session watch time" or "next-video play rate" to playlists or end screens the way Google Analytics attributes conversions. YouTube intentionally keeps much of session-level recommendation data opaque.

What you can measure is:

What you can't measure directly

YouTube does not expose metrics such as:

• Session watch time generated by a specific end screen
• "Next-video play rate" after an end screen click
• Incremental watch time caused by adding a playlist vs. linking a single video
• Cross-session attribution from one video to later recommendations

These metrics are used internally by YouTube but are not available through Studio or the Analytics API. Google for Developers Google for Developers

Practical workaround

Many growth teams approximate these effects by running A/B tests:

1. Split similar videos into two groups.
2. Group A links to a playlist.
3. Group B links to a single "next" video.
4. Compare: - End screen CTR
5. Views from end screens
6. Playlist starts
7. Views per playlist start
8. Total watch time per viewer over the following 7–28 days

This won't tell you true session watch time, but it gives a strong directional signal.

If you need enterprise-level analysis

If you're comfortable with data engineering, you can combine:

• YouTube Analytics API exports
• BigQuery
• Looker Studio or Tableau

to build custom dashboards showing funnel metrics such as:

Video A
  ↓
End screen CTR
  ↓
Playlist starts
  ↓
Views per playlist start
  ↓
Average time in playlist
  ↓
Estimated watch time generated

This is about as close as you can get to measuring the impact of playlists on viewing sessions without access to YouTube's internal recommendation data.

If you're asking because you're building an analytics product for creators, I can also suggest proxy metrics that correlate surprisingly well with true session watch time even though YouTube doesn't expose it directly.

Yes, but with an important limitation: there isn't a first-party tool that directly attributes "session watch time" or "next-video play rate" to playlists or end screens the way Google Analytics attributes conversions. YouTube intentionally keeps much of session-level recommendation data opaque.

What you can measure is:

What you can't measure directly

YouTube does not expose metrics such as:

• Session watch time generated by a specific end screen
• "Next-video play rate" after an end screen click
• Incremental watch time caused by adding a playlist vs. linking a single video
• Cross-session attribution from one video to later recommendations

These metrics are used internally by YouTube but are not available through Studio or the Analytics API. developers.google.com

Practical workaround

Many growth teams approximate these effects by running A/B tests:

1. Split similar videos into two groups.
2. Group A links to a playlist.
3. Group B links to a single "next" video.
4. Compare:
   • End screen CTR
   • Views from end screens
   • Playlist starts
   • Views per playlist start
   • Total watch time per viewer over the following 7–28 days

This won't tell you true session watch time, but it gives a strong directional signal.

If you need enterprise-level analysis

If you're comfortable with data engineering, you can combine:

• YouTube Analytics API exports
• BigQuery
• Looker Studio or Tableau

to build custom dashboards showing funnel metrics such as:

Video A
  ↓
End screen CTR
  ↓
Playlist starts
  ↓
Views per playlist start
  ↓
Average time in playlist
  ↓
Estimated watch time generated

This is about as close as you can get to measuring the impact of playlists on viewing sessions without access to YouTube's internal recommendation data.

If you're asking because you're building an analytics product for creators, I can also suggest proxy metrics that correlate surprisingly well with true session watch time even though YouTube doesn't expose it directly.

Yes, but with an important limitation: there isn't a first-party tool that directly attributes "session watch time" or "next-video play rate" to playlists or end screens the way Google Analytics attributes conversions. YouTube intentionally keeps much of session-level recommendation data opaque.

What you can measure is:

What you can't measure directly

YouTube does not expose metrics such as:

• Session watch time generated by a specific end screen
• "Next-video play rate" after an end screen click
• Incremental watch time caused by adding a playlist vs. linking a single video
• Cross-session attribution from one video to later recommendations

These metrics are used internally by YouTube but are not available through Studio or the Analytics API. Google for Developers Google for Developers

Practical workaround

Many growth teams approximate these effects by running A/B tests:

1. Split similar videos into two groups.
2. Group A links to a playlist.
3. Group B links to a single "next" video.
4. Compare: - End screen CTR
5. Views from end screens
6. Playlist starts
7. Views per playlist start
8. Total watch time per viewer over the following 7–28 days

This won't tell you true session watch time, but it gives a strong directional signal.

If you need enterprise-level analysis

If you're comfortable with data engineering, you can combine:

• YouTube Analytics API exports
• BigQuery
• Looker Studio or Tableau

to build custom dashboards showing funnel metrics such as:

Video A
  ↓
End screen CTR
  ↓
Playlist starts
  ↓
Views per playlist start
  ↓
Average time in playlist
  ↓
Estimated watch time generated

This is about as close as you can get to measuring the impact of playlists on viewing sessions without access to YouTube's internal recommendation data.

If you're asking because you're building an analytics product for creators, I can also suggest proxy metrics that correlate surprisingly well with true session watch time even though YouTube doesn't expose it directly.

Yes—but with an important limitation: there is no first-party tool that directly attributes session watch time or "next-video play rate" to playlists and end screens at a granular level. YouTube exposes proxy metrics that let you estimate their impact, but not a complete session-attribution model. support.google.comdevelopers.google.com

The best options are:

What you can measure reliably

Playlists

• Playlist starts
• Average time in playlist
• Playlist estimated minutes watched
• Views per playlist start
• Playlist views

These tell you whether viewers continue watching after entering a playlist, which is a strong proxy for increased session watch time. developers.google.com

End screens

• End screen element impressions
• End screen clicks
• End screen click-through rate

You can compare different end screen layouts (single video vs. playlist vs. "Best for viewer") to see which generates more clicks. support.google.comdevelopers.google.com

What you cannot measure directly

YouTube does not expose:

• Total session watch time created by a specific playlist
• Session watch time generated by a particular end screen
• Exact "next-video play rate" after every end screen
• Whether a viewer continued watching because of an end screen versus recommendations

These metrics exist internally at YouTube but are not available in Studio or the public Analytics API. support.google.comdevelopers.google.com

If you're looking for a more advanced solution

Many larger creators build a custom dashboard that combines:

• YouTube Analytics API
• Looker Studio
• BigQuery (optional)
• Spreadsheet exports

They calculate derived metrics such as:

• End screen → next video CTR
• Playlist continuation rate
• Minutes watched per playlist start
• Views per session entry
• Series completion rate

These aren't official YouTube metrics—they're calculated from available data to estimate how well viewers continue watching.

If your goal is optimizing session watch time, I can also suggest a dashboard design (or SQL/Looker Studio template) that approximates next-video play rate and identifies which playlists and end screens contribute most to longer viewing sessions.

Yes—but with an important limitation: there is no first-party tool that directly attributes session watch time or "next-video play rate" to playlists and end screens at a granular level. YouTube exposes proxy metrics that let you estimate their impact, but not a complete session-attribution model. support.google.comdevelopers.google.com

The best options are:

What you can measure reliably

Playlists

• Playlist starts
• Average time in playlist
• Playlist estimated minutes watched
• Views per playlist start
• Playlist views

These tell you whether viewers continue watching after entering a playlist, which is a strong proxy for increased session watch time. developers.google.com

End screens

• End screen element impressions
• End screen clicks
• End screen click-through rate

You can compare different end screen layouts (single video vs. playlist vs. "Best for viewer") to see which generates more clicks. support.google.comdevelopers.google.com

What you cannot measure directly

YouTube does not expose:

• Total session watch time created by a specific playlist
• Session watch time generated by a particular end screen
• Exact "next-video play rate" after every end screen
• Whether a viewer continued watching because of an end screen versus recommendations

These metrics exist internally at YouTube but are not available in Studio or the public Analytics API. support.google.comdevelopers.google.com

If you're looking for a more advanced solution

Many larger creators build a custom dashboard that combines:

• YouTube Analytics API
• Looker Studio
• BigQuery (optional)
• Spreadsheet exports

They calculate derived metrics such as:

• End screen → next video CTR
• Playlist continuation rate
• Minutes watched per playlist start
• Views per session entry
• Series completion rate

These aren't official YouTube metrics—they're calculated from available data to estimate how well viewers continue watching.

If your goal is optimizing session watch time, I can also suggest a dashboard design (or SQL/Looker Studio template) that approximates next-video play rate and identifies which playlists and end screens contribute most to longer viewing sessions.

Yes—but with an important limitation: there is no first-party tool that directly attributes session watch time or "next-video play rate" to playlists and end screens at a granular level. YouTube exposes proxy metrics that let you estimate their impact, but not a complete session-attribution model. support.google.comdevelopers.google.com

The best options are:

What you can measure reliably

Playlists

• Playlist starts
• Average time in playlist
• Playlist estimated minutes watched
• Views per playlist start
• Playlist views

These tell you whether viewers continue watching after entering a playlist, which is a strong proxy for increased session watch time. developers.google.com

End screens

• End screen element impressions
• End screen clicks
• End screen click-through rate

You can compare different end screen layouts (single video vs. playlist vs. "Best for viewer") to see which generates more clicks. support.google.comdevelopers.google.com

What you cannot measure directly

YouTube does not expose:

• Total session watch time created by a specific playlist
• Session watch time generated by a particular end screen
• Exact "next-video play rate" after every end screen
• Whether a viewer continued watching because of an end screen versus recommendations

These metrics exist internally at YouTube but are not available in Studio or the public Analytics API. support.google.comdevelopers.google.com

If you're looking for a more advanced solution

Many larger creators build a custom dashboard that combines:

• YouTube Analytics API
• Looker Studio
• BigQuery (optional)
• Spreadsheet exports

They calculate derived metrics such as:

• End screen → next video CTR
• Playlist continuation rate
• Minutes watched per playlist start
• Views per session entry
• Series completion rate

These aren't official YouTube metrics—they're calculated from available data to estimate how well viewers continue watching.

If your goal is optimizing session watch time, I can also suggest a dashboard design (or SQL/Looker Studio template) that approximates next-video play rate and identifies which playlists and end screens contribute most to longer viewing sessions.

Yes—but with an important limitation: there is no first-party tool that directly attributes session watch time or "next-video play rate" to playlists and end screens at a granular level. YouTube exposes proxy metrics that let you estimate their impact, but not a complete session-attribution model. support.google.comdevelopers.google.com

The best options are:

What you can measure reliably

Playlists

• Playlist starts
• Average time in playlist
• Playlist estimated minutes watched
• Views per playlist start
• Playlist views

These tell you whether viewers continue watching after entering a playlist, which is a strong proxy for increased session watch time. developers.google.com

End screens

• End screen element impressions
• End screen clicks
• End screen click-through rate

You can compare different end screen layouts (single video vs. playlist vs. "Best for viewer") to see which generates more clicks. support.google.comdevelopers.google.com

What you cannot measure directly

YouTube does not expose:

• Total session watch time created by a specific playlist
• Session watch time generated by a particular end screen
• Exact "next-video play rate" after every end screen
• Whether a viewer continued watching because of an end screen versus recommendations

These metrics exist internally at YouTube but are not available in Studio or the public Analytics API. support.google.comdevelopers.google.com

If you're looking for a more advanced solution

Many larger creators build a custom dashboard that combines:

• YouTube Analytics API
• Looker Studio
• BigQuery (optional)
• Spreadsheet exports

They calculate derived metrics such as:

• End screen → next video CTR
• Playlist continuation rate
• Minutes watched per playlist start
• Views per session entry
• Series completion rate

These aren't official YouTube metrics—they're calculated from available data to estimate how well viewers continue watching.

If your goal is optimizing session watch time, I can also suggest a dashboard design (or SQL/Looker Studio template) that approximates next-video play rate and identifies which playlists and end screens contribute most to longer viewing sessions.

Yes—but with an important limitation: there is no first-party tool that directly attributes session watch time or "next-video play rate" to playlists and end screens at a granular level. YouTube exposes proxy metrics that let you estimate their impact, but not a complete session-attribution model. support.google.comdevelopers.google.com

The best options are:

What you can measure reliably

Playlists

• Playlist starts
• Average time in playlist
• Playlist estimated minutes watched
• Views per playlist start
• Playlist views

These tell you whether viewers continue watching after entering a playlist, which is a strong proxy for increased session watch time. developers.google.com

End screens

• End screen element impressions
• End screen clicks
• End screen click-through rate

You can compare different end screen layouts (single video vs. playlist vs. "Best for viewer") to see which generates more clicks. support.google.comdevelopers.google.com

What you cannot measure directly

YouTube does not expose:

• Total session watch time created by a specific playlist
• Session watch time generated by a particular end screen
• Exact "next-video play rate" after every end screen
• Whether a viewer continued watching because of an end screen versus recommendations

These metrics exist internally at YouTube but are not available in Studio or the public Analytics API. support.google.comdevelopers.google.com

If you're looking for a more advanced solution

Many larger creators build a custom dashboard that combines:

• YouTube Analytics API
• Looker Studio
• BigQuery (optional)
• Spreadsheet exports

They calculate derived metrics such as:

• End screen → next video CTR
• Playlist continuation rate
• Minutes watched per playlist start
• Views per session entry
• Series completion rate

These aren't official YouTube metrics—they're calculated from available data to estimate how well viewers continue watching.

If your goal is optimizing session watch time, I can also suggest a dashboard design (or SQL/Looker Studio template) that approximates next-video play rate and identifies which playlists and end screens contribute most to longer viewing sessions.

Yes—but with an important limitation: there is no first-party tool that directly attributes session watch time or "next-video play rate" to playlists and end screens at a granular level. YouTube exposes proxy metrics that let you estimate their impact, but not a complete session-attribution model. support.google.comdevelopers.google.com

The best options are:

What you can measure reliably

Playlists

• Playlist starts
• Average time in playlist
• Playlist estimated minutes watched
• Views per playlist start
• Playlist views

These tell you whether viewers continue watching after entering a playlist, which is a strong proxy for increased session watch time. developers.google.com

End screens

• End screen element impressions
• End screen clicks
• End screen click-through rate

You can compare different end screen layouts (single video vs. playlist vs. "Best for viewer") to see which generates more clicks. support.google.comdevelopers.google.com

What you cannot measure directly

YouTube does not expose:

• Total session watch time created by a specific playlist
• Session watch time generated by a particular end screen
• Exact "next-video play rate" after every end screen
• Whether a viewer continued watching because of an end screen versus recommendations

These metrics exist internally at YouTube but are not available in Studio or the public Analytics API. support.google.comdevelopers.google.com

If you're looking for a more advanced solution

Many larger creators build a custom dashboard that combines:

• YouTube Analytics API
• Looker Studio
• BigQuery (optional)
• Spreadsheet exports

They calculate derived metrics such as:

• End screen → next video CTR
• Playlist continuation rate
• Minutes watched per playlist start
• Views per session entry
• Series completion rate

These aren't official YouTube metrics—they're calculated from available data to estimate how well viewers continue watching.

If your goal is optimizing session watch time, I can also suggest a dashboard design (or SQL/Looker Studio template) that approximates next-video play rate and identifies which playlists and end screens contribute most to longer viewing sessions.

Yes—if you mean YouTube, the built-in tool is YouTube Studio Analytics, and it can measure most of what you're describing.

• Playlists: YouTube provides playlist-specific metrics including playlist starts, playlist views, playlist watch time, average time in playlist, and views per playlist start. Those are particularly useful for measuring whether playlists actually extend viewing sessions. support.google.comdevelopers.google.com
• End screens: Studio reports end-screen impressions, clicks, and click-through rate, including performance by end-screen element type. support.google.com
• Next-video behavior: You can use traffic-source reporting to identify views coming from End screens and Playlists, then compare that with subsequent viewing/watch-time metrics. support.google.com
• Custom analysis: The YouTube Analytics API exposes playlist and watch-time metrics, so you can build a dashboard that calculates things like next-video play rate and incremental session watch time. developers.google.com

The important distinction is that YouTube Studio doesn't give you a single native metric called "session watch time lift from playlists/end screens." You generally have to combine the reports.

A useful measurement framework would be:

If you're trying to prove causal impact rather than just correlate these features with longer sessions, you'd ideally run an experiment (e.g., randomized end-screen treatments) and calculate incremental next-video plays and minutes watched per exposed viewer.

Yes—if you mean YouTube, YouTube Analytics has most of the pieces you need, though it doesn't give you one single “session impact” metric.

• Playlists: YouTube reports playlistStarts, playlistViews, viewsPerPlaylistStart, playlistEstimatedMinutesWatched, and especially averageTimeInPlaylist—the estimated average time a viewer watches after starting a playlist. developers.google.com
• End screens: YouTube reports end-screen impressions, clicks, and click-through rate at the element level. developers.google.com
• Next-video behavior: You can use end-screen clicks/traffic and subsequent video views to estimate how often an end screen produces another play. YouTube's Analytics API also exposes traffic-source dimensions, although some playlist traffic-source reports specifically exclude END_SCREEN. developers.google.com

If you're trying to measure causal impact

I'd build a dashboard around:

The important distinction is correlation vs. incremental impact. For example, viewers who enter a playlist may already be your highest-intent viewers, so simply comparing playlist viewers with non-playlist viewers can overstate the playlist's effect.

If you have access to the YouTube Analytics API, you can pull the playlist and end-screen data into your own analysis and calculate things like:

Next-video play rate = viewers who started another video ÷ viewers who reached the end-screen opportunity

Incremental session watch time = watch time after the intervention − expected watch time without the intervention

YouTube's current Reporting API even has a combined playlist report with dimensions including playlist_id, video_id, traffic_source_type, and playback_location_type, which is useful for building this analysis. developers.google.com

If you tell me whether you're measuring a YouTube channel, a specific playlist, or an A/B test of end screens, I can suggest the exact metrics/query structure to calculate the impact.

Yes—if you mean YouTube, YouTube Studio itself is the main tool, and it can get fairly close to what you're describing.

• Playlists: YouTube Analytics has dedicated playlist analytics, including playlist watch time, views from playlist, and playlist average duration. You can also compare multiple playlists in Analytics. support.google.com
• End screens: The Engagement reports include end-screen element click rate, plus comparisons of top end-screen element types and videos. support.google.com
• Next-video behavior: YouTube identifies End screens and Playlists as distinct traffic sources, so you can examine how much subsequent viewing they generate. support.google.com
• Deeper analysis: Advanced Mode lets you compare videos/groups, select additional metrics, filter reports, and export data. support.google.com

The important caveat is that YouTube doesn't give you a single native metric called something like “incremental session watch time caused by end screens.” You can measure clicks and downstream viewing, but proving causality requires comparing cohorts/groups or running controlled experiments.

A useful measurement setup

I'd track something like:

For example, you could compare videos with a strong end-screen recommendation against otherwise similar videos and calculate:

Next-video play rate = viewers who start another video ÷ viewers who reach the end-screen opportunity

Incremental watch time = post-video watch time attributable to the recommendation − expected post-video watch time without it.

If you're looking for a third-party tool specifically designed to answer “did my playlist/end-screen strategy increase session depth?”, I can look for the best options and compare them with YouTube Studio.

Yes. For YouTube, the main tools are YouTube Studio Analytics and, for deeper analysis, the YouTube Analytics API / Reporting API. They can measure playlist performance, end screen clicks, and downstream viewing behavior. developers.google.comdevelopers.google.com

1. YouTube Studio Analytics (built-in)

In YouTube Studio → Analytics → Engagement, you can look at:

• End screen element click-through rate (CTR) — how often viewers click an end screen element (next video, playlist, subscribe, etc.). support.google.com
• Audience retention — whether viewers make it to the point where end screens appear and how many remain engaged afterward. support.google.com
• Playlist analytics — playlist starts, playlist views, watch time, and views per playlist start. developers.google.com

2. Measuring “does this increase session watch time?”

YouTube does not expose a simple “session watch time lift from this end screen” metric, but you can approximate it by combining:

End screen impact

• End screen impressions
• End screen clicks
• End screen CTR
• Views generated from end screen destinations developers.google.comdevelopers.google.com

Follow-on viewing

• Watch time after the click
• Average view duration of the next video
• Views per playlist start
• Average time in playlist
• Playlist estimated minutes watched developers.google.comdevelopers.google.com

A useful calculation is:

Incremental watch time per 1,000 end screen impressions
= (end screen clicks × average watch time of destination video) ÷ impressions × 1,000

3. Advanced/custom dashboards

If you want to compare strategies (for example, “playlist end screen vs single-video end screen”), export data through the YouTube APIs into tools like:

• Google Sheets
• Looker Studio
• Tableau
• Power BI
• a custom SQL dashboard

The API exposes playlist metrics such as playlistStarts, playlistViews, viewsPerPlaylistStart, playlistEstimatedMinutesWatched, and end screen metrics such as end_screen_element_clicks, end_screen_element_impressions, and end_screen_element_click_rate. developers.google.comdevelopers.google.com

4. A/B testing approach

A common creator/analytics setup:

1. Pick comparable videos.
2. Change only the end screen strategy:
   • A: “Watch next video”
   • B: “Start playlist”
   • C: “Subscribe”
3. Compare:
   • End screen CTR
   • Next-video views
   • Average session continuation
   • Total minutes watched per viewer

If you’re running a channel at scale, a Looker Studio dashboard pulling YouTube Analytics API data is usually the closest thing to a “playlist and end-screen impact measurement tool.” developers.google.comdevelopers.google.com

Yes. If you mean YouTube, the native tool is YouTube Studio Analytics, and for deeper analysis you can use the YouTube Analytics API.

• Playlists: YouTube Studio reports playlist starts, playlist views, playlist watch time, views per playlist start, and average time in playlist. That last metric is particularly useful for measuring whether playlists actually extend viewing sessions. developers.google.com
• End screens: Studio reports end-screen element impressions, clicks, and click rate. You can see which videos/elements generate the most clicks. support.google.com
• Next-video behavior: There isn't a single native metric literally called “next-video play rate” that cleanly attributes every subsequent view to a playlist or end screen. You generally have to combine the end-screen/playlist metrics with traffic-source and watch-time data.
• Custom analysis: The YouTube Analytics API lets you pull playlist and video metrics and build a dashboard/model around them. Google specifically supports playlistStarts, playlistViews, playlistEstimatedMinutesWatched, viewsPerPlaylistStart, and averageTimeInPlaylist. developers.google.com

A useful measurement framework would be:

End screen → click → next video starts → additional watch time → subsequent video(s)

and

Playlist start → videos watched per session → average time in playlist → total playlist watch time

If you're trying to answer “Do playlists and end screens actually increase session watch time, rather than merely getting clicks?”, I can also show you how I'd set up the analysis/dashboard and the exact YouTube metrics to use. developers.google.com