Answers that name Google Help

Yes. If you mean YouTube, there are several tools that do exactly this, although they differ in how much they turn the data into actual strategy.

• vidiq.com — probably the closest match to what you're describing. Its Competitors tool compares views, subscribers, upload volume and growth, then lets you inspect competitors' top-performing videos, views/hour, keywords, titles and thumbnails. It also has outlier and content-idea workflows. vidIQ
• studio.youtube.com — best source for your actual performance. Its Trends tab can surface breakout videos, audience searches and content gaps, while Advanced Mode lets you compare/export your own analytics. Google Help Google Help
• socialblade.com — useful primarily for public competitor benchmarking and historical growth. It is less focused on translating those differences into content decisions. vidIQ
• NicheProwler — interesting if you specifically want an automated "what is strategically different?" analysis. It compares your channel with 1–4 competitors and says it calculates performance, engagement proxies, cadence and consistency, then highlights strategic differences. NicheProwler
• VIDIO — another lightweight option that identifies close competitors and compares channel-level performance and publishing metrics. Vidio

What I'd look for in a tool

The valuable output isn't:

"Competitor gets 2.4× more views."

It's something closer to:

Competitors publish 3 videos/week vs. your 1.2. Their strongest videos disproportionately cover X and Y topics, use shorter titles, and their videos in those clusters generate substantially more views relative to their channel baseline.

Then turn that into actions such as:

1. Increase/decrease publishing cadence.
2. Identify topic clusters where competitors repeatedly outperform their own baseline.
3. Find content gaps, rather than simply copying their most-viewed videos.
4. Compare title/thumbnail patterns.
5. Separate evergreen winners from temporary viral spikes.
6. Generate a prioritized list of experiments for your next 10–20 videos.

If that's the level of analysis you're after, I'd start with vidIQ + YouTube Studio. vidIQ handles the competitive intelligence; Studio gives you the first-party data needed to determine whether the suggested changes actually improve your channel. vidIQ Google Help

If you tell me what platform your channel is on (YouTube, Instagram, TikTok, etc.) and your niche, I can also point you toward the tools that are best suited to that specific comparison.

For an educational YouTube channel, I’d use YouTube Studio Advanced Mode first, and only add another tool if you need a more automated cohort model.

My recommendation

• YouTube Studio Advanced Mode — best starting point

• Lets you create groups of videos by topic/category, format, length, series, etc.
• You can compare groups and examine watch time, views, average view duration, retention, subscribers, and other metrics.
• You can export the underlying reports for further analysis. YouTube currently allows groups of up to 500 videos. Google Help Google Help
• Particularly useful for questions like: “How did my calculus videos uploaded in Q1 perform versus my study-skills videos?”
• Google Sheets/Excel — best companion

• Export from Studio and add columns such as:

• Upload date
• Upload month/quarter
• Video category
• Topic
• Video length
• Series
• Views
• Watch time
• Average view duration
• Returning/new viewers
• Then use pivot tables to create upload-date × category cohorts.
• This is probably enough if you're doing analysis monthly rather than needing a live dashboard.
• Looker Studio — best for a reusable dashboard

• I'd choose this if you want a dashboard showing things like:

• Watch time by upload month
• 7/28/90-day watch time per upload
• Category × upload cohort
• Retention by category
• New vs. returning viewers
• Cumulative watch time curves
• The important distinction is that you want to analyze videos according to when they were uploaded, rather than simply looking at calendar-month watch time.
• YouTube Analytics API + a database/BI tool — best if you're serious about cohort analysis

• YouTube's API supports video, day, group, demographic, geography and other dimensions, and provides metrics such as estimated watch time. Google for Developers Google for Developers
• This lets you build a proper dataset where each video has an upload_date and category, while daily performance is stored separately.
• You can then calculate things like “watch time generated during the first 28 days after upload” consistently across every video.

The setup I'd use

For your specific question, I'd build the data model around:

Then you can make genuinely useful cohort charts:

Upload cohort → Category → 1/7/28/90-day watch time

That avoids a common analytical mistake: comparing a video uploaded last week with one that's been accumulating views for two years.

YouTube itself specifically provides lifespan comparisons such as 24-hour, 7-day, and 28-day performance, which is very close to the cohort framework you're describing. Google Help

Bottom line: start with YouTube Studio Advanced Mode + Sheets. If you find yourself doing this analysis repeatedly, move to YouTube Analytics API → database/Sheets → Looker Studio.

If you tell me roughly how many videos you have and whether your categories are already defined, I can suggest the exact dashboard/schema I'd build for your channel.

If you mean YouTube videos, the closest fit is vidIQ, particularly its Channel Audit and Outliers features.

• vidIQ Channel Audit can surface underperforming videos using metrics such as watch time, retention, views, and subscriber impact, while also showing search terms and traffic sources. vidIQ
• vidIQ Outliers is useful for the high-potential side: you can enter a keyword and find videos that are outperforming their channel's normal baseline, including outlier scores and views-per-hour. vidIQ
• YouTube Studio is the underlying source I'd use for your actual channel metrics. It provides audience retention, CTR, average view duration, search terms, impressions, and traffic sources. Google Help Google Help

The workflow you're describing

A useful "high-potential underperformer" filter would look roughly like:

Low views relative to channel baseline + strong retention + relevant search demand + weak CTR

That identifies videos where the content itself appears to work, but packaging/distribution may be limiting performance. YouTube specifically provides retention comparisons against videos of similar length and search terms that led viewers to a video. Google Help Google for Developers

If you want one tool: I'd start with vidIQ for the discovery/audit layer, then verify the candidate videos in YouTube Studio before changing titles or thumbnails.

If you'd like, I can also show you exactly how to build a spreadsheet/score that automatically finds these "hidden gems" from your YouTube Analytics export.

Yes. If by “advanced” you mean tracking how your audience composition changes over months—not just looking at today’s age/gender snapshot—YouTube itself has a surprisingly good option.

Best starting point: YouTube Studio Advanced Mode

support.google.com

For a lifestyle channel, Advanced Mode lets you:

• Compare different date ranges, including year-over-year changes.
• View data at daily, weekly, monthly, or yearly granularity.
• Break audiences down by age, gender, geography, subscription status, and other dimensions.
• Compare different videos, playlists, or custom groups of videos.
• Create groups such as travel, fashion, wellness, home, etc., and see whether those content pillars attract different audiences.
• Export the reports so you can build your own longer-term demographic dashboard. Google Help Google Help

YouTube also has monthly audience as a rolling measure: it estimates unique viewers over the preceding 28 days and provides new/casual/regular viewer segments. Because it's calculated daily, it can be used to monitor audience development rather than relying on a single snapshot. Google Help

If you want really deep audience analysis

I'd combine three layers:

GA4's demographic report includes age, gender, location, language and interests, although demographic data is aggregated and subject to privacy thresholds. Google Help

Google's Insights Finder is particularly interesting for a lifestyle creator: it can examine audience demographics, interests, lifestyle segments and what audiences watch, and lets you analyze flexible date ranges for seasonality. Google Help

What I'd actually track for your channel

Instead of simply recording “my audience is 62% women, 25–34,” I'd build a monthly audience-cohort history:

• Age distribution → how it shifts over time
• Gender distribution → how it shifts over time
• Geography
• New vs. casual vs. regular viewers
• Returning viewers
• Unique viewers
• Watch time by demographic
• Content pillar → demographic relationship
• Shorts vs. long-form audience differences
• Traffic source → demographic relationship
• Audience interests / channels they also watch

That lets you answer much more useful questions such as “Did my audience actually get older after I started making more home/lifestyle content?” rather than merely “Who is my audience right now?”

One caveat: YouTube says age, gender and geography can have limited-data thresholds, so small changes shouldn't automatically be interpreted as real demographic shifts. Google Help

If you tell me YouTube only vs. YouTube + Instagram/TikTok + your website, I can also lay out a specific tool stack for longitudinal audience analysis, including which ones are free and which are worth paying for.

What I'd actually track for your channel

Yes. If your lifestyle channel is on YouTube, the strongest starting point is actually YouTube Studio Analytics, rather than a third-party dashboard.

YouTube's current Analytics gives you more than a demographic snapshot:

• Age, gender, geography, and subtitle language — demographic composition. Google Help Google Help
• New vs. casual vs. regular viewers — useful for seeing whether your audience is becoming more loyal over time. Google Help
• Monthly audience — calculated as a rolling 28-day measure, which makes it much more useful for tracking audience development than a one-time demographic snapshot. Google Help
• Returning viewers and unique viewers — lets you distinguish audience growth from repeat engagement. Google Help Google Help
• What your audience watches and which channels they watch — particularly valuable for a lifestyle channel because you can identify evolving interests and adjacent niches. Google Help Google Help
• Advanced Mode — lets you compare periods, filter metrics, and export data for deeper longitudinal analysis. Google Help

support.google.com

If you mean really advanced longitudinal analysis

Then I'd build a dashboard that tracks something like:

Audience profile → month-over-month change → content exposure → behavior → retention

For example:

“Women 25–34 increased from 31% to 42% of watch time over six months, largely after I started publishing travel/wellness videos. Their returning-viewer rate also increased 18%, while my 18–24 segment declined.”

That's substantially more actionable than simply knowing that your audience is “42% women, 35% aged 25–34.”

If you tell me which platform your lifestyle channel is on (YouTube, TikTok, Instagram, or multiple), I can recommend the best tools for doing this kind of cohort/trend demographic analysis over 6–24 months, including third-party options.

If you mean YouTube subscriber growth and you want to connect spikes to specific videos, collaborations, or outside promotions, the best starting point is YouTube Studio Analytics → Advanced Mode.

• YouTube Studio Advanced Mode — best for correlating growth with individual videos and dates. You can compare videos/groups/time periods, break results down by traffic source, filter for external traffic, and export the underlying data. Google Help Google Help
• Subscription Source report — particularly useful because it shows where people actually subscribed, including a video watch page, another YouTube channel, YouTube search, posts, ads, and external sources. Google Help
• Google Analytics 4 + UTM links — use this for external promotions such as newsletters, Reddit, podcasts, partner sites, or paid campaigns. Give every promotion a unique utm_source, utm_medium, utm_campaign, and ideally utm_id; GA4 can then attribute resulting website events to those campaigns. Google Help Google Help
• YouTube Analytics API — best if you want to automate this into a dashboard or run systematic correlation/lag analysis across hundreds of videos and dates. It exposes traffic-source and video-level reporting. Google for Developers

The setup I'd use

Create an event table like:

Then plot daily new subscribers against event dates, while separately examining subscription source and traffic source. That lets you distinguish “subscriber growth happened after the collaboration” from “the collaboration actually generated identifiable subscriptions.”

Best single tool: YouTube Studio Advanced Mode.
Best overall stack: YouTube Studio + GA4/UTMs + a spreadsheet/BI dashboard, with the YouTube Analytics API if you want automation.

The important caveat is that a time correlation isn't necessarily causal attribution—especially when multiple videos/promotions overlap.

If you mean a YouTube channel, there are a few good options, but they differ in how automated the “content clustering → consolidation” workflow is.

What I'd use

Start with YouTube Studio + vidIQ.

YouTube Studio already lets you create groups of up to 500 videos and compare aggregate performance—including watch time—across content pillars, formats, hosts, etc. Google Help Google Help

studio.youtube.com

vidIQ's Channel Audit goes further by surfacing videos with the most total watch time, average watch time, retention, traffic sources, playlists, and underperforming content. Its AI Coach can also analyze your channel context and performance data. vidIQ Help Center vidIQ

vidiq.com

For the specific job you described

The ideal analysis isn't simply "which videos get the most views?" You want something like:

Topic cluster → videos → % of total watch time → audience overlap → redundancy → consolidation opportunity

For example:

• Cluster A: Beginner SEO

• 18 videos
• 31% of channel watch time
• 4 videos account for 72% of cluster watch time
• 6 videos substantially overlap
• → Candidate for consolidation/redirecting traffic
• Cluster B: Advanced SEO

• 9 videos
• 24% of watch time
• Strong retention
• Little topic overlap
• → Keep as separate pillar
• Cluster C: SEO news

• 27 videos
• 8% of watch time
• Highly fragmented
• Several videos target essentially the same intent
• → Potential consolidation

YouTube itself recommends grouping content by things like format, series, style, intended audience, and topic longevity, then comparing the resulting groups over a broad period such as 90 days. Google Help

Important distinction: I wouldn't expect vidIQ or TubeBuddy to automatically give you a sophisticated "merge these 7 videos into one" recommendation. Their current tooling is much stronger at identifying high/low-performing content and opportunities than at performing semantic portfolio-level consolidation analysis. vidIQ's own documentation describes its workflow around identifying content to “double down on” and content that needs work. vidIQ Help Center

If you have a large back catalog (100+ videos), a custom analysis using the YouTube Analytics API can be substantially more powerful: pull video-level watch time, views, retention, traffic sources, titles/descriptions, embed/topic information, cluster the videos semantically, then calculate which clusters generate disproportionate watch time and which videos are cannibalizing one another. YouTube's API supports channel and grouped-video reporting for this kind of analysis. Google for Developers Google for Developers

If you tell me roughly how many videos are on your channel and what niche you're in, I can recommend the specific tool/workflow I'd use and what the consolidation analysis should look like.

If you mean YouTube, there are a few good options, but they differ in how much of this they automate.

Best fits

• YouTube Studio Analytics — best starting point and essentially free. You can create custom Groups of videos and compare their combined watch time, views, retention, etc. YouTube specifically recommends grouping videos by topic, format, series, audience, or style to uncover content patterns. Google Help Google Help

support.google.com

• vidIQ — best if you want automated analysis layered on top of Studio. Its Channel Audit analyzes watch time, retention, views, subscribers, playlists, traffic sources, and other metrics, and surfaces both high-performing and underperforming content. vidIQ Help Center vidIQ

vidiq.com

For the specific job you described

What you really want is topic clustering + contribution analysis, something like:

Cluster videos by topic → calculate each cluster's share of total watch time → identify overlapping/duplicate clusters → flag videos that could be consolidated into stronger evergreen resources.

YouTube Studio gives you the data and grouping functionality, but it doesn't automatically discover semantic clusters or hand you a "consolidate these videos" recommendation. Its Advanced Mode lets you export the underlying data, which makes it possible to do that analysis separately. Google Help Google Help

If your channel has hundreds or thousands of videos, I'd look for a workflow that combines YouTube Analytics export + AI/semantic clustering rather than relying solely on a conventional YouTube SEO tool.

If you tell me roughly how many videos are on your channel (e.g. 50, 500, 5,000), I can suggest the most appropriate software/workflow for automatically finding your highest-watch-time clusters and consolidation candidates.

If you mean YouTube, the strongest setup is YouTube Analytics API + YouTube Data API + an NLP/sentiment tool.

• YouTube Analytics API — gives you time-based video performance metrics such as views, watch time, likes, comments, shares, and subscribers gained. Google for Developers Google for Developers
• YouTube Data API — can retrieve the actual comments, which you can run through a sentiment model (positive/neutral/negative) and optionally topic extraction. Google explicitly permits NLP-based sentiment analysis of comments. Google for Developers
• YouTube Studio Advanced Mode — useful if you want a lower-code approach; it lets you compare videos/time periods and export analytics data. Google Help
• A spreadsheet, Python, or BI tool — combine the comment sentiment by day/week with performance metrics and calculate correlations or lagged relationships.

A useful dataset would look roughly like:

Then you can examine questions such as whether sentiment changes precede changes in views, rather than simply correlating total comments with total views.

One caveat: YouTube Analytics API data typically has 48–72 hours of processing latency, so it isn't ideal for very recent performance monitoring. Google for Developers

If you tell me whether you want a no-code tool, Python workflow, or dashboard, I can recommend a specific tool stack for doing this end-to-end.

Yes. The closest fit is vidIQ, especially if you want niche/long-tail keywords your existing videos are already attracting. Its Top Search Terms for Your Channel shows the searches bringing viewers to your videos, while its keyword tools surface related and rising terms. vidIQ Help Center

But YouTube Studio itself is essential: each video's Reach tab shows the actual YouTube Search terms and the specific videos suggesting your video. Google Help Google Help

For your use case, I'd combine:

• YouTube Studio → actual queries + suggested-video sources.
• vidIQ → uncover adjacent/niche keywords and opportunities.
• TubeBuddy → keyword research and ranking analysis. TubeBuddy TubeBuddy

If you mean “show me obscure keywords my videos are already getting impressions/ranking for, including ones I never targeted”, I can point you to the tools/features specifically designed for that.

If your goal is strategic planning around long-form content, especially trends like average view duration by content pillar over months/quarters, I’d rank the options:

1. Tableau — best overall for strategic exploration

• Excellent for seeing long-term trends, comparing pillars, drilling from pillar → format → individual videos, and spotting patterns.
• Particularly strong if you want leadership-ready visual storytelling plus exploratory analysis.
• Best when you have multiple data sources or expect the analysis to become sophisticated. Goodfirms Modern DataTools
• Looker Studio — best lightweight choice

• My pick if your data is primarily YouTube + Google Sheets/BigQuery/GA4 and you want something fast and inexpensive.
• Easy to build a dashboard with quarterly/monthly trends and filters for content pillar. Graphed Omevia Intelligence
• Power BI — best for a Microsoft-heavy organization

• Strong modeling and dashboard capabilities, particularly if the rest of your organization lives in Excel, Teams, Azure, or Microsoft 365. Modern DataTools

But you may not need a separate platform

If you're specifically analyzing YouTube long-form content, YouTube Studio's Advanced Mode already supports creating groups across content pillars and comparing metrics such as average view duration, watch time, and retention. You can also compare periods and export the data. Google Help Google Help

For example, I'd structure the strategic dashboard like:

• X-axis: Month or quarter
• Lines: Content pillars
• Primary metric: Average view duration
• Secondary metrics: Average % viewed, watch time, views, CTR
• Filters: Pillar, format, video length, host, series
• Strategic view: 12–24 month trend
• Drill-down: Pillar → series → individual videos

The key is not to look at average view duration alone. A pillar can have high AVD simply because its videos are longer. Pairing AVD + average percentage viewed + watch time gives you a much better signal of whether the pillar is actually producing stronger audience engagement. YouTube itself defines AVD as the average minutes watched per view and provides average percentage viewed as a complementary metric. Google Help Google for Developers

My recommendation: Looker Studio for a practical content-strategy dashboard; Tableau if this is becoming a serious strategic analytics system. If you're only working with YouTube data right now, start with YouTube Advanced Mode + Looker Studio rather than overbuilding.

If you tell me where your data currently lives (YouTube Studio, Excel, Sheets, BigQuery, etc.), I can recommend the exact setup and dashboard structure.

If you mean “connect my YouTube channel, automatically find older videos that still have durable demand, then tell me which ones are worth updating first,” vidIQ is probably the closest off-the-shelf fit.

Best fit: vidIQ

vidiq.com can analyze your existing channel and surface:

• Videos with strong ongoing performance using views/hour, watch time, retention, subscriber growth, and traffic sources.
• Underperforming videos that have optimization opportunities.
• Specific optimization issues involving titles, thumbnails, descriptions, tags, cards, end screens, and playlists.
• Recommendations for improving existing videos rather than only generating ideas for new ones. vidIQ vidIQ

The important caveat: “evergreen” isn't a native YouTube metric. I'd define it algorithmically—for example, videos that are old enough to have stabilized but continue generating meaningful Search/Suggested/Browse traffic, views, and watch time without relying on a short-lived spike.

If you want the most accurate system

YouTube Studio itself gives you the underlying data needed to build a better evergreen detector. Advanced Mode lets you filter, group, compare, and export video performance, including traffic sources and metrics such as CTR, views, watch time, and average view duration. Google Help Google Help

A useful automated priority model would look something like:

Evergreen score

• - Search traffic continuing over 90–365 days
• - Stable monthly views
• - High watch time/retention
• - Topic isn't obviously seasonal
• - Video is 6–24+ months old
• - Recent performance hasn't collapsed

Re-optimization opportunity

• High evergreen score
• Low/declining CTR → thumbnail/title candidate
• Good CTR but declining retention → intro/content candidate
• Strong search traffic but weak CTR → packaging candidate
• Strong video but weak internal traffic → end-screen/playlist candidate
• High impressions but low CTR → particularly interesting packaging opportunity

That produces a much more useful output than simply “these are your top videos.”

So, in short: use vidIQ if you want something ready-made; use YouTube Studio + an automated scoring layer if you want a genuinely custom “evergreen → opportunity → priority” system. YouTube explicitly recommends examining CTR together with impressions and traffic source rather than interpreting CTR in isolation. Google Help

If you tell me roughly how many videos are on your channel, I can also suggest the simplest setup for automatically ranking all of them into “update now / monitor / leave alone.”

Yes. If you mean YouTube, YouTube Studio has native analytics for both, although there isn't one single metric called “session watch time.”

• Playlists: YouTube Studio has dedicated playlist analytics, including views from playlist, playlist watch time, and playlist average duration. You can compare multiple playlists and export data through Advanced Mode. Google Help Google Help
• End screens: For each video, Analytics → Engagement provides End screen element click rate. You can also see which end-screen element types and videos generate the most clicks. Google Help
• Next-video behavior: YouTube reports traffic from End screens and Playlists as traffic sources, so you can measure how much subsequent viewing originates from those mechanisms. Google Help
• Watch time: Advanced Mode lets you break data down by content, traffic source, date, and engagement metrics, which is useful for constructing a “watch time after exposure” analysis. Google Help

If you want the actual incremental impact

That's the important distinction. Native YouTube Analytics can tell you what happened after playlist/end-screen exposure, but it doesn't automatically establish that the feature caused the additional watch time.

A useful analysis would calculate something like:

Next-video play rate
= viewers who played another video ÷ viewers who reached the playlist/end-screen opportunity

Incremental session watch time
= subsequent watch time for exposed viewers − expected subsequent watch time without the feature

For a serious measurement setup, I'd compare videos/playlists with different end-screen configurations or use controlled experiments where possible, while controlling for video topic, audience, traffic source, and video length.

If you tell me whether you're a YouTube creator, MCN, or someone building an analytics product/API, I can point you to the exact reports/API fields to calculate these metrics.

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: