Answers that name YouTube Studio

For what you’re describing—watch time and viewer cohorts grouped by upload date and video category—I’d use a small analytics stack rather than relying entirely on YouTube Studio.

My picks

• Best overall / easiest: YouTube Studio Advanced Mode

• It already lets you compare videos/groups/time periods, change breakdowns and metrics, filter, save reports, and export data. Google Help
• Great if you want to answer questions like: “How did videos uploaded in January perform over their first 30/60/90 days?”
• Downside: cohort analysis across your own custom categories gets cumbersome.
• Best for a serious custom dashboard: YouTube Analytics API + Looker Studio

• This is what I'd choose if your channel is large enough that you're making recurring content decisions from the data.
• The API exposes dimensions such as video, day/month, traffic source, geography, demographics, and device, plus watch-time metrics. Google for Developers
• You can maintain a spreadsheet/database containing: - Video ID
• Upload date
• Video category/topic
• Video length
• Series/course
• Then calculate days since upload and build cohort charts in Looker Studio.
• This gives you the most flexibility.
• Best turnkey option: Improvado

• It specifically supports custom segments such as video category and upload-date cohorts, and can combine YouTube data with other marketing/business data. Improvado
• I'd consider it if you don't want to build/maintain the data pipeline yourself.
• Probably overkill for a smaller creator.
• Interesting education-specific option: TubeAnalytics

• It is explicitly aimed at educational creators and emphasizes retention, CTR, subscriber conversion, and per-second retention analysis. TubeAnalytics
• Worth testing if your main question is which lessons/topics actually create valuable viewers, rather than just aggregate watch time.

What I'd actually build

For an educational channel, I'd structure the data around cohorts based on upload date, rather than just looking at lifetime video performance.

For example:

That lets you distinguish “this video has lots of watch time because it's old” from “this category generates watch time unusually quickly after publication.”

I'd also add:

• 7/30/90-day views
• 7/30/90-day watch hours
• Average percentage viewed
• Returning vs. new viewers
• Subscribers generated per 1,000 views
• Traffic source
• Video length
• Category/topic
• Upload month/quarter
• Evergreen vs. timely content

YouTube itself provides new/casual/regular viewer reporting, and its Audience/Engagement areas expose useful watch-time and retention information. Google Help Google Help

My recommendation

If you're under ~100 videos: start with YouTube Studio Advanced Mode + a Google Sheet.

If you're at 100–1,000+ videos or publish consistently: build YouTube Analytics API → database/Google Sheets → Looker Studio. That's the setup I'd recommend for your specific cohort question.

If you want it turnkey and don't mind paying: investigate Improvado or TubeAnalytics.

One important wrinkle: YouTube's analytics API gives you activity date and video ID, so the useful cohort dimension—“days since this video was uploaded”—is something you'll generally create by joining analytics data to each video's upload date. Google for Developers Google for Developers

If you tell me roughly how many videos you have, how often you upload, and what you mean by “category” (YouTube's built-in categories vs. your own topics like Algebra/Physics/Calculus), I can recommend the exact stack and dashboard structure I'd use.

For an educational YouTube channel, you’ll usually want a tool stack that can answer questions like:

• “Do biology tutorials uploaded in 2025 retain viewers better than exam-prep videos?”
• “Which upload cohorts produce the most watch time after 30/90/180 days?”
• “Are newer viewers becoming returning viewers?”
• “Which categories create subscribers, not just views?”

Here are the best options depending on how advanced you want to get:

My recommended setup for an educational channel

1. Start with YouTube Studio Advanced Mode

• Create video groups such as:
  • Algebra
  • Physics
  • Study skills
  • Exam preparation
  • Beginner vs advanced lessons
• Compare:
  • Watch time
  • Average view duration
  • Returning viewers
  • Subscribers gained
  • Traffic source

YouTube Studio’s Advanced Mode supports comparisons, groups, breakdowns, metrics, filters, and exports. support.google.com

2. Build a cohort dashboard in Looker Studio
A useful schema:

Key charts:

• Watch hours by upload month
• Median watch time per video by category
• Subscriber conversion by topic
• Retention curve by category
• “Evergreen score” (watch time after 90+ days)

YouTube’s reporting APIs expose metrics such as estimated minutes watched, average view duration, views, and subscribers gained, which can feed custom dashboards. developers.google.com

3. Add a spreadsheet/database layer if you have many videos
For 100+ uploads:

• Export YouTube Analytics data weekly
• Store snapshots in:
  • Google Sheets (simple)
  • BigQuery (advanced)
  • PostgreSQL (technical teams)

Then analyze:

• “Videos uploaded in Q1 2026”
• “Beginner lessons vs advanced lessons”
• “First 30 days vs lifetime performance”

If you want the simplest good workflow:

YouTube Studio → Google Sheets → Looker Studio

That gives you most of what a creator needs without paying for enterprise software.

If your channel is large (100k+ subscribers)

I’d consider:

• BigQuery + Looker Studio for custom cohort modeling
• Tableau/Power BI if you have a data analyst
• A creator analytics platform if you need competitor benchmarking

One note: YouTube’s built-in Audience reports already separate viewer behavior into groups like new, casual, and regular viewers, which is useful for measuring whether educational content is building a repeat audience. support.google.com

If you tell me your channel size (e.g., 10k vs 500k subscribers) and whether you categorize videos manually (topics/playlists/tags), I can suggest a more specific stack.

If you mean YouTube videos that are underperforming on views but have strong retention and keyword potential, I’d use TubeBuddy first.

• tubebuddy.com — best fit for this exact workflow. Its Retention Analyzer can surface underperforming videos by retention, while Keyword Explorer/Search Explorer evaluates search volume, competition, and keyword opportunities. TubeBuddy TubeBuddy TubeBuddy
• vidiq.com — strong alternative. Its Channel Audit specifically identifies low-performing videos and examines watch time/retention, while its Keyword Research tool scores search volume and competition. vidIQ Help Center vidIQ Help Center
• YouTube Studio — best free source of truth. You can combine impressions, CTR, average view duration, average percentage viewed, search terms, and retention to find videos with good content quality but weak packaging/discovery. Its Trends tab can also reveal content gaps. Google Help Google Help

The pattern you're looking for

A particularly valuable candidate is:

Low views + high impressions + low/moderate CTR + strong retention + relevant/high-demand keyword

That often means the video itself is working, but the title/thumbnail/keyword targeting isn't, making it a good candidate for optimization rather than creating a new video from scratch.

If you give me your YouTube channel URL, I can also show you exactly how I'd score your videos for “hidden potential” using views, CTR, retention, and keyword opportunity.

For your specific use case, I’d recommend tubebuddy.com.

Why it fits:

• Tracks thumbnail A/B tests: You can test thumbnail variants and see CTR, impressions, clicks, views, watch time, and other results for A vs. B. TubeBuddy TubeBuddy
• Keeps testing context: Its A/B-testing workflow gives you a results dashboard, making it easier to compare what worked across experiments rather than treating each test as an isolated decision. TubeBuddy
• Built for channel management: It can work across your existing video library rather than just helping you design individual thumbnails. vidIQ
• Useful for building a thumbnail playbook: Over time, you can identify patterns in the visual styles that consistently produce better CTR for your audience.

One caveat: TubeBuddy's A/B tests alternate versions based on YouTube Analytics' 24-hour data blocks, so it's not a perfectly simultaneous split test. TubeBuddy Help Center

If you mainly want free testing, YouTube Studio's native A/B Testing is worth starting with. Eligible creators can test up to three thumbnail/title combinations, though YouTube evaluates the winner primarily by watch-time share, rather than simply highest CTR. vidIQ

So I'd rank them:

1. TubeBuddy — best match for CTR + ongoing A/B-test tracking/history.
2. YouTube Studio — best free/native option.
3. vidIQ — excellent for channel research and optimization, but it currently doesn't offer its own dedicated A/B-testing tool. vidIQ

If your priority is specifically “show me every thumbnail I've tested, the CTR lift, winner, test duration, and let me spot patterns across 50–500 videos,” I can also find the best tool specifically for that historical-analysis workflow.

If you mean YouTube, I’d pick TubeBuddy for your specific use case.

• TubeBuddy — best fit if you want to actively A/B test thumbnails and connect that testing to channel performance. Its Thumbnail Analyzer supports thumbnail A/B tests and CTR-oriented analysis, while its broader tools are designed around managing and evaluating your video library. TubeBuddy TubeBuddy

tubebuddy.com

• Viewstats — better if by “A/B test history” you mean a research library of tests. It catalogs observed thumbnail tests, test dates, variants, test counts, and performance context, including other channels' tests. It doesn't run tests on your own uploads. Viewstats

viewstats.com

• YouTube Studio — use the native A/B Testing / Test & Compare feature if you're eligible. It can test up to three thumbnail variants and determines the winner using watch-time share rather than simply CTR. vidIQ Google Help

My recommendation: TubeBuddy + YouTube Studio if you want an ongoing workflow for your own channel; add Viewstats if studying historical thumbnail tests from successful channels is important.

One caveat: if your priority is specifically a single historical dashboard showing every thumbnail version, its CTR over time, and your A/B-test results across the entire channel, none of these is a perfect all-in-one solution. I'd prioritize the native YouTube data and use a tool that preserves your test history rather than relying on screenshots/spreadsheets.

Yes. If by “over time” you mean tracking how the makeup and behavior of your audience changes, rather than just seeing “women 25–34 = 32%” today, there are better options.

Best fit: YouTube Studio Advanced Mode

For a lifestyle channel on YouTube, YouTube Studio’s Advanced Mode is the first tool I’d use. It lets you:

• Compare demographics across different time periods
• Break performance down by age, gender, geography, audience, content, traffic source, etc.
• Compare individual videos, groups of videos, or periods
• Look at new vs. casual vs. regular viewers
• Switch between daily, weekly, monthly, and yearly views
• Export the underlying report data for deeper analysis support.google.com

That makes it possible to answer questions like:

“Has my audience shifted toward 25–34 women over the past 12 months?”
“Did my audience composition change after I started making travel content?”
“Which demographic is becoming more loyal, rather than simply generating one-off views?”

YouTube Analytics / Advanced Mode guide

For genuinely advanced longitudinal analysis: GA4

If your channel also drives people to a website, newsletter, store, or other owned property, Google Analytics 4 is considerably more powerful for longitudinal analysis.

Its Explorations include:

• Cohort analysis
• Segment overlap
• User lifetime analysis
• Funnels
• Path exploration
• Custom audience segments

Cohort Exploration specifically lets you follow groups of users over time and break those cohorts down by dimensions. support.google.com

Google Analytics Explorations documentation

The setup I'd recommend for a lifestyle creator

Use YouTube Analytics as the source of truth for your YouTube audience, then export periodically and build a longitudinal dataset with dimensions such as:

The particularly useful metric is new → casual → regular viewer progression. YouTube explicitly provides these audience groups, and changes in casual/regular viewers can indicate that you're developing a more loyal audience rather than merely getting additional reach. support.google.com

One caveat: demographic data can be limited or thresholded, so you shouldn't interpret small demographic movements as precise measurements. support.google.com

If you're looking for something more sophisticated than YouTube's built-in reports—e.g. an actual “audience evolution dashboard” that detects demographic shifts, identifies emerging audience segments, and correlates those shifts with your content over 6–24 months—I can recommend a few tools/setups specifically for that use case.

If your goal is specifically “what caused this subscriber spike?”, I’d start with YouTube Studio/YouTube Analytics, then add a lightweight attribution layer for external promotions.

Best overall: YouTube Analytics + a simple event log

YouTube Analytics already gives you unusually useful attribution:

• Subscriber source — including the video watch page, other YouTube channels, external sources, ads, posts, etc. Google Help
• Subscribers gained/lost by video and over time via Analytics/API. Google Developers
• Traffic-source details — including external websites, related videos, other channels, ads, and search terms. Google Developers Google Help

The missing piece is usually context: “Collab with Creator X,” “Reddit promotion,” “podcast appearance,” “newsletter mention,” etc. Put those events into a simple spreadsheet/database with:

date/time | event | video | collaborator/promotion | URL | campaign

Then overlay those events against daily subscribers gained, views, and traffic sources.

If you want a dedicated tool

Precision looks particularly relevant if you want a dashboard rather than building the analysis yourself. It pulls YouTube channel/video performance, traffic sources, and subscriber activity and explicitly focuses on identifying which videos drive new subscribers. Precision

For a creator/team doing serious analysis, I'd rank the options:

1. YouTube Analytics + Google Sheets — best value and surprisingly powerful.
2. Precision — best if you want automated dashboards and cross-metric analysis.
3. Custom YouTube Analytics API + event database — best if you need rigorous attribution across many collaborations/promotions.
4. vidIQ — better for content/competitive intelligence than for precise causal attribution. vidIQ

Important distinction: none of these can perfectly prove that a collaboration caused 2,000 subscribers. They can establish strong temporal/source evidence. For external campaigns, use unique URLs/UTMs or promo codes where possible so you can distinguish correlation from actual attributable conversions.

If you tell me whether this is YouTube, TikTok, Instagram, or a newsletter, I can recommend the best specific stack and show you exactly how I'd structure the attribution dashboard.

For a creator/team doing serious analysis, I'd rank the options:

If you mean YouTube, there isn’t one mainstream tool that perfectly does “cluster my entire channel → attribute watch time to each cluster → tell me which videos to consolidate.” But a few come close.

Best options

• YouTube Studio Advanced Mode — best starting point, free. You can create groups of up to 500 videos, compare their combined performance, and export the underlying data. This is the strongest source of truth for identifying which topic clusters actually drive watch time. Google Help

studio.youtube.com

• vidIQ — best for automated channel diagnosis. Its Channel Audit surfaces total watch time, average watch time, retention, top-performing content, playlists, search terms, and “content to double down on.” It doesn't appear to have a dedicated “consolidate these overlapping videos” feature, though. vidIQ Help Center vidIQ

vidiq.com

• TubeBuddy — best for deeper optimization workflows. Its analytics suite includes retention analysis, video-level performance, opportunity finding, and channel analytics. It is useful for finding weak/overlapping content, but again, consolidation isn't its core automated recommendation. TubeBuddy

tubebuddy.com

• TubeAnalytics — worth looking at if your priority is strategic channel analysis. Its positioning is specifically around authenticated channel analysis, retention curves, competitor benchmarks, and decision support, rather than primarily SEO/keyword optimization. TubeAnalytics TubeAnalytics

tubeanalytics.net

What I'd use for your specific goal

I'd use YouTube Studio + an analysis layer rather than choosing a tool purely on its audit score.

The workflow I'd want is:

Videos → semantic/topic clusters → watch time per cluster → % of channel watch time → overlapping videos → consolidation candidates

For example:

The key consolidation signal isn't simply “low views.” I'd look for videos that target essentially the same viewer/search intent, where one video substantially outperforms the others in watch time and retention. Those are the strongest candidates to merge into a definitive resource, update the winner, and redirect/internal-link the weaker videos.

If you want, I can also recommend the best tool specifically for this workflow based on your channel size and whether you want an automated AI cluster analysis or are willing to upload/export your YouTube Analytics data.

If your goal is specifically “which topic clusters generate the most watch time, and which videos should I consolidate/retire because they overlap?”, I’d shortlist these:

My recommendation

Start with YouTube Studio + vidIQ. YouTube itself lets you create custom groups of up to 500 videos and compare aggregate performance, including watch time, so you can establish the actual winners rather than relying on third-party estimates. support.google.comkeywordsrocket.comsupport.google.com

vidIQ Channel Audit is probably the easiest commercial tool for the job. Its audit surfaces watch time, retention, subscriber growth, top content, playlists, traffic sources and underperforming videos, which gives you the inputs needed to identify clusters worth expanding or pruning. support.vidiq.com

For actual content-cluster discovery, KeywordsRocket is interesting because it explicitly analyzes content themes/clusters and breakout videos rather than focusing primarily on SEO. keywordsrocket.com

For consolidation, I'd favor a tool/framework that can answer something more specific than “these videos overlap”:

Cluster A generates 42% of channel watch time from 18% of videos → consolidate 7 overlapping videos into 2–3 definitive videos, redirecting the weaker videos' traffic via descriptions/end screens.

That's a more useful decision than simply finding similar titles.

One important point: YouTube Studio is especially valuable right now because its Advanced Mode lets you compare groups and export the underlying data. support.google.comkeywordsrocket.comsupport.google.com

If you give me your YouTube channel URL, I can also analyze the channel structure and tell you what clusters I'd consolidate, which I'd double down on, and which individual videos are redundant.

Yes. For YouTube specifically, there are tools that can help surface long-tail/niche keywords your existing videos are already appearing for, plus opportunities from Suggested Videos and related content.

The closest matches:

• vidiq.com — Good for discovering keywords, tracking rankings, and finding content opportunities. It can help identify search terms around your niche and suggest topics based on your channel data. vidIQ vidIQ
• tubebuddy.com — Strong for YouTube SEO workflows: keyword discovery, rank tracking, competitor analysis, and optimization suggestions. TubeBuddy TubeBuddy
• search.google.com — If your videos are indexed in Google, it can reveal actual search queries driving impressions/clicks (more useful for Google search than YouTube Suggested).
• studio.youtube.com — The native source. Go to: - Analytics → Reach → YouTube Search terms (what people typed)
• Analytics → Reach → Traffic source: Suggested videos (which videos are sending viewers)
• Advanced mode → filter by video to compare winners

For the specific thing you described — “what weird/niche phrases are my recent videos already ranking for?” — I’d look for a workflow like:

1. Export your last 10–50 videos from YouTube Studio.
2. Put the videos into vidIQ/TubeBuddy rank tracking.
3. Look for: - keywords where you rank positions ~5–30 (easy wins)
4. unexpected long-tail phrases with low competition
5. suggested-video neighbors that repeatedly appear
6. Make follow-up videos targeting those exact clusters.

A more advanced option is to analyze your own channel data with the YouTube Analytics API and build a keyword map from:

• search terms
• suggested video sources
• retention by traffic source
• subscriber conversion by query

One caveat: YouTube does not expose a complete list of every keyword your videos rank for, so third-party tools are estimates or based on tracked keywords. The most accurate data about your actual viewers comes from YouTube Studio. vidIQ TubeBuddy

If you mean YouTube specifically, TikTok, or Google Search video results, the best tool stack changes.

Yes — but there’s an important distinction between “keywords my videos are actually getting found for” and “keywords I might rank for.”

Best option: YouTube Studio itself

For your actual recent videos, YouTube Studio gives you the first-party data:

• YouTube Search terms — the specific searches that led viewers to each video.
• Suggested videos — the specific videos that were recommending your video.
• Both are available under a video's Analytics → Reach reports. Google Help Google Help

YouTube does limit/omit very low-volume search terms, so you won't necessarily get every query someone typed. Google Help

If you want a tool that surfaces the niche opportunities automatically

vidIQ is probably closest to what you're describing. Its Top Search Terms for Your Channel identifies terms actually bringing viewers to your videos, while its keyword tools surface related/niche terms, search volume, competition, and rising searches. vidIQ Help Center vidIQ

It also has a rank checker where you can give it a video + keyword and see where that video currently appears in YouTube search. vidIQ

vidiq.com

The really interesting workflow

If your goal is “find weird/niche keywords my recent videos are already accidentally winning on”, I'd do:

Recent videos → actual Search terms → filter for low-volume/long-tail queries → compare which ones generated disproportionately good views/watch time → identify related terms → check rankings → make follow-up videos around the winners.

For Suggested, use the videos appearing in Content suggesting this video as a separate signal. That can reveal a different kind of niche: the topics/videos YouTube thinks your content belongs beside, rather than what people explicitly searched. Google Help

YouTube's own Trends tab is also useful for expanding those discoveries into related searches and emerging topics. Google Help

If you tell me what kind of videos you make, I can point you to the tool/workflow that's best for finding those unexpected long-tail rankings, rather than generic keyword research.

If you mean a tool that connects to your YouTube channel, identifies videos that continue attracting views, and then tells you what to optimize first, I’d look at these:

• YouTube Studio — best first-party option. Its Analytics can surface older videos that are still gaining traction and lets you evaluate traffic sources, CTR, retention, and watch time. YouTube specifically recommends looking for patterns in topic longevity and individual video performance. Google Help Google Help
• vidiq.com — probably the best fit if your goal is re-optimization. It combines channel auditing with keyword/search analysis, title recommendations, and competitive benchmarks. OverseerOS vidIQ
• tubetoolkit.com — specifically advertises channel-connected analytics that identify rising videos and evergreen content, making it a close match to your wording. TubeToolkit
• trendsiq.io — more focused on identifying evergreen topics using sustained search demand and decay rate, rather than auditing your existing videos. TrendsIQ

My pick

For an automated workflow of “find my evergreen videos → rank the opportunities → tell me what to change”, I'd start with vidIQ + YouTube Studio.

A useful priority model is:

Evergreen potential × current traffic × search opportunity × optimization gap

For example, an old video with steady search traffic but a mediocre CTR is a much higher-priority re-optimization candidate than an old video that's already performing exceptionally well. YouTube confirms that changing a title or thumbnail can change how viewers respond and therefore potentially improve performance, although it recommends not changing what's already working. Google Help

If you want, I can also identify the tool that does the most automation specifically—i.e., automatically produces a ranked “optimize these 10 videos first” list rather than just giving you analytics.

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.

If you mean a YouTube channel, I’d pick vidIQ for what you described.

Its Channel Audit specifically surfaces:

• SEO/metadata issues across titles, descriptions, tags, thumbnails, cards, end screens, and playlists.
• Your top search terms and traffic sources.
• Underperforming videos and retention problems.
• Content you should double down on based on actual channel performance. support.vidiq.com

More importantly for your goal, vidIQ's keyword research estimates search volume and competition, while its optimization tools let you identify weak videos and prioritize improvements. support.vidiq.com

For “prioritized by potential traffic lift”

I'd use vidIQ + YouTube Studio, rather than trusting an SEO score alone. vidIQ's scores are useful, but its SEO score partly reflects factors you can't immediately control, such as existing video performance. support.vidiq.com

A good prioritization model is:

Potential traffic lift ≈ search demand × ranking opportunity × current impressions gap × expected CTR improvement

That lets you rank fixes like:

1. High opportunity: Existing video already gets search impressions but ranks/CTR poorly → optimize title/thumbnail first.
2. High opportunity: You rank for related terms but not the higher-volume variant → retarget/update the video.
3. Medium: Strong topic with weak description/metadata → optimize metadata.
4. Low: Cosmetic SEO issues with little realistic traffic impact → fix later.

TubeBuddy is a good alternative if you have a large existing library and want bulk metadata changes/testing; its emphasis is more on channel management and testing than vidIQ's research/growth workflow. vidiq.com

If you give me your YouTube channel URL, I can also help you build a traffic-lift-prioritized audit framework—e.g., issue → affected videos → estimated opportunity → effort → priority.