Answers that name a competitor

Yes. If you mean YouTube, there are several tools that do this, but they differ in how actionable the output is.

The strongest fit: vidIQ

vidiq.com has a dedicated Competitors feature that lets you compare your channel with competitors across views, subscribers, publishing activity, daily growth, and top-performing videos. You can also sort competitor videos by views, views/hour, and recency to identify content that is currently working. vidIQ Help Center VidIQ

More importantly, vidIQ now offers an MCP integration, which can connect its YouTube data to AI tools and let you ask questions such as:

• "Compare my channel growth against my top 3 competitors."
• "Why did my last 5 videos underperform?"
• "What topics are trending in my niche that I haven't covered?"
• "Find the content patterns behind my competitors' breakout videos."

vidIQ vidIQ Help CenterThat is pretty close to the "show me the differences and tell me what to do about them" workflow you're describing.

Other useful options

• Social Blade — good for historical public benchmarking: subscriber/view growth, upload patterns, rankings, etc. It is more of a measurement tool than a strategy tool. vidIQ
• TubeBuddy — useful for competitor monitoring, SEO, testing, and managing your existing channel. vidIQ
• VIDIO — specifically offers side-by-side competitor analysis, including subscribers, lifetime views, publishing volume, and competitive distributions. Vidio.ai Vidio.ai
• YouTube Studio — still the source I'd use for your actual private performance data (CTR, retention, revenue, audience, etc.), since competitor tools generally only have access to competitors' public data. vidIQ

What I'd actually want the tool to produce

Rather than simply giving you a dashboard saying "Competitor A gets 2× your views," the useful analysis is something like:

The important caveat is that competitor public data can't tell you their CTR, retention, or revenue, so those should not be treated as known facts. The strategy layer has to infer patterns from things like topics, titles, thumbnails, views, upload frequency, and video velocity.

If you tell me what platform your channel is on (YouTube, TikTok, Instagram, etc.) and what niche you're in, I can point you toward the tool/workflow that best matches what you're trying to accomplish.

Yes. There are several tools that do this, especially for YouTube, and the better ones go beyond subscriber/view comparisons by surfacing content gaps, winning topics, formats, upload patterns, and packaging differences. vidIQ NicheProwler

Here are the main options:

For actionable strategy differences, the analysis you want should answer questions like:

• Topic gaps: “Competitors get traction from videos about X, but we rarely cover it.”
• Format gaps: “Competitors’ top videos are 8–12 minute tutorials, while ours are mostly short commentary.”
• Packaging gaps: “Their titles use problem/solution framing; ours use generic descriptions.”
• Cadence gaps: “They publish 3× weekly and concentrate uploads around certain themes.”
• Audience gaps: “They attract viewers through beginner content, while we mostly publish advanced content.”
• Outlier videos: “These 5 competitor videos dramatically outperform their average—what pattern do they share?”

A practical workflow:

1. Pick 5–10 competitors with a similar audience (not just the biggest channels).
2. Pull their last 30–100 videos.
3. Compare:

• views relative to channel size
• topic clusters
• titles/thumbnails
• upload frequency
• video length
• recurring formats
• Turn the differences into experiments:

• “Test this topic”
• “Try this format”
• “Change title framing”
• “Create a better version of this underserved idea”

If you tell me your platform (YouTube, TikTok, Instagram, podcast, newsletter, etc.) and your channel size/niche, I can suggest the best tool stack and what metrics to compare.

For an educational YouTube channel, you’ll probably want something that can answer questions like:

• “Do videos uploaded in 2025 retain viewers better than 2024 videos?”
• “Which course/topic categories generate the most watch hours?”
• “Do beginner tutorials create more returning viewers than advanced lessons?”
• “How long does it take a new upload to reach its first 1,000 watch hours?”

Here are the best options depending on how deep you want to go:

Best overall: YouTube Studio Advanced Mode (free)

studio.youtube.com is the starting point. It already has the underlying data you need: watch time, retention, viewers, returning viewers, traffic sources, and video-level comparisons. Advanced Mode lets you compare videos/groups, change breakdown dimensions, add metrics, filter, and export reports. CCNews

Good for:

• Cohorts by upload date
• Comparing playlists/courses
• Watch time by video
• Retention curves
• Subscriber vs non-subscriber behavior

Limitation: It is not a true cohort database. You’ll likely export data and build your own views.

Best for a serious creator team: Looker Studio + YouTube Analytics API

lookerstudio.google.com is probably the best fit if you want dashboards like:

A common setup:

1. Export YouTube Analytics data daily.
2. Store it in Google Sheets, BigQuery, or a database.
3. Add custom fields: - Upload month
4. Course/topic category
5. Difficulty level
6. Video format
7. Instructor
8. Visualize in Looker Studio.

This gives you the closest thing to a Netflix-style content analytics system.

Best no-code creator analytics tools

TubeBuddy

Good if you mainly want optimization:

• Thumbnail/title testing
• SEO
• Video comparisons
• Publishing workflow

Less suited for deep cohort analysis.

vidIQ

Good for:

• Topic research
• Competitive analysis
• Identifying content opportunities

Less focused on internal watch-time cohorts.

Best if you want a data warehouse approach

For a channel with hundreds/thousands of lessons:

• fivetran.com + BigQuery + Looker Studio
• getdbt.com for transforming data

This lets you create tables like:

video_cohort_performance

There are also open-source data models for YouTube analytics that structure video performance, demographics, and metadata for analysis workflows. GitHub

My recommendation for an educational channel

Small/medium channel (<500 videos):

1. YouTube Studio Advanced Mode
2. Export monthly
3. Google Sheets + Looker Studio dashboard

Growing channel (500–5,000 videos):

1. YouTube Analytics API
2. BigQuery
3. Looker Studio
4. Add custom tagging for: - subject
5. course
6. learner level
7. exam/topic

Large education business:

• Data warehouse + BI stack (BigQuery/Snowflake + Looker/Tableau)

The key thing: YouTube’s native categories are usually too broad for education. You’ll get much better insights by creating your own taxonomy (for example: “Calculus → Limits → Beginner” or “Python → Data Analysis → Intermediate”) and analyzing cohorts around that. CCNews

If you tell me your channel size (number of videos, monthly watch hours, and whether you have courses/playlists), I can suggest a more specific stack.

If your main requirement is “track thumbnail CTR + keep a history of A/B tests across my whole channel,” I’d look at TubeBuddy first.

• tubebuddy.com — strongest fit for an ongoing testing workflow. Its A/B testing can alternate thumbnail/title/other metadata variants and lets you review the resulting analytics, rather than treating each experiment as an isolated event. TubeBuddy Help Center vidIQ
• YouTube Studio A/B Testing — best if you want the native, first-party option. Eligible creators can test up to three thumbnail variants, with YouTube evaluating them primarily by watch-time share, not simply CTR. vidIQ
• tubeanalytics.net — worth considering specifically for historical channel-level analysis: comparing CTR, retention, and performance before/after thumbnail changes. TubeAnalytics
• viewstats.com — useful if you also want to study thumbnail changes and A/B-test patterns across other channels, but its A/B library isn't a system for testing your own uploads. vidIQ

What I'd choose

For your exact use case, TubeBuddy + YouTube Studio is the combination I'd investigate: use YouTube's native experiments where available, while keeping a more structured testing/analytics workflow in TubeBuddy.

One important distinction: CTR alone isn't necessarily the best test metric. YouTube's native system uses watch-time share because a thumbnail can generate more clicks while attracting viewers who leave quickly. vidIQ

If you tell me roughly how many videos you publish per month and whether you're testing thumbnails on old videos or new uploads, I can narrow this down to the most suitable tool/workflow.

If you mean YouTube, I’d look at TubeBuddy for the workflow you described.

• A/B testing: TubeBuddy can test thumbnail variations and other metadata, alternating versions over time and reporting the associated analytics. TubeBuddy Help Center
• Test history: It’s geared toward managing repeated tests across an existing video library, which makes it more suitable if you want to build a record of what thumbnail styles have worked on your channel. vidIQ
• CTR tracking: YouTube Analytics remains the source for your actual impressions CTR; TubeBuddy can help organize the testing workflow around those metrics. Note that TubeBuddy's tests are sequential rather than YouTube's concurrent experiment. TubeBuddy Help Center Google Help

One important alternative

YouTube Studio itself now has native A/B testing for eligible long-form videos. You can test up to 3 thumbnails/titles simultaneously, and YouTube determines the result using watch-time share, not CTR alone. Tests generally take a few days and can run up to two weeks. Google Help

So I'd use:

YouTube Studio → if you primarily want statistically cleaner native thumbnail experiments.
TubeBuddy → if you specifically want an external testing workflow plus ongoing channel/library management.

If your priority is specifically “show me every thumbnail I've tested, the CTR before/after, test dates, and the winning variant in one historical dashboard,” I can look for a tool that specializes in exactly that rather than general YouTube SEO.

For correlating subscriber growth spikes with specific videos, collaborations, and external promotions, the best setup is usually not one tool but a small attribution stack. The right choice depends on whether you need channel-level diagnosis or marketing attribution.

Best overall: YouTube Studio + a tracking layer

1. studio.youtube.com — source of truth for subscriber changes

Best for answering:

• Which videos generated subscribers?
• When did subscriber velocity change?
• What traffic sources contributed?
• Did a collaboration video outperform normal uploads?

Use:

• Analytics → Content → individual video → “Subscribers gained”
• Analytics → Advanced Mode → compare videos and date ranges
• Mark upload dates, collab dates, and promotion dates against subscriber graphs

YouTube Studio is the most accurate source for first-party subscriber and video performance data. OutlierKit

2. analytics.google.com — for external promotions

Best when growth comes from:

• newsletters
• websites
• paid ads
• podcasts
• influencer mentions
• Reddit/community posts

Use:

• UTM-tagged links
• campaign names
• landing pages

Example:

youtube.com/@channel?utm_source=podcast&utm_campaign=guest_episode&utm_content=episode_42

Then compare:

• referral traffic
• signups
• conversions
• timing against subscriber jumps

Best creator-focused analytics add-ons

tubebuddy.com

Good for:

• identifying which videos drive subscriber growth
• comparing uploads
• finding patterns in topics, titles, and formats

It provides video-level analytics and channel growth insights beyond basic reporting. TubeBuddy TubeBuddy

vidiq.com

Good for:

• content opportunity analysis
• competitor comparisons
• identifying breakout videos and topics

(Useful if you are trying to understand why a video caused growth, not just whether it did.)

Best for collaborations and sponsorship attribution

CreatorIQ

Best for agencies/brands managing many creators:

• campaign tracking
• creator performance reporting
• cross-platform measurement

Modash

Best for:

• tracking creator campaigns
• comparing collaboration outcomes
• measuring influencer-driven traffic and conversions

prismattribution.com

Best for YouTube sponsorship measurement where you need to connect sponsored videos with measurable conversion events. PRISM Attribution

My recommended workflow for a serious YouTube channel

Create a simple “growth events ledger”:

Then overlay it with:

1. YouTube Studio subscriber graph
2. Video-level subscriber gains
3. External traffic from GA4
4. Promo links/UTMs
5. Notes about algorithmic boosts or viral moments

A spreadsheet or dashboard in lookerstudio.google.com pulling from YouTube Analytics + GA4 is often enough for most creators.

If you are running a large channel (100k+ subscribers) or doing many collaborations, I’d lean toward YouTube Studio + GA4 + a creator campaign platform. For a solo creator, YouTube Studio + a well-maintained event timeline is usually the highest signal-to-effort approach.

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, the closest fits are:

• vidiq.com — probably the closest match to what you described. Its Channel Audit surfaces videos by total watch time, average watch time, retention, views, and subscribers, and flags content that may need work. Its newer AI tooling can also identify niche/topic clusters and patterns across videos. vidIQ Help Center vidIQ Help Center
• YouTube Studio Advanced Mode — best if you want the underlying data rather than an automated recommendation engine. You can create groups of up to 500 videos, compare their watch time, retention, CTR, subscribers, etc., and export the results. Google Help Google for Developers
• tubebuddy.com — useful for content optimization/testing, but it is less directly focused on automatically discovering watch-time clusters and consolidation opportunities.

For your specific use case

I'd structure the analysis around:

Topic cluster → number of videos → total watch time → % of channel watch time → average retention → overlap between videos → candidates to consolidate/redirect/update.

For example, you might discover:

YouTube itself doesn't automatically do the semantic clustering + "these 12 videos are redundant, consolidate them" step particularly well; its Groups feature requires you to define the groups yourself. Google Help

If your goal is specifically automated cluster discovery and consolidation recommendations, I'd start with vidIQ, then validate its conclusions against YouTube Studio's actual watch-time data.

If you give me your channel URL, I can also look at the available public data and show you what a cluster/consolidation analysis would look like.

If you need more than a platform’s built-in live dashboard, the right tool depends on whether you care most about encoder health, viewer QoE, CDN/distribution, or full observability/alerting. Here are strong options:

For streamers using OBS (Twitch, YouTube Live, etc.)

Look at tools focused on the broadcaster side:

• Stream Guardian — monitors OBS-related stats such as bitrate, FPS, CPU load, and dropped frames in a customizable dashboard. Stream Guardian
• Livepush — useful if you stream to multiple destinations and need per-destination publish health, bitrate, FPS, and connection monitoring. Livepush

Metrics worth tracking beyond the default dashboard

Encoder/source health

• Output bitrate stability
• Dropped frames
• Encoder overload
• CPU/GPU utilization
• Keyframe interval problems
• Audio/video sync drift

Network and delivery

• Upload bandwidth
• Packet loss
• RTT/latency
• CDN ingest errors
• Regional failures

Viewer experience (QoE)

• Stream start time
• Rebuffer ratio
• Playback failures
• Average watch time
• Concurrent viewers
• Quality switches (ABR changes)

Operations

• Alerting thresholds
• Incident timelines
• Historical comparisons
• API export into your own dashboards

Quick recommendations by use case

• Solo creator / OBS streamer: Stream Guardian or Livepush
• Small production team: Grafana + Prometheus or Wowza analytics
• Consumer video app: Mux Data
• Enterprise OTT/broadcast: Wowza, Mux, Datadog integrations
• WebRTC/interactivity: Agora Analytics

If you tell me your platform (Twitch, YouTube, RTMP, HLS, WebRTC, custom player, etc.) and whether you need viewer-side QoE or encoder-side health, I can narrow this down.

If you mean monitoring the stream itself and what viewers are experiencing, rather than just the platform’s built-in creator dashboard, I’d look at these:

• data.mux.com — probably the strongest general-purpose choice for engineering/operations. Its real-time Monitoring Dashboard exposes concurrent viewers, startup failures/time, playback failures, rebuffering, average bitrate, geography, and other QoE dimensions, with monitoring data available at under-20-second latency. Mux Mux Data
• Datadog + your streaming/player telemetry — useful if your team already uses Datadog and wants stream health alongside servers, APIs, CDN, and infrastructure. This is more of a build-your-own observability approach than a turnkey video-QoE product.
• Grafana + Prometheus/OpenTelemetry — good for a customizable operations dashboard if you control the encoder, ingest, player, or backend. You can put encoder CPU/GPU, dropped frames, bitrate, ingest errors, CDN metrics, viewer counts, and application metrics on one screen.
• Conviva — worth evaluating for large-scale OTT/broadcast operations where detailed viewer QoE, audience segmentation, and service-provider/CDN analysis are important. It is more enterprise-oriented.
• Cloud/platform monitoring — if you're using AWS, Azure, or Google Cloud for the streaming stack, their native monitoring can complement a video-QoE product by exposing infrastructure and delivery-side problems that the player dashboard won't.

What I'd choose by use case

One important distinction: viewer-side QoE and encoder-side health are different problems. For example, Mux can monitor things viewers actually experience—startup time, rebuffering, playback failures, bitrate, and concurrent viewers—and it also has a separate real-time Live Stream Input Health dashboard for monitoring what it's receiving from your encoder. Mux Mux

If you're running your own website/player, Mux is particularly interesting because its SDKs can collect live-stream latency, bitrate, errors, startup performance, and other player metrics from players such as HLS.js and Video.js. Mux Mux

If you tell me where you're streaming (YouTube/Twitch, your own HLS/DASH stream, OBS, Wowza, AWS IVS, etc.) and whether you care more about encoder health, viewer QoE, or both, I can narrow this to 2–3 tools and compare pricing/integration.

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.

Yes. If you mean “show me the weird/niche queries that my existing videos are actually getting discovered from,” rather than generic keyword-volume tools, there are a few options.

Best fit for what you're describing

1. YouTube Studio — surprisingly useful, and free

For each video, go to Analytics → Reach → YouTube Search terms. YouTube shows the actual search terms viewers used when they found that video. It also separately shows Suggested videos, including the videos that referred viewers to yours. Google Help Google for Developers

This is the closest thing to ground truth because it's based on your actual traffic, not estimated keyword volume.

The limitation: YouTube doesn't give you a giant keyword-discovery database or necessarily expose every low-volume query.

2. vidIQ — probably the closest third-party fit

vidIQ has a Top Search Terms for Your Channel feature that summarizes search terms bringing viewers to your videos, including their contribution to views/watch time. It also has personalized Keywords For You based on your channel data. vidIQ Help Center

support.vidiq.com

That's more interesting for your use case than simply typing "my niche + keyword" into a keyword tool.

3. TubeBuddy — better for expanding what you discover

TubeBuddy's Keyword Explorer gives search/competition information and related keyword ideas, while Search Explorer lets you investigate keywords directly from YouTube search results. TubeBuddy TubeBuddy

tubebuddy.com

The important distinction

There are really two different datasets you're asking about:

And Suggested is fundamentally different from Search. YouTube says Suggested traffic comes from recommendations next to/after other videos, and its recommendation system uses signals including viewing/search history, clicks, watch time, and satisfaction—not simply keywords. Google Services

So if your goal is something like:

“My video unexpectedly started getting 2,000 views/day. Tell me the obscure searches and adjacent videos that are causing it, then find more topics I can exploit.”

I'd use YouTube Studio as the source of truth + vidIQ as the discovery layer.

If you tell me what kind of videos/channel you make, I can also show you a specific workflow for turning those hidden Search + Suggested terms into 10–20 new video ideas.

If you mean “scan my existing YouTube library, find videos that keep getting views over time, then tell me which ones are worth updating first,” vidIQ is the closest fit.

• vidIQ Channel Audit analyzes your channel and surfaces performance patterns using metrics such as views per hour, watch time, retention, subscribers gained, and traffic sources. vidIQ vidIQ Help Center
• Its Optimize tool then analyzes individual existing videos and suggests improvements to titles, thumbnails, SEO, and content. You can sort videos by optimization scores to identify where to work next. vidIQ vidIQ Help Center
• It also tracks title/thumbnail changes against subsequent performance, which is useful for determining whether re-optimization actually helped. vidIQ Help Center

TubeBuddy is another good option, particularly its Opportunity Finder and SEO Studio, but its documented workflow is more centered on finding optimization opportunities and improving metadata than automatically defining an “evergreen → re-optimize first” queue. TubeBuddy TubeBuddy

One caveat: neither tool appears to define “evergreen” as a simple dedicated label. In practice, I'd identify evergreen candidates from your YouTube Analytics by looking for older videos that continue generating meaningful views/watch time, especially from Search or other sustained traffic, and then use vidIQ to prioritize optimization. YouTube Studio itself provides the underlying performance and traffic-source data in Advanced Mode. Google Help

If you tell me your channel size/niche and roughly how many videos you have, I can suggest a concrete automated workflow for turning your library into an “Evergreen → Priority → Action” list.

If you mean a tool that connects to your YouTube channel, automatically finds older videos that still have growth potential (“evergreen” videos), and tells you what to re-optimize first, these are the closest options:

For this specific workflow:

“Find my hidden evergreen winners → rank which ones to refresh → tell me what to change”

I’d look first at:

1. vidIQ Channel Audit / AI Coach — strongest general-purpose option for connected channel analysis and prioritized recommendations. vidIQ vidIQ
2. TubeToolkit Analytics — if your main requirement is explicitly spotting evergreen videos. TubeToolkit
3. YTGrowth — if you want a more audit-like “top fixes first” report. YTGrowth

A good evergreen detection model usually looks for videos with signals like:

• steady views per day/week long after publishing
• high search traffic share
• strong watch time relative to channel average
• high retention but declining impressions (often a refresh opportunity)
• topics that remain relevant but have outdated packaging

If you tell me your channel size, niche, and whether you care more about SEO/search traffic, browse recommendations, or revenue, I can narrow down the best tool.

If you mean a YouTube channel (not a website), the best tools are different from traditional SEO crawlers. You’ll want something that audits videos, titles, thumbnails, keywords, rankings, retention signals, and content opportunities, then helps prioritize what could create the biggest traffic gains.

Good options:

For “give me prioritized fixes based on potential traffic lift”, I’d look for a workflow like:

1. Connect YouTube Analytics data (impressions, CTR, watch time, retention).
2. Identify:
   • Videos ranking on page 1–2 but with weak CTR → thumbnail/title tests.
   • Videos with high retention but low impressions → keyword/topic optimization.
   • Old videos with declining traffic → refresh opportunities.
   • Topics competitors rank for that you don’t → content gaps.
3. Rank fixes by:
   • Estimated search demand
   • Current ranking position
   • Existing channel authority
   • Effort required

A practical stack:

• vidIQ or Morningfame → YouTube-specific prioritization.
• YouTube Studio Analytics → actual traffic opportunity signals.
• Ahrefs/Google Trends → validate search demand.

If you meant a website/channel brand (e.g., a content site, newsletter, or ecommerce channel) rather than YouTube, the recommendation changes—tools like Semrush, Ahrefs, or impact-ranked audit platforms are more appropriate. Some newer audit tools specifically try to rank fixes by traffic impact rather than just listing errors. ranklogs.com

If you share your channel type (YouTube, TikTok, blog, podcast, etc.) and size, I can narrow it down.

If you mean a YouTube channel, vidIQ is probably the closest match to what you're describing. Its channel audit evaluates SEO optimization and other growth signals, while its keyword research surfaces search demand, competition, and keyword opportunities. vidIQ vidIQ

For your specific requirement—“find issues → estimate opportunity → tell me what to fix first”—I'd look at:

• vidIQ — strong for SEO audits, keyword opportunity discovery, competitor research, and channel-specific recommendations. vidIQ vidIQ
• TubeBuddy — particularly useful if you have a large existing library and want to identify/bulk-fix metadata, tags, descriptions, and other SEO issues. vidIQ YTGrowth
• YouTube Studio — essential companion because it gives you the first-party data on impressions, CTR, search traffic, retention, and actual performance; third-party “opportunity” scores are estimates. vidIQ
• YTGrowth — specifically advertises a prioritized action plan based on a broader audit including SEO, CTR, retention, traffic sources, and competitor benchmarks. YTGrowth

My practical setup: use vidIQ + YouTube Studio. Let vidIQ identify SEO/keyword opportunities, then use your Studio data to estimate which fixes are actually likely to matter for your channel rather than blindly chasing SEO scores. vidIQ

If you give me your channel URL, I can also tell you exactly what I'd audit and how I'd construct a traffic-lift-prioritized fix list.