ChatGPT answers, unedited

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.

For what you’re describing—watch time + viewer cohorts, sliced by upload date and educational video category—I’d use a combination rather than relying on a generic social-media dashboard.

My recommended setup

1. YouTube Studio Advanced Mode — start here.

• You can create groups of videos by category/topic.
• Compare groups, videos, and time periods.
• Export the underlying reports.
• YouTube specifically supports comparing videos over equivalent 24-hour, 7-day, and 28-day lifespans, which is extremely useful when comparing videos uploaded on different dates. Google Support Google Support
• YouTube also exposes new/casual/regular viewer segments, although these aren't a traditional user-level cohort table. Google Support Google Support
• Google Sheets or Excel — probably the best next layer for your particular analysis.

• Export video-level/daily data from YouTube.
• Add columns such as:

• Video
• Upload date
• Category
• Video length
• Views
• Watch time
• Average view duration
• Average % viewed
• New / casual / regular audience
• Days since upload
• Then build a cohort table where rows = upload week/month and columns = days/weeks since upload.
• Looker Studio — if you want a persistent dashboard rather than spreadsheets.

• Good for visualizing cohort curves such as:

• Week 0 → Week 1 → Week 2 → Week 4 watch time
• Upload month × video category
• Category × average watch time per viewer
• New vs. returning audience
• The underlying YouTube Analytics API supports dimensions such as video, date, demographics, traffic source, and audience-retention measurements. Google for Developers Google for Developers

The important distinction

If by "viewer cohorts" you mean:

"People who first discovered my channel from videos uploaded in January—how much do they continue watching my channel in February, March, April, etc.?"

that's substantially more sophisticated than YouTube Studio's standard reports. YouTube's API gives you powerful video/date/audience dimensions, but it does not simply hand you a user-level longitudinal cohort table. Google for Developers Google for Developers

If instead you mean:

"Videos uploaded in January: how much watch time did they accumulate in their first 7/28/90 days, broken down by educational category?"

then YouTube Studio Advanced Mode + a spreadsheet/dashboard is enough, and you can make a very useful cohort analysis without buying expensive creator software.

For an educational channel, I'd structure the core analysis like this:

That lets you distinguish "this category gets lots of views" from "this category produces durable watch time after publication."

If you tell me roughly how many videos you have (e.g. 100 vs. 2,000) and whether you mean video cohorts or actual viewer cohorts, I can recommend the specific software stack and show you exactly how I'd structure the analysis.

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.

For an educational YouTube channel, I’d use YouTube Studio + a BI tool, rather than buying a specialized YouTube analytics platform immediately.

My recommendation

1. YouTube Studio Advanced Mode — start here

• Free and already has the underlying data.
• You can create groups such as Math, Science, Exam Prep, Tutorials, etc., then compare their watch time, retention, subscribers, and other metrics.
• You can compare videos/groups over equivalent lifespans such as their first 24 hours, 7 days, or 28 days, which is particularly useful when comparing videos uploaded on different dates. Google Help Google Help
• You can export the reports for deeper analysis. Google Help

2. Looker Studio — my pick for the next step
Use it to build a dashboard where every video has fields like:

Upload date → Video category → Video → Views → Watch hours → Avg. view duration → Retention → Subscribers

Then you can answer questions like:

• "Do videos uploaded in Q1 have better 28-day watch time than Q2?"
• "Which educational category generates the most watch hours per upload?"
• "Are viewers of my calculus videos returning to subsequent calculus videos?"
• "Which upload cohorts produce the most returning viewers?"
• "Does a video's first-week performance predict its 90-day watch time?"

The underlying YouTube Analytics API supports dimensions such as date, month, video, demographics, device, traffic source and audience retention, so you can build substantially more customized reporting than Studio's standard charts. Google for Developers Google for Developers

If you want serious cohort analysis

I'd actually build a small BigQuery → Looker Studio pipeline.

Your fact table could look roughly like:

That "days since upload" field is the key. It lets you compare videos fairly despite different upload dates.

One important limitation: YouTube's audience-retention API report is video-specific, rather than something you can simply aggregate across multiple videos in one retention query. Google for Developers

What I'd choose

• Small channel / just getting started: YouTube Studio Advanced Mode
• Want beautiful dashboards + minimal coding: Looker Studio
• Want rigorous upload-cohort analysis: BigQuery + Looker Studio
• Want an off-the-shelf third-party analytics product: I can compare those too, but I wouldn't buy one until you've established exactly which cohort metrics YouTube's own data can't give you.

If you tell me roughly how many videos you have and what you mean by "viewer cohorts" (e.g. viewers who first discovered you in January vs. viewers returning 30/60/90 days later), I can recommend the exact stack and dashboard schema I'd use.

Your fact table could look roughly like:

That "days since upload" field is the key. It lets you compare videos fairly despite different upload dates.

For an educational YouTube channel, I’d start with YouTube Studio Advanced Mode + Looker Studio, rather than paying for a creator-SEO tool.

My recommendation

1. YouTube Studio Advanced Mode — best starting point

• Free and first-party.
• Lets you create video groups such as “Algebra,” “Exam Prep,” “Study Skills,” etc.
• Compare groups, videos, and time periods.
• Analyze watch time, average view duration, retention, subscribers, traffic sources, and audience characteristics.
• Export the underlying data for further analysis. support.google.com

This is particularly useful because YouTube lets you create groups of up to 500 videos, so you can manually assign every upload to your educational category and analyze the categories as cohorts. support.google.com

2. Looker Studio — best for the dashboard you actually want
Use YouTube/Google data as the source and build a dashboard with dimensions like:

Then calculate things like:

• Watch hours per upload
• Watch hours per 1,000 views
• Average view duration
• Average percentage viewed
• 7/30/90-day watch time after publication
• Subscriber conversion
• Category × upload-month performance
• New vs. returning/casual/regular viewer behavior

YouTube's underlying Analytics APIs expose video ID, date, watch time, average view duration, average percentage viewed, subscriber status, traffic source, geography, etc., which makes this kind of cohort analysis possible. developers.google.com

One important distinction

If by "viewer cohorts by upload date" you mean:

"Of people who first watched a video published in January, how much did they watch over the following 7/30/90 days?"

that's more sophisticated than ordinary YouTube Studio reporting. You'd ideally want a data warehouse + YouTube Analytics API + Looker Studio setup, because the API gives you video-level and date-level activity that you can join against each video's publication date. developers.google.com

I'd structure the data roughly like:

Video
→ upload date
→ category
→ length
→ topic

Viewer activity
→ viewing date
→ video
→ views
→ watch minutes
→ average view duration
→ subscriber status

Then derive:

Days since upload = viewing date − upload date

That gives you genuinely useful curves like:

Biology videos published in Q1: 420 watch hours in first 7 days → 1,100 by day 30 → 2,800 by day 90.

That is much more actionable for an educational channel than simply looking at lifetime views.

What I'd choose

If you're nontechnical:
YouTube Studio Advanced Mode → export → Google Sheets/Looker Studio

If you want a polished recurring dashboard:
YouTube Analytics API → BigQuery → Looker Studio

If you're trying to answer sophisticated cohort questions:
YouTube Analytics API → BigQuery → Looker Studio is the setup I'd recommend.

I wouldn't make vidIQ or TubeBuddy the centerpiece of this particular analysis. They're useful for things like SEO, keyword research, competitor research, and publishing optimization, but your question is fundamentally a content-performance/cohort analytics problem. YouTube's own data is the better foundation. support.google.com

If you tell me roughly how many videos you have and whether you use Google Sheets/BigQuery, I can lay out the exact dashboard schema—including the cohort calculations I’d use for an educational channel.

For an educational YouTube channel, I’d use a stack rather than one tool, because the analysis you want — watch time + viewer cohorts + upload date + video category — usually requires combining YouTube’s first-party data with a dashboard layer.

Best options

1. studio.youtube.com — best starting point (free)

Best if you own the channel.

Use:

• Advanced Mode → break down by: - Video
• Date published
• Content groups
• Traffic source
• Viewer type (new/casual/regular)
• Export CSVs for deeper analysis

YouTube Studio’s Advanced Mode supports custom breakdowns, comparisons, groups, filters, and exports. Google Help The Audience and Content reports also expose viewer segments such as new, casual, and regular viewers. Google Help Google Help

For your use case, create video groups like:

• Algebra lessons
• Exam prep
• Coding tutorials
• Study skills
• Beginner vs advanced lessons

Then compare:

• Average view duration
• Watch hours per upload
• 30/90/365-day performance
• Subscriber conversion
• Returning viewer rate

2. lookerstudio.google.com — best for dashboards

Best if you want a "channel analytics cockpit."

Build dashboards with dimensions:

• Upload month
• Video category
• Series/course
• Video length bucket
• Evergreen vs news/tutorial content

Useful charts:

• Cohort heatmap: "videos uploaded in Jan → watch hours over 12 months"
• Category leaderboard
• Retention curve by course type
• Subscriber gain per 1,000 watch hours

Typical setup:

• YouTube Analytics API → Google Sheets/BigQuery → Looker Studio

3. cloud.google.com + SQL — best for serious channels

Use this if you have hundreds/thousands of videos.

You can answer questions like:

"Do my physics videos uploaded in Q1 generate more watch time from returning viewers after 180 days than my chemistry videos?"

or:

"Which video categories create the highest lifetime watch hours per upload?"

A typical table:

Then run cohort queries.

4. vidiq.com or tubebuddy.com — useful for content decisions

These are better for:

• Topic research
• SEO
• Packaging
• Competitor comparisons

They are less ideal for deep private watch-time cohorts because they generally don't have the same internal viewer data that YouTube Studio has.

5. Dedicated creator analytics tools

If you want ready-made cohort reports, look at tools such as:

• tubeanalytics.net — focused on cohort-style content performance analysis. TubeAnalytics

My recommended setup for an educational channel

Small channel (<100 videos):

• YouTube Studio Advanced Mode
• Google Sheets
• Looker Studio dashboard

Growing channel (100–1,000 videos):

• YouTube Analytics API
• BigQuery
• Looker Studio

Large course/education business:

• BigQuery warehouse
• Looker Studio or Tableau
• Automated weekly cohort reports

The specific dashboard I’d build for an educational channel would have:

1. Upload cohort table

• Rows: upload month
• Columns: months since upload
• Metric: watch hours
• Content category comparison

• Category → lifetime watch hours/video
• Category → subscriber conversion
• Category → returning viewer growth
• Student lifecycle view

• First video watched
• Videos watched next
• Time from first view → subscription
• Evergreen score

• Watch hours after 90 days ÷ first 30-day watch hours

That last metric is especially useful for education because many lessons are discovered months or years after publishing. TubeAnalytics

If you tell me your channel size (number of videos + whether you use playlists/courses), I can suggest the simplest stack that won’t require unnecessary engineering.

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

My picks

1. YouTube Studio — best starting point

• Free and already has your underlying data.
• Advanced Mode lets you compare videos/groups, add metrics, change breakdowns, filter, and export reports. Google Help
• You can create groups such as Algebra, Physics, Study Skills, etc., then compare their watch time and other metrics.
• Downside: it isn't really a flexible cohort-analysis environment. You have to do more work to get “videos uploaded in March → watch time in days 0–7, 8–30, 31–90” style analysis.
• TubeAnalytics — best if you want creator-focused cohort analysis

• Specifically supports the idea of grouping videos by launch period/theme and comparing their performance over equivalent follow-up windows. TubeAnalytics
• This is probably the closest off-the-shelf fit to your question.
• Google Sheets/Excel + YouTube export — best for maximum flexibility

• Export your video-level data from YouTube Studio and add columns such as: - Upload date
• Upload month/quarter
• Video category/topic
• Video length
• Views
• Watch hours
• Average view duration
• Subscribers gained
• Then build a pivot table where rows = upload cohort, columns = age of video, and filters = category.
• YouTube explicitly supports exporting Advanced Mode reports. Google Help
• BigQuery + Looker Studio — best if you're serious about this

• I'd choose this if your channel has hundreds/thousands of videos or you want an automatically updating analytics system.
• YouTube's APIs expose video/date and audience-related dimensions, which gives you the raw ingredients for custom reporting. Colin Leitner Colin Leitner

What I'd actually build for an educational channel

I'd make a cohort dashboard like this:

Then add viewer cohorts:

• New viewers
• Casual viewers
• Regular viewers
• Subscribers vs non-subscribers
• Returning viewers
• Watch time per unique viewer

YouTube itself defines new/casual/regular viewers based on viewing behavior and provides these audience metrics, although some of the more sophisticated cross-tabulation you’re asking for isn't exposed directly in Studio. Google Help Google Help

My recommendation: start with YouTube Studio → export → Google Sheets if you have fewer than ~500 videos. If you want this to become a recurring analytical system, go straight to BigQuery + Looker Studio. If you want the least technical route, TubeAnalytics is worth evaluating.

If you tell me roughly how many videos you have and what you mean by “viewer cohorts” (e.g. new vs returning viewers, or viewers who first discovered you in a particular month), I can recommend the exact setup and dashboard I'd use.

For an educational YouTube channel, I’d start with YouTube Studio Advanced Mode + a spreadsheet/Looker Studio, rather than buying a creator-optimization tool.

My recommendation

1. YouTube Studio Analytics — best starting point

• It can compare videos, groups, and time periods and lets you export reports. You can create groups of up to 500 videos. Google Help
• YouTube's underlying Analytics API exposes metrics such as estimated minutes watched, average view duration, views, subscribers gained/lost, etc. Google Developers
• This is the data I'd use as the source of truth.
• Looker Studio — best for the dashboard you’re describing

• Build a dataset with one row per video: - Upload date
• Video/category
• Video length
• Views
• Watch hours
• Average view duration
• Average percentage viewed
• Subscribers gained
• Traffic source
• Audience type
• Then create cohort views such as "videos uploaded in January → watch time accumulated in days 0–7, 8–30, 31–90, etc."
• This is much more flexible than the standard Studio interface.
• vidIQ — useful supplement, not my primary choice

• Its historical-performance tools are useful for seeing how individual videos perform throughout their lifecycle. vidIQ
• I'd use it for content research, benchmarking, titles/thumbnails, and competitive analysis, rather than as the core cohort database.
• Tubular Labs — if you want serious enterprise-level analysis

• Tubular is considerably more powerful for categorizing content and comparing performance over standardized post-publication windows (1, 2, 3, 7, 30 days), plus audience/category analysis. Tubular Labs
• Probably overkill unless your channel/business is large.

The important distinction

If by "viewer cohorts by upload date" you mean:

"For videos uploaded in March, what percentage of their total watch time came from new vs. returning viewers, and how did that cohort behave over the following 7/30/90 days?"

I'd build this yourself from YouTube Analytics data rather than rely on vidIQ.

YouTube does provide new, casual, and regular viewer metrics, but its native Audience reporting isn't really a full arbitrary cohort-analysis system. Google Help

A particularly useful dashboard for an educational channel would look like:

That lets you answer the really valuable questions: Which subjects produce durable watch time? Which upload cohorts compound over time? Which topics attract viewers who actually watch deeply?

If you tell me roughly how many videos are on your channel and whether you're comfortable using Google Sheets/Excel, I can recommend a specific stack and show you exactly how I'd structure the cohort dashboard.

If your goal is specifically “How do videos perform as cohorts based on upload date × category, especially in watch time and viewer behavior?”, I’d use a YouTube-native data stack rather than a generic social-media dashboard.

My picks

• Best overall: TubeAnalytics — probably the closest fit if you want cohort-style analysis without building it yourself. It specifically supports grouping videos by launch period/topic and comparing their performance over consistent follow-up windows. It also has retention and topic analysis aimed at educational creators. TubeAnalytics TubeAnalytics

tubeanalytics.net

• Best flexible/cheap option: YouTube Analytics + Looker Studio — use YouTube's data as the source and build your own cohort dashboard. This is what I'd choose if you're comfortable with spreadsheets/dashboarding. YouTube exposes video-level, daily, demographic, traffic-source and watch-time data through its Analytics APIs. Colin Leitner Colin Leitner Google for Developers
• Best for serious/custom analysis: YouTube Analytics API + BigQuery + Looker Studio — if your channel is large enough that you want a proper analytics warehouse. You can create a table where each video has upload_date, category, video_id, etc., then join that against daily watch-time/viewer data and calculate cohorts yourself.

The dashboard I'd build

For an educational channel, I'd structure the data roughly like:

The important thing is to normalize for time since upload. Otherwise an old video will almost always beat a new video simply because it has had longer to accumulate watch time.

I'd want charts for:

1. Watch time by days since upload — Day 1, 7, 30, 60, 90.
2. Upload-month cohort × category — e.g. January Algebra vs February Algebra.
3. Viewer cohort — new vs casual vs regular viewers. YouTube itself provides these audience classifications. Google Help
4. Retention by category — especially important for educational content because a 30-minute lesson generating 15 minutes of viewing can be more valuable than a 6-minute video with a higher completion percentage.
5. Subscriber conversion by category.
6. Evergreen vs launch-driven performance — how much watch time continues accumulating after 30/60/90 days.
7. Traffic source by cohort — Search vs Suggested vs Browse vs External.

One important caveat

YouTube's native Analytics is actually quite powerful, but “upload date cohort × category × subsequent viewer behavior” isn't a single built-in report. The API gives you the underlying dimensions/metrics—such as video, day, demographics, watch time and retention—so a custom dashboard can construct that cohort view. Colin Leitner Colin Leitner

My recommendation: start with YouTube Analytics → Looker Studio if you have fewer than ~500–1,000 videos. If you're already dealing with a substantial catalog and want automated cohort reporting, use YouTube Analytics API → BigQuery → Looker Studio. If you don't want to build the machinery, TubeAnalytics is worth evaluating first.

If you tell me roughly how many videos you have, how often you upload, and whether your categories are YouTube categories or your own topic categories, I can recommend the exact stack and dashboard schema I'd use.

If you're talking about a YouTube educational channel, I'd use a two-layer setup:

My recommendation

1. YouTube Studio Advanced Mode — start here

• Free and already connected to your channel.
• Lets you break down performance by content/video, date, audience, traffic source, etc., compare videos/groups, save reports, and export data. Google Help
• YouTube also lets you create groups of up to 500 videos, which is useful for grouping categories such as Algebra, Physics, Exam Prep, Study Skills, etc. Google Help

2. Google Sheets — best for a lightweight cohort model
Export your YouTube data and create a table where each video has:

Then you can compare 0–7, 8–30, 31–90, 91–180 days after publication across categories. This is probably the most useful analysis for an educational channel because it separates "this video got lots of watch time" from "this category consistently generates watch time after publication."

3. Looker Studio — best dashboard layer
Once your spreadsheet/data is clean, use Looker Studio for an interactive dashboard with:

• Upload month/cohort
• Video category
• Video
• Days since upload
• Watch time
• Views
• Average view duration
• Average percentage viewed
• Subscribers gained
• New vs. returning viewers

If your channel is large: BigQuery

If you're publishing a lot of videos or want this to become a repeatable analytics system, I'd go with:

YouTube Analytics API → BigQuery → Looker Studio

The YouTube APIs expose daily video-level data and watch-time metrics, including watch_time_minutes and average view duration. LTNR Google for Developers You can join that to video metadata such as publication date and your own category taxonomy, then calculate cohorts in SQL. The API supports video and day dimensions, which is particularly useful for constructing a "days since upload" cohort. LTNR

What I'd choose for you

Small/medium channel:
YouTube Studio → Google Sheets → Looker Studio

Large channel / serious analytics:
YouTube Analytics API → BigQuery → Looker Studio

I wouldn't buy a specialized YouTube analytics SaaS just for this. YouTube Studio already gives you much of the underlying data and export capability, while BigQuery/Sheets gives you the custom cohort analysis that Studio doesn't conveniently provide. Google Help

One important distinction: if by "viewer cohorts" you mean people who first discovered your channel in January vs. February and how much they subsequently watched, that's a different cohort analysis from videos uploaded in January vs. February. YouTube's standard Audience reporting has new/casual/regular viewer concepts, but a true upload-date × viewer-cohort × category model is better built from exported/API data. Google Help

If you tell me roughly how many videos you have and what you mean by "category" (YouTube's category vs. your own subjects like math/physics/history), I can lay out the exact dashboard/schema I'd use.

For an educational YouTube channel, I’d choose a tool based on how deep you want the cohort analysis to go. The key views you’re describing are:

• Upload-date cohorts: “How do videos published in January perform after 7/30/90/180 days?”
• Content-category cohorts: “Do my math tutorials retain viewers better than exam-prep videos?”
• Viewer cohorts: “Do people who discover me through beginner videos come back?”
• Watch-time curves: “Which topics create durable watch time vs one-week spikes?”

Here are the strongest options:

1. studio.youtube.com — best starting point (free)

Best if you have a small-to-medium channel and want reliable first-party data.

YouTube Studio’s Advanced Mode lets you:

• break down performance by video, date, traffic source, audience type, and other dimensions
• compare videos or groups
• export reports for deeper analysis Google Help

For your use case, create video groups such as:

• “Algebra”
• “AP Exam Prep”
• “Beginner Lessons”
• “Short Explainers”
• “Deep Dives”

Then compare:

• watch time per impression
• average view duration
• returning viewers
• subscribers gained
• 30/90-day performance

Google HelpLimit: It does not naturally give you a Netflix-style cohort table (“all videos uploaded in Q1 → retention after 90 days”) without exporting data.

2. lookerstudio.google.com + YouTube Analytics API — best custom dashboard

Best if you’re comfortable with spreadsheets or want a creator “BI dashboard.”

Typical setup:

Data sources

• YouTube Analytics API
• YouTube Studio exports
• Google Sheets

Dashboard tabs

1. Upload cohorts

• Upload month
• Category
• First 7/30/90/180 days watch time
• Views per subscriber
• Topic performance

• Category → total watch hours
• Category → average retention
• Category → returning viewers
• Evergreen analysis

• Watch hours generated after 90 days
• Videos still gaining momentum

The YouTube Analytics API exposes metrics like estimated minutes watched, average view duration, views, and subscribers gained, which are useful for building these reports. Google for Developers

3. tubeanalytics.net — easiest creator-focused cohort tool

Best if you don’t want to build dashboards.

It is designed around content cohort analysis:

• grouping videos by upload period or theme
• comparing durability over time
• analyzing watch time, retention, subscribers, and revenue by cohort TubeAnalytics

Good fit for an educational channel because your content likely has long-tail value (a lesson published six months ago may still outperform a new upload).

4. Spreadsheet + API workflow — best for serious educational channels

If you have hundreds of lessons, I’d consider a lightweight data warehouse approach:

Stack

• YouTube Analytics API
• Google Sheets or Airtable
• BigQuery (optional)
• Looker Studio

Create a table like:

Then generate cohorts:

My recommendation by channel size

• <50 videos: YouTube Studio Advanced Mode only
• 50–500 videos: YouTube Studio exports + Looker Studio
• 500+ videos / multiple course categories: API + dashboard
• You want answers quickly without building: TubeAnalytics

For an educational channel, I’d prioritize 90-day and 180-day watch-time cohorts, not just views. A lesson that earns steady watch time for a year is usually more valuable than a video that spikes for a week.

A useful schema for your channel would be:

• Cohort: upload month
• Category: subject/course level
• Metrics: watch hours, returning viewers, subscribers gained, average percentage viewed
• Windows: day 7, 30, 90, 180 after upload

That will tell you which subjects are actually building your audience.

For an educational channel, I’d prioritize 90-day and 180-day watch-time cohorts, not just views. A lesson that earns steady watch time for a year is usually more valuable than a video that spikes for a week.

A useful schema for your channel would be:

• Cohort: upload month
• Category: subject/course level
• Metrics: watch hours, returning viewers, subscribers gained, average percentage viewed
• Windows: day 7, 30, 90, 180 after upload

That will tell you which subjects are actually building your audience.

For an educational YouTube channel, you’ll probably want something that goes beyond basic “top videos” reporting and lets you analyze:

• Watch time by upload cohort (videos published in the same month/quarter)
• Viewer cohorts (new vs returning learners, subscriber vs non-subscriber)
• Performance by curriculum/topic/category
• Retention curves and drop-off points
• Long-tail performance (which lessons keep generating watch hours months later)

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

1. studio.youtube.com — Best starting point (free)

Best if you want reliable first-party data.

You can analyze:

• Watch time and average view duration
• New, casual, and regular viewers
• Returning viewers
• Viewer demographics
• Traffic sources
• Video-level retention
• Content comparisons

YouTube’s Audience reports specifically include viewer groups such as new, casual, and regular viewers, which are useful cohort-style metrics for educational channels. Google Help

Limitation: YouTube Studio does not make it easy to create custom cohorts like:

“Videos uploaded Jan–Mar 2025 → how many watch hours did they generate in their first 90 days vs videos uploaded Jan–Mar 2024?”

For that, you’ll likely want a BI layer.

2. lookerstudio.google.com + YouTube Analytics API — Best custom dashboard option

This is what I’d recommend for a serious educational channel.

Build dashboards like:

Upload cohort analysis

Category/topic analysis

Tag videos with categories like:

• Algebra
• Physics
• Coding
• Exam prep
• Beginner tutorials

Then compare:

• Watch hours/video
• Subscriber conversion
• Returning viewers
• Retention
• Search traffic

The YouTube Analytics API supports dimensions and metrics for reports, including video activity, watch time, views, subscribers gained, and time-based breakdowns. Google for Developers Google for Developers

Best stack:

• Data source: YouTube Analytics API
• Database: Google Sheets / BigQuery
• Dashboard: Looker Studio

3. vidiq.com — Best for content strategy

Useful if your main question is:

“What should I teach next?”

Good for:

• Topic research
• Keyword opportunities
• Competitor analysis
• Identifying high-performing subjects

Less useful for deep cohort analysis.

4. tubebuddy.com — Best for optimization workflows

Good for:

• Thumbnail/title testing
• SEO workflows
• Publishing checklists

Not my first choice for cohort analytics.

5. Spreadsheet + Python/R — Best if you want research-grade analysis

For an education channel with hundreds of videos, I’d consider:

• Export YouTube Analytics data
• Store video metadata: - Upload date
• Topic/category
• Length
• Difficulty level
• Instructor
• Series/course
• Analyze cohorts in: - Python (pandas)
• R
• Tableau
• Power BI

This lets you answer questions like:

• “Do beginner videos create more returning viewers than advanced lessons?”
• “Which courses have the highest learner retention?”
• “Do shorter lessons outperform longer lectures?”
• “Which topics create subscribers six months later?”

My recommended setup for an educational channel

Small channel (<100 videos):

1. YouTube Studio
2. Google Sheets export
3. Looker Studio dashboard

Growing channel (100–1,000 videos):

1. YouTube Analytics API
2. BigQuery or Sheets
3. Looker Studio
4. Add a manual “topic/category” taxonomy

Large education library (1,000+ videos):

1. YouTube Analytics API
2. BigQuery
3. Looker Studio / Tableau
4. Automated topic tagging

A particularly useful metric for education is “watch hours per video in the first 180 days” rather than total views, because older evergreen lessons often compound slowly.

If you tell me your approximate channel size (number of videos, subscribers, and whether videos are courses/playlists vs standalone lessons), I can suggest a more specific stack.

For an educational YouTube channel, I’d choose a tool based on how deep you want the cohort analysis to go. The specific breakdown you’re describing — watch time + viewer cohorts + upload date cohorts + video categories — is usually beyond basic “top videos” dashboards.

Here are the strongest options:

1. studio.youtube.com — best starting point (free)

Best for: most creators, especially if you’re willing to export data.

YouTube Studio already gives you:

• Watch time by video
• Average view duration
• Returning vs new viewers
• New/casual/regular viewer segments
• Performance comparisons between videos or groups
• Exportable reports through Advanced Mode Google Help Google Help

For your use case, I’d create reports like:

The limitation: YouTube Studio is not great for custom cohort tables across hundreds of videos.

2. lookerstudio.google.com + YouTube Analytics API — best custom dashboard

Best for: an educational channel with a content library.

This is probably what I’d recommend if you have:

• dozens/hundreds of lessons
• multiple course categories
• evergreen content
• a need to compare “Class A vs Class B vs Class C”

You can build dashboards like:

Upload Cohort Matrix

              Days after upload
Upload Month   7d     30d    90d    365d
Jan 2026       8k     30k    90k    220k watch min
Feb 2026       10k    42k    110k   ...

Category Performance

Category          Videos   Watch Hours/video   Returning viewers
Python Basics       25          4,200               38%
Math Tutorials      40          3,100               42%
Exam Prep           18          6,500               55%

3. VidIQ — best for content strategy

Best for: deciding what educational content to make next.

Useful for:

• identifying topics that outperform
• comparing videos by keyword/topic
• finding content gaps

Less ideal for deep internal cohort analysis because it is more focused on optimization and growth.

4. TubeBuddy — good for workflow + testing

Best for: creators who also need:

• title/thumbnail testing
• SEO workflows
• publishing processes

Less powerful than a custom dashboard for cohort analysis.

5. Spreadsheet + API workflow — surprisingly powerful

For a serious educational channel, many teams use:

• Google Sheets
• YouTube Analytics API
• Looker Studio
• optional Python/R scripts

Your raw table might look like:

Then you can pivot by:

• upload month
• topic
• instructor
• course
• difficulty level
• format

My recommendation for an educational channel

Small channel (<50 videos):

• YouTube Studio + Google Sheets

Growing channel (50–500 videos):

• YouTube Studio exports → Looker Studio dashboard

Large course library (500+ videos):

• YouTube Analytics API → BigQuery/Sheets → Looker Studio

If I were building this specifically for an education channel, I’d track these cohorts:

1. First 7/30/90/365 days after upload

• Does a lesson have a long tail?
• Course/topic cohorts

• Which subjects create repeat viewers?
• Learner cohorts

• New viewers → returning viewers → subscribers
• Learning-path cohorts

• Do viewers watch Lesson 1 → Lesson 2 → Lesson 3?

A tool like YouTube Studio can provide much of the raw data, but a Looker Studio dashboard is what turns it into a useful “curriculum analytics” system. Google Help

I can also sketch a dashboard layout (tabs, charts, and metrics) specifically for an educational YouTube channel.

If you mean YouTube, I’d start with YouTube Studio itself, then add a BI tool if you want true cohort analysis.

My picks

• YouTube Studio Advanced Mode — best starting point / free. You can compare videos or custom groups, choose breakdowns and metrics, filter results, save reports, and export the data. YouTube also lets you create groups of up to 500 videos, which is useful for categories such as “Algebra,” “Study Skills,” or “Exam Prep.” Google Help

support.google.com

• Google Sheets + YouTube Analytics API — best for the analysis you’re describing. The API exposes video, date, group, demographic, traffic-source and retention dimensions, plus estimatedMinutesWatched, views, etc. Google for Developers Google for Developers You can build a table where each video has:

• upload date
• category/topic
• video length
• views
• watch time
• average view duration
• subscribers gained
• audience retention
• traffic source

Then calculate cohorts by upload month/week and compare how different categories accumulate watch time over 7/30/90/180 days.

• Looker Studio — best dashboard layer. Put your YouTube-export/API data into Sheets or BigQuery, then build interactive charts such as:

• Upload cohort × video category → watch hours
• Days since upload → cumulative watch time
• Category → median 30-day watch time
• Upload month → returning/new/casual/regular viewer mix
• Video → retention curve
• BigQuery + Looker Studio — best if the channel is large. I'd use this once you're dealing with hundreds/thousands of videos or want historical automated reporting. YouTube's bulk Reporting API is specifically designed for larger-scale data and includes dimensions such as date, video ID, demographics, subscribed status, and traffic source. Google for Developers

What I'd use for an educational channel

YouTube Studio → API → BigQuery/Sheets → Looker Studio

The important distinction is that YouTube Studio can show you the underlying metrics, but your cohort question is really a custom data-modeling problem. You want to separate when someone watched from when the video was published.

For example:

That lets you answer a much more useful question than “which videos got the most watch time?”:

Which types of educational videos continue generating watch time long after publication?

If you tell me roughly how many videos you have (e.g. 50, 500, 5,000) and whether you're comfortable with Sheets/SQL, I can recommend the simplest setup and give you the exact cohort schema/dashboard to build.

For what you’re describing, I’d use a YouTube-native data stack, rather than a generic social-media analytics tool.

My recommendation

1. YouTube Studio Advanced Mode — start here

It already lets you break down performance by video, groups, dates, audience, etc., compare groups/time periods, and export reports. You can create groups of up to 500 videos, which is useful for categories like Algebra, Physics, Exam Prep, Study Skills, etc. Google Help

2. Google Sheets — best next step for cohort analysis

Export your YouTube data and add columns such as:

• Upload date
• Video category/topic
• Video length
• Views
• Watch time
• Average view duration
• Subscribers gained
• Traffic source
• Days since upload

Then you can build cohorts like "videos uploaded in January 2026" and compare their first 7/30/90/180 days of watch time.

3. Looker Studio — best dashboard layer

If you want something that feels like a proper analytics product, put the data into Sheets or BigQuery and build interactive dashboards. For example:

• X-axis: days since upload
• Y-axis: cumulative watch hours
• Color: educational category
• Lines: upload-month cohorts
• BigQuery + YouTube APIs — best if your channel is large

If you're talking about hundreds/thousands of videos and want repeatable analysis, I'd graduate to BigQuery. YouTube's APIs expose video/date dimensions and metrics such as estimatedMinutesWatched, average view duration, views, and subscribers gained. Google for Developers Google for Developers

The key distinction

What you're asking for is really content cohort analysis, not ordinary YouTube analytics.

I'd structure your data approximately like:

That lets you answer much more useful questions such as "Do my Physics videos accumulate watch time faster than Algebra videos?" rather than simply "Which videos have the most views?"

YouTube's current Advanced Mode is particularly useful as the starting point because it supports custom breakdowns, metrics, comparisons, saved reports, and exports. Google Help

If this is a serious educational channel, my stack would be:

YouTube Studio → Google Sheets → Looker Studio

…and BigQuery only once the spreadsheet approach starts becoming painful.

If you tell me roughly how many videos you have and whether you want to analyze 7/30/90/365-day cohorts, I can recommend the exact setup and the fields/formulas I'd use.

For an educational YouTube channel, you’ll probably want something that goes beyond basic views and subscribers and lets you analyze cohorts by upload date, topic/category, retention, and long-term watch time. My recommendations:

Best overall: studio.youtube.com + a BI dashboard

Best for: serious channels that want ownership of their data.

Use:

• YouTube Studio Advanced Mode for exports
• Google Sheets / Looker Studio / Tableau / Power BI for cohort dashboards

You can build reports like:

• Upload month → cumulative watch hours after 7/30/90/365 days
• Video category → average viewer retention
• Topic cluster → subscriber conversion
• First-time viewers vs returning viewers by content type

YouTube Studio already provides audience segments such as new, casual, and regular viewers, plus watch-time and audience reports. Google Help Advanced Mode also allows expanded reports and exports for deeper analysis. WebFiddle

My pick for a channel you plan to grow for years: YouTube Studio → BigQuery/Sheets → Looker Studio.

Best creator-focused tool: TubeBuddy

Best for: optimizing educational content production.

Useful for:

• Comparing videos by topic
• Finding which formats perform
• Metadata testing
• Publishing workflow

Less ideal for true cohort analysis (e.g., “videos uploaded in Q1 2025 retained viewers better over 12 months”).

Best for deeper YouTube research: vidIQ

Best for: educational channels driven by search.

Good for:

• Topic discovery
• Keyword opportunities
• Competitor comparisons
• Identifying content gaps

Less focused on cohort retention modeling.

Best if you want automated cohort dashboards: adaptive.ai

Best for: teams that want recurring reports.

It can track metrics over time and group videos by custom categories such as tutorials, interviews, or other content types. Adaptive AI

Best custom setup for an education channel

I would structure your data like this:

Then create dashboards:

1. Content cohort dashboard

“Do my newer lessons outperform older ones?”

• X-axis: days since upload
• Y-axis: cumulative watch hours
• Group: upload month/category

2. Curriculum dashboard

“Which subjects create loyal viewers?”

• Category
• Average view duration
• Returning viewers
• Subscribers per 1,000 views

3. Student journey dashboard

“Are people watching a series?”

Track:

• Video A → Video B continuation
• Playlist completion
• Returning viewers after first lesson

My recommended stack by channel size

• Under 10k subscribers: YouTube Studio + Sheets
• 10k–100k: YouTube Studio + Looker Studio + vidIQ
• 100k+: YouTube Analytics API + BigQuery + Looker Studio/Tableau

For an educational channel specifically, I’d prioritize retention curves and cohort watch time over raw views. A lesson that brings fewer views but creates returning viewers and multi-video sessions is often more valuable than a viral one-off. YouTube’s audience reports specifically separate new, casual, and regular viewers, which is useful for measuring that kind of loyalty. Google Help

If you tell me your channel size, whether you use YouTube only or also a course/LMS, and how many videos you have, I can suggest a more specific stack and dashboard layout.

If your goal is specifically “How does watch time accumulate for videos uploaded in different periods, and which content categories create the strongest/most valuable viewer cohorts?”, I’d use a YouTube Analytics → data warehouse/dashboard setup rather than a generic social-media analytics tool.

My picks

1. Best overall: YouTube Analytics + Looker Studio

• Start with YouTube Studio’s Advanced Mode for exploration and exports. It lets you compare videos/groups/time periods, change breakdowns and metrics, filter data, save reports, and export them. Google Help
• Then build a Looker Studio dashboard around your exported/API data.
• Best if you want a repeatable dashboard such as: - Upload month → total watch hours
• Upload month → watch hours per video
• Video category → watch hours
• Upload cohort × category → 7/30/90/180-day watch time
• New vs. returning/regular viewers
• Viewer retention by cohort
• Best if you want serious analysis: BigQuery + Looker Studio

• This is what I'd choose if your channel has a substantial library.
• Pull YouTube Analytics/Reporting data into BigQuery, attach a metadata table containing each video's upload date/category, and calculate cohort metrics yourself.
• Google's APIs support dimensions such as video, day/month, demographics, traffic source, device, etc., making this much more flexible than the standard Studio interface. Google for Developers Google for Developers
• You can then create a cohort matrix like:

3. Simplest: YouTube Studio alone

• Don't underestimate it. YouTube's current Advanced Mode already provides comparisons, breakdowns, filters, multiple metrics, saved reports and exports. Google Help
• The limitation is that cohort analysis across upload date × category × elapsed time gets cumbersome. That's where a spreadsheet/warehouse becomes worthwhile.

One important distinction

I'd define your cohorts by video upload date, not by the date the watch occurred.

For example, suppose you uploaded 20 videos in March. You want to ask:

“How much watch time did the March cohort generate during its first 30 days, regardless of which calendar days those watches occurred?”

That's much more useful for an educational channel because it lets you compare the lifetime trajectory of different batches of content.

I'd create these fields for every video:

• video_id
• upload_date
• upload_month
• category
• video_length
• views
• watch_hours
• avg_view_duration
• avg_percentage_viewed
• subscribers_gained
• new_viewers
• returning/regular viewers
• traffic_source
• 7_day_watch_hours
• 30_day_watch_hours
• 90_day_watch_hours
• 180_day_watch_hours

Then your most useful visualization is a cohort heatmap:

Upload month × video category → watch hours in first N days

That will answer questions like “Are my newer biology videos actually outperforming my older math videos, or are they simply benefiting from having had more time to accumulate views?”

YouTube itself provides new/casual/regular viewer analysis, so you can also layer audience behavior onto the content cohorts. Google Help

What I'd choose for you

Small channel (<100–200 videos):
YouTube Studio → export → Google Sheets/Excel → Looker Studio

Growing channel (200–1,000+ videos):
YouTube Analytics API → BigQuery → Looker Studio

If you're comfortable with SQL/Python:
Definitely BigQuery. It gives you the flexibility to construct the exact cohort definitions you want rather than trying to force YouTube Studio into doing it.

One caveat: YouTube's analytics/reporting data has specific dimension/metric combinations and availability constraints, so you should design the data model around the API's supported reports rather than assuming every metric can be cross-tabbed arbitrarily. Google for Developers Google for Developers

If you tell me roughly how many videos you have and whether you want a no-code solution, I can recommend the exact stack and dashboard structure I'd use.

If you mean YouTube educational content and you want to answer questions like:

• “Which upload cohorts generate the most watch time?”
• “Do videos in my Algebra category retain viewers better than Biology?”
• “How does a video perform in its first 7/30/90 days?”
• “Which viewer cohorts keep coming back to older videos?”

I’d use a YouTube-native + BI stack, rather than buying a specialized creator analytics product.

My recommendation

1. YouTube Studio — start here

studio.youtube.com is surprisingly capable now. Advanced Mode lets you compare videos/groups/time periods, change dimensions and metrics, filter data, save reports, and export the current view. Google Help

For your use case, I'd create YouTube Groups for your categories—for example:

• Algebra
• Physics
• Chemistry
• Exam prep
• Study skills

Then compare groups on watch time, average view duration, returning/new viewers, views, and retention.

YouTube's Audience tab also separates viewers into new, casual, and regular viewers, which is useful for understanding whether particular categories are building a durable audience. Google Help

Cost: Free
Best for: Getting started and answering ~80% of normal channel questions.

2. Looker Studio + YouTube Analytics data — my pick for you

If you specifically want cohort analysis by upload date × category, I'd graduate to Looker Studio.

The important distinction is that you want to build your own analytical dataset rather than just look at YouTube's standard dashboards.

A useful table would look roughly like:

Then you can make a cohort matrix such as:

Upload month × category → cumulative watch hours after 7/30/90/180 days

That is much more useful for an educational channel than simply ranking videos by lifetime views.

YouTube's Analytics API supports dimensions such as video, day/month, demographics, traffic source, device, etc., and its retention reporting provides 100 points across a video's elapsed time. Google for Developers

3. BigQuery — if your channel is large or you want serious cohort analysis

If you're comfortable with SQL, I'd eventually use:

YouTube Analytics → BigQuery → Looker Studio

This is the setup I'd choose if you have hundreds/thousands of videos or want to analyze several years of data.

You can construct cohorts based on:

• Upload date
• Video category
• Topic
• Video length
• Series/course
• Traffic source
• Viewer geography
• New vs returning viewer
• Days since publication
• First-view date, if the available data supports the cohort definition you want

The YouTube Reporting API provides bulk data with dimensions including date and video ID, which makes it much better suited to building a historical analytical warehouse than manually exporting individual reports. Google for Developers

What I would actually build

For an educational channel, I'd make a dashboard with four views:

1. Cohort performance

Upload month × category

→ views, watch hours, subscribers, average view duration at 7 / 30 / 90 / 365 days

2. Content decay curves

For each category:

Watch hours per day since upload

This tells you whether, say, Physics videos are evergreen while Exam Prep videos spike and decay.

3. Viewer quality

Category × new/casual/regular viewers

This answers a much more valuable question than “which video got the most views?”:

Which topics turn one-time viewers into recurring viewers?

4. Video-level cohort table

Each video gets a row with:

Upload date | category | length | 7d watch hours | 30d watch hours | 90d watch hours | retention | returning-viewer rate

YouTube's current Analytics already exposes watch time and average view duration, and its retention reports let you examine where viewers drop off. Google Help

Bottom line

If you want something you can use today: YouTube Studio Advanced Mode.

If you want a serious dashboard: YouTube Analytics/API → BigQuery → Looker Studio.

If you don't want to code: YouTube Studio exports + Google Sheets/Looker Studio is probably the sweet spot.

One important wrinkle: YouTube changed how views are counted starting August 24, 2026, so I'd use watch hours and engaged-view metrics alongside views when comparing cohorts across this transition. Google Help

If you tell me roughly how many videos you have and whether you already use Google Sheets/BigQuery, I can recommend the exact stack and lay out the cohort dashboard schema for you.

For an educational YouTube channel, I’d use YouTube Analytics + Looker Studio as the default setup.

My recommendation

YouTube Studio's Advanced Mode already lets you compare videos/groups/time periods, choose breakdowns and metrics, filter results, and export reports. Google Help

But Looker Studio is where I'd build your actual cohort dashboard. Google's YouTube Analytics connector lets you connect your owned channel directly to Looker Studio. Google Cloud

The cohort analysis I'd build

The important distinction is that you want to analyze videos by their upload cohort, rather than simply looking at calendar-month performance.

I'd create a table like:

Then add filters for:

• Upload month/week
• Video category/topic
• Video length
• New vs returning viewers
• Traffic source
• Subscriber status
• Individual video

This lets you answer much more useful questions such as:

"When I publish biology videos, how much watch time does the average video accumulate in its first 30 days compared with algebra?"

rather than:

"How many hours did the channel get last month?"

One important limitation

YouTube's underlying Analytics API gives you excellent building blocks—video, day/month, demographics, watch time, average view duration, average percentage viewed, etc.—but "upload date" and your own educational category taxonomy aren't necessarily ready-made cohort dimensions. Google for Developers Google Developers

So I'd maintain a small video metadata table:

video_id
title
upload_date
category
subtopic
video_length
series
difficulty

Then join that against YouTube's daily analytics data.

That gives you a proper analytical dataset:

Video → Upload date → Cohort → Category → Daily performance

You can then calculate Day 1 / Day 7 / Day 30 / Day 90 watch time for every video and compare cohorts fairly.

If you want to go one level deeper

For an educational channel, I'd particularly track:

• Watch hours per video
• Watch hours per 1,000 impressions
• Average percentage viewed
• 30-day cumulative watch hours
• Returning-viewer rate
• Subscribers gained per 1,000 views
• Search vs Browse vs Suggested contribution
• Performance by topic/category
• Performance by upload cohort

YouTube's API supports daily watch-time reporting and audience-retention metrics, including estimatedMinutesWatched, averageViewPercentage, audienceWatchRatio, and relativeRetentionPerformance. Google Developers Google Developers

Bottom line: start with YouTube Analytics → Looker Studio. Don't buy a specialized analytics platform yet. If you eventually want automated historical cohort analysis across hundreds/thousands of videos, move the YouTube data into BigQuery and use Looker Studio as the visualization layer.

If you tell me roughly how many videos you have and what categories you teach, I can lay out the exact dashboard/schema I'd build, including the cohort calculations.

So I'd maintain a small video metadata table:

video_id
title
upload_date
category
subtopic
video_length
series
difficulty

Then join that against YouTube's daily analytics data.

That gives you a proper analytical dataset:

Video → Upload date → Cohort → Category → Daily performance

You can then calculate Day 1 / Day 7 / Day 30 / Day 90 watch time for every video and compare cohorts fairly.

If you want to go one level deeper

For an educational channel, I'd particularly track:

If you mean “I want to know how much watch time each upload generates over its lifetime, grouped into cohorts by upload date and by my own educational content categories”, I’d use YouTube Studio + Looker Studio rather than a creator-SEO tool.

My recommendation

1. YouTube Studio — source of truth

• Free and already has Advanced Mode, where you can compare videos/groups/time periods, change dimensions and metrics, filter, save reports, and export data. Google Help
• YouTube also exposes the underlying Analytics API, including video, date, traffic-source, demographic and other dimensions. Google for Developers Google for Developers
• For an educational channel, I'd create YouTube Groups such as: - Algebra
• Biology
• Study skills
• Exam prep
• Beginner / intermediate / advanced
• Then compare groups by watch time, views, average view duration, returning viewers, subscribers gained, and retention.
• Looker Studio — best for the cohort dashboard

• Use it if you want a permanent dashboard rather than repeatedly exporting CSVs.
• Build a dataset with one row per video × day, plus metadata such as: - Upload date
• Video category/topic
• Video length
• Views
• Watch time
• Average view duration
• Subscribers gained
• Then calculate “days since upload” and cohort by upload week/month.
• Your key chart becomes something like:

Then add category as a filter. That's much more useful than simply ranking videos by lifetime watch time because older videos otherwise have an enormous built-in advantage.

3. Tubular Labs — if you want serious competitive benchmarking

• tubularlabs.com is much more powerful for comparing your content against the broader YouTube ecosystem. It supports upload-date filtering, custom dashboards/reports, standardized performance windows such as 1/3/7/30 days, and automated video categorization. Tubular Labs
• I'd consider it overkill for a single educational channel, though.

What I'd actually build

For your use case, I'd choose:

YouTube Analytics → data export/API → Google Sheets or BigQuery → Looker Studio

The important part isn't the visualization software; it's structuring the data around content age.

I'd have these dimensions:

• Upload cohort: week or month of publication
• Days since upload: 0, 1, 2, … 365
• Content category: your educational topic taxonomy
• Video: individual upload
• Format: long-form / Short / live
• Video length bucket: e.g. <10 min, 10–20, 20–40, 40+ min

And these metrics:

• Watch hours
• Views
• Average view duration
• Average percentage viewed
• Subscribers gained
• Returning viewers
• Impressions
• CTR

YouTube's Audience reports also distinguish new, casual, and regular viewers, which is particularly useful for determining whether a category is merely generating search traffic or actually building a recurring educational audience. Google Help

One important caveat: YouTube's native reports don't give you a magical “upload cohort” dimension. The Analytics API gives you date/video-level dimensions, so the cohort analysis is something you construct from those data. Google for Developers Google for Developers

If you tell me roughly how many videos you have (e.g. 100 vs. 2,000) and whether you're comfortable with Sheets/SQL, I can recommend the simplest stack and show you exactly how I'd structure the cohort dashboard.

For what you’re describing—watch time + viewer cohorts, segmented by upload date and your own video categories—I’d use a YouTube Analytics → Looker Studio setup rather than a creator-optimization tool.

My recommendation

1. YouTube Studio — start here

YouTube Studio’s Advanced Mode lets you compare performance, drill into reports, and export analytics data. Google Support

It’s enough if you mainly want:

• Watch time by video
• Views / average view duration
• Performance over time
• Audience characteristics
• Comparisons between groups of videos

The limitation is that your exact analysis—“videos uploaded in January vs. February, broken down by educational category, and how their viewer cohorts behaved afterward”—quickly becomes awkward in the Studio UI.

2. Looker Studio — best overall choice

This is what I'd pick for your channel.

Google provides a native YouTube Analytics connector, so you can connect your channel directly and build dashboards. Google Cloud Google Cloud Documentation

I'd create a dataset with fields like:

Then you can make a cohort heatmap:

Upload cohort → Day 1 → Day 7 → Day 30 → Day 90 watch time

and compare it across categories:

Biology vs. Physics vs. History vs. Math

That gets much closer to the analysis you're describing.

YouTube's underlying Analytics API supports video, date, demographic, traffic-source and other dimensions, along with metrics such as estimated minutes watched, average view duration, views and subscribers gained. Google for Developers Google for Developers Google for Developers

If you want a more serious analytics stack

I'd use:

YouTube Analytics API → BigQuery → Looker Studio

This is preferable once you have hundreds/thousands of videos or want to maintain historical cohort data automatically. The YouTube Reporting API provides bulk reports with dimensions including date and video ID, which you can join to your own metadata table containing category, upload date, topic, instructor, series, difficulty, etc. Google for Developers

The important insight is that “video category” should probably be your own taxonomy, rather than relying on YouTube's generic category. For an educational channel, I'd create something like:

video_id
upload_date
category
subcategory
series
difficulty
duration_bucket
evergreen_vs_current

Then join that metadata to the YouTube performance data.

What I'd choose for you

• Small/medium channel: YouTube Studio + Looker Studio
• Growing channel / serious experimentation: YouTube Analytics API + BigQuery + Looker Studio
• Want something turnkey with less data engineering: a third-party creator analytics platform can be useful, but I'd still favor the Google stack for custom cohort analysis.

One particularly useful metric I'd build is cumulative watch time per video normalized by days since upload. That lets you answer a much more interesting question than “which videos got the most watch time?”:

Which types of educational videos continue accumulating watch time months after publication?

That's essentially the cohort analysis you're looking for.

Then you can make a cohort heatmap:

Upload cohort → Day 1 → Day 7 → Day 30 → Day 90 watch time

and compare it across categories:

Biology vs. Physics vs. History vs. Math

That gets much closer to the analysis you're describing.

YouTube's underlying Analytics API supports video, date, demographic, traffic-source and other dimensions, along with metrics such as estimated minutes watched, average view duration, views and subscribers gained. Google for Developers Google for Developers Google for Developers

If you want a more serious analytics stack

I'd use:

YouTube Analytics API → BigQuery → Looker Studio

This is preferable once you have hundreds/thousands of videos or want to maintain historical cohort data automatically. The YouTube Reporting API provides bulk reports with dimensions including date and video ID, which you can join to your own metadata table containing category, upload date, topic, instructor, series, difficulty, etc. Google for Developers

The important insight is that “video category” should probably be your own taxonomy, rather than relying on YouTube's generic category. For an educational channel, I'd create something like:

video_id
upload_date
category
subcategory
series
difficulty
duration_bucket
evergreen_vs_current

Then join that metadata to the YouTube performance data.

What I'd choose for you

• Small/medium channel: YouTube Studio + Looker Studio
• Growing channel / serious experimentation: YouTube Analytics API + BigQuery + Looker Studio
• Want something turnkey with less data engineering: a third-party creator analytics platform can be useful, but I'd still favor the Google stack for custom cohort analysis.

One particularly useful metric I'd build is cumulative watch time per video normalized by days since upload. That lets you answer a much more interesting question than “which videos got the most watch time?”:

Which types of educational videos continue accumulating watch time months after publication?

That's essentially the cohort analysis you're looking for.

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.

For an educational YouTube channel, I’d use YouTube Analytics + Looker Studio as the default stack.

My recommendation

1. YouTube Studio Analytics — use this as your source of truth. Advanced Mode lets you compare videos/groups/time periods, change dimensions and metrics, filter audiences, and export data. Google Help Google Help

support.google.com

2. Looker Studio — use this for the actual cohort analysis/dashboard. Google provides a native YouTube Analytics connector, so you can connect your channel without building an API pipeline. Google Cloud Documentation Google Cloud Documentation

lookerstudio.google.com

3. Google Sheets or BigQuery — add one of these if you want to build a more sophisticated historical dataset. BigQuery becomes worthwhile once you're analyzing a large library or want to join YouTube data with things like email signups, course sales, or website behavior.

The analysis I'd build

For your specific question, I wouldn't just look at YouTube's standard "watch time by video." I'd create a video-upload cohort model:

Then add a viewer cohort dimension, such as:

• New viewers
• Casual viewers
• Regular viewers
• Subscriber vs. non-subscriber
• Ideally, the time since the video was published: 0–7 days, 8–30, 31–90, 91–180, 180+

That lets you answer much more useful questions like:

"Do my biology videos generate more long-term watch time than my math videos?"

rather than simply:

"Which videos got the most views?"

YouTube itself distinguishes new, casual, and regular viewers, and its Analytics data can be broken down by content, date, audience, and other dimensions. Google Help Google for Developers

One important caveat

If by "viewer cohorts by upload date" you mean "people who first watched my channel during the week/month a particular video was uploaded, and then how much they watched afterward," that's more sophisticated than the standard YouTube Studio reports.

In that case, I'd build a small YouTube Analytics API → BigQuery → Looker Studio pipeline rather than relying solely on the Studio interface. YouTube exposes time-period and video-level dimensions through its Analytics API, which makes this kind of longitudinal analysis much more feasible. Google for Developers

So my ranking:

• 🥇 YouTube Studio + Looker Studio — best starting point, inexpensive and flexible.
• 🥈 YouTube Analytics API + BigQuery + Looker Studio — best if you're serious about cohort analysis and have lots of videos.
• 🥉 Supermetrics/Coupler-style connector + Looker Studio — easier if you don't want to build the data pipeline yourself; third-party connectors can provide additional extraction capabilities. Coupler Blog

If you tell me roughly how many videos you have and what you mean by "viewer cohort" (e.g. new viewers in the month of upload vs. viewers who watched a particular upload), I can recommend the exact setup and the dimensions/metrics I'd use.

"Which videos got the most views?"

YouTube itself distinguishes new, casual, and regular viewers, and its Analytics data can be broken down by content, date, audience, and other dimensions. Google Help developers.google.com

One important caveat

If by "viewer cohorts by upload date" you mean "people who first watched my channel during the week/month a particular video was uploaded, and then how much they watched afterward," that's more sophisticated than the standard YouTube Studio reports.

In that case, I'd build a small YouTube Analytics API → BigQuery → Looker Studio pipeline rather than relying solely on the Studio interface. YouTube exposes time-period and video-level dimensions through its Analytics API, which makes this kind of longitudinal analysis much more feasible.

So my ranking:

If your goal is specifically “How does watch time behave for cohorts of videos based on when they were uploaded, and how does that differ by educational category?”, I’d use a YouTube Analytics + Looker Studio setup rather than a creator-SEO tool.

My recommendation

1. Best overall: YouTube Analytics + Looker Studio

studio.youtube.com is the source of truth for your channel data. Its Advanced Mode lets you compare videos, demographics, dates, content, traffic sources, and other dimensions, and export the data. Google Help

Then use lookerstudio.google.com to build the cohort dashboard.

I'd structure your data around:

The important trick is age since upload. Instead of asking “How much watch time did videos uploaded in March get?”, ask:

“How much watch time did the March cohort accumulate during its first 7/30/90 days?”

That makes older and newer educational videos genuinely comparable.

YouTube's reporting APIs expose video-level and daily watch-time data, including watch_time_minutes, average view duration, percentage viewed, views, and subscriber gains. Google for Developers Google for Developers

2. If you want the most powerful setup: YouTube API → BigQuery → Looker Studio

For a serious channel, this is what I'd eventually build.

YouTube Analytics API → BigQuery → Looker Studio

YouTube's Reporting API can provide bulk video analytics with date and video_id dimensions, while the Analytics API supports targeted queries. Google for Developers Google for Developers

You'd maintain a video metadata table like:

video_id
title
upload_date
category
length_minutes
topic
format

And a daily performance table:

video_id
date
views
watch_time_minutes
avg_view_duration
avg_view_percentage
subscribers_gained

Then calculate:

days_since_upload = date - upload_date

That gives you extremely useful cohort analyses:

• 0–7 day watch time by category
• 0–30 day watch time by upload month
• 30–90 day watch time by category
• Watch-time curve by video age
• Median vs. average performance by cohort
• Evergreen vs. launch-dependent videos
• Category × video age
• Upload month × category
• Videos that continue accumulating watch time after 90 days

This is much more useful than simply ranking videos by lifetime watch time.

Where vidIQ fits

vidiq.com is useful if you also want topic research, competitor analysis, SEO, and channel-growth benchmarking. Its current research products, for example, analyze large datasets of YouTube channels and uploads. vidIQ vidIQ

But I wouldn't make vidIQ the primary tool for your cohort analysis. Your own YouTube Analytics data is more appropriate for answering questions about your audience's watch behavior.

What I'd choose

• Small/medium educational channel: YouTube Studio + Looker Studio
• Growing channel with lots of videos: YouTube Analytics API + BigQuery + Looker Studio
• Need SEO/topic/competitor intelligence too: Add vidIQ
• Want the simplest possible solution: Start with YouTube Studio Advanced Mode and export to Sheets

If you tell me roughly how many videos you have (e.g. 50, 500, 5,000) and what you mean by “category” (YouTube's category vs. your own subjects like math/history/programming), I can recommend the exact setup and the cohort dashboard I'd build for an educational channel.

I'd structure your data around:

The important trick is age since upload. Instead of asking “How much watch time did videos uploaded in March get?”, ask:

“How much watch time did the March cohort accumulate during its first 7/30/90 days?”

That makes older and newer educational videos genuinely comparable.

YouTube's reporting APIs expose video-level and daily watch-time data, including watch_time_minutes, average view duration, percentage viewed, views, and subscriber gains. Google for Developers Google for Developers

2. If you want the most powerful setup: YouTube API → BigQuery → Looker Studio

For a serious channel, this is what I'd eventually build.

YouTube Analytics API → BigQuery → Looker Studio

YouTube's Reporting API can provide bulk video analytics with date and video_id dimensions, while the Analytics API supports targeted queries. Google for Developers Google for Developers

You'd maintain a video metadata table like:

video_id
title
upload_date
category
length_minutes
topic
format

And a daily performance table:

video_id
date
views
watch_time_minutes
avg_view_duration
avg_view_percentage
subscribers_gained

Then calculate:

days_since_upload = date - upload_date

That gives you extremely useful cohort analyses:

• 0–7 day watch time by category
• 0–30 day watch time by upload month
• 30–90 day watch time by category
• Watch-time curve by video age
• Median vs. average performance by cohort
• Evergreen vs. launch-dependent videos
• Category × video age
• Upload month × category
• Videos that continue accumulating watch time after 90 days

This is much more useful than simply ranking videos by lifetime watch time.

Where vidIQ fits

For what you’re describing, I’d use YouTube Analytics as the data source, then put the data into a BI tool rather than relying on a standalone YouTube analytics SaaS.

My picks

My recommendation: Looker Studio + YouTube Analytics/API.

YouTube Studio's Advanced Mode already lets you compare videos/groups/time periods, change dimensions and metrics, filter data, save reports, and export views. You can also create groups of up to 500 videos—which is particularly useful for categorizing educational content. support.google.com

The key, though, is to create your own upload cohorts.

For example:

• Upload cohort: Jan–Mar 2026
• Category: Algebra
• Videos: 18
• Days since upload: 0–7 / 8–30 / 31–90 / 91–365
• Watch time: total hours
• Views: total
• Average view duration
• Returning viewers
• Subscribers gained
• Traffic source

Then you can answer questions like:

"Do Algebra videos uploaded in Q1 generate more watch time per video than Physics videos uploaded in Q2?"

or:

"Which category produces the strongest 90-day viewer cohort?"

Why I wouldn't use YouTube Studio alone

The YouTube Analytics API gives you the underlying dimensions and metrics, including video, day/month, geography, traffic source, device, demographics, subscriber status, etc. developers.google.com

Meanwhile, the YouTube Data API gives you each video's metadata, including its category and published date. developers.google.com

That means you can build a dataset like:

video_id
title
published_date
upload_cohort
category
video_length
date
days_since_upload
views
watch_time_hours
avg_view_duration
subscribers_gained
returning_viewers
traffic_source

That's much more powerful than looking at "last 28 days" performance.

The dashboard I'd build

I'd make 4 tabs:

1. Cohort overview

Rows = upload cohorts
Columns = categories

Show:

• Watch hours/video
• Views/video
• Avg % viewed
• Subscribers/video
• 30-day watch time
• 90-day watch time
• 365-day watch time

2. Cohort curves

X-axis = days since publication
Y-axis = cumulative watch hours

Color = category.

This is probably the single most useful visualization for an educational channel because it separates "this video got lots of watch time because it's old" from "this type of video accumulates watch time unusually quickly."

3. Category performance

Compare categories such as:

• Math
• Science
• History
• Exam prep
• Tutorials
• etc.

with median rather than just average performance.

4. Viewer cohorts

Group viewers based on when they first watched your channel, then track their subsequent viewing behavior.

For example:

First watched channel in January → how many hours did this cohort watch in February, March, April, etc.?

That's a different—and more interesting—question from simply grouping videos by upload date.

One important distinction

If by "viewer cohorts by upload date" you mean people who first discovered your channel from videos uploaded during a particular period, I'd build that as a custom data model rather than assume YouTube Studio's standard cohort reports will give you exactly that.

YouTube's APIs are designed around combinations of dimensions and metrics, so you can construct the analysis yourself. developers.google.com

If this were my educational channel, I'd start with:

YouTube Analytics → Google Sheets/BigQuery → Looker Studio

You get a relatively inexpensive stack, retain your historical data, and can make the exact cohort cuts you want.

If you tell me roughly how many videos you have and whether you want a no-code solution, I can recommend the specific setup—and the exact cohort fields/dashboard I'd use.

For what you described—watch time + viewer cohorts grouped by upload date and educational video category—I’d use YouTube Analytics + Looker Studio, with Supermetrics if you want a more automated/robust setup.

My recommendation

YouTube's Advanced Analytics already lets you customize breakdowns by content, date, audience, traffic source, etc. support.google.com

The limitation is that the exact analysis you're describing is really a cohort dataset problem: you want to attach each video's upload date + category to subsequent daily/weekly viewing behavior.

The setup I'd build

Use Supermetrics → Looker Studio.

Supermetrics can pull video-level performance, split by date, and exposes fields such as video title, video ID, published date, watch time, average view duration, average percentage viewed, traffic source, etc. docs.supermetrics.com

Then create a dataset roughly like:

The key calculated field is:

Days since upload = viewing date − video published date

That lets you compare videos fairly even though they were uploaded at different times.

Then your dashboard can answer questions like

1. Which categories have the strongest first-7-day performance?

Math: 18.4 watch hours/video
Biology: 26.7
History: 14.2

2. Which categories have the longest tails?

Plot cumulative watch hours by days since upload:

Day 0 → Day 1 → Day 7 → Day 30 → Day 90 → Day 365

This is particularly valuable for educational content because evergreen videos can continue accumulating watch time long after publication.

3. Which upload cohorts are improving?

For example:

Q1 2026 Biology videos → 1,200 watch hours in first 30 days
Q2 2026 Biology videos → 1,850 watch hours in first 30 days

That's much more meaningful than simply comparing total watch time, because older videos have had more time to accumulate views.

One important distinction

If by "viewer cohorts" you mean actual groups of viewers—e.g.:

People who first watched my channel in January → how much did they watch in February/March?

that's a different analysis from video upload cohorts.

YouTube's API provides dimensions such as day, month, video, country, age group, gender, etc., and metrics including estimated watch time and average view duration. developers.google.cn But YouTube's native reporting isn't designed to hand you a ready-made arbitrary "first watched in month X" retention cohort.

If that's what you mean, I'd lean toward YouTube data → BigQuery → Looker Studio, where you can construct the cohort logic yourself.

Bottom line

If you're non-technical:
YouTube Analytics → Supermetrics → Looker Studio

If you're technical / want this to become a serious analytics system:
YouTube Analytics API → BigQuery → Looker Studio

I'd choose the second approach if you have a substantial back catalog and expect to keep producing videos, because you can build a reusable dataset where every video automatically gets an upload cohort, category, age-in-days, and cumulative watch-time curve.

YouTube Analytics API documentation
Supermetrics YouTube → Looker Studio 

For what you’re describing, I’d use YouTube Studio Advanced Mode first, then add a BI tool if you want a more sophisticated cohort model.

My picks

YouTube Studio's Advanced Mode now lets you create groups of up to 500 videos, compare groups and time periods, choose breakdowns/metrics, and export the underlying data. support.google.com

The setup I'd recommend for an educational channel

Create a dataset where each video has:

• Upload date
• Video ID/title
• Content category — e.g. algebra, statistics, study skills, exam prep
• Video length
• Views
• Watch time
• Average view duration
• Average percentage viewed
• Subscribers gained
• Traffic source
• Publish month/quarter

Then calculate cohorts such as:

Upload cohort → days/months since upload → watch time

For example:

That lets you answer much more useful questions than "which videos got the most views?"

For example:

Do newer algebra videos accumulate watch time faster than older algebra videos?

or

Which educational categories have the strongest long-tail watch time after 90 days?

If you want the really good version

I'd use YouTube Analytics API → BigQuery → Looker Studio.

The YouTube Analytics API supports video, date, group and other dimensions plus metrics such as views, estimated minutes watched, average view duration and average view percentage. developers.google.cn

The important wrinkle is that "category" isn't necessarily a native YouTube Analytics dimension in the way you're probably thinking about it. For an educational channel, I'd create your own content_category field and assign every video to one of perhaps 5–10 content pillars. Then your warehouse/dashboard can cohort on that field.

YouTube's own bulk reporting also exposes video-level daily watch-time data, including watch_time_minutes, so you can build a proper upload-date cohort rather than relying only on the current video's aggregate stats. developers.google.com

My recommendation: if you have fewer than ~500–1,000 videos, start with YouTube Studio Advanced Mode + Google Sheets/Looker Studio. If you're trying to build a serious content analytics system—with automatic daily ingestion and cohorts by upload month × category × age of video—go straight to BigQuery + Looker Studio.

If you tell me roughly how many videos you have and whether you're comfortable with SQL, I can recommend the exact stack and dashboard structure.

For an educational YouTube channel, I’d use YouTube Analytics + Looker Studio as the default stack, and only move to a paid BI/ETL tool if you outgrow it.

My recommendation

1. YouTube Studio — source of truth

• Free and directly connected to your channel.
• Advanced Mode lets you compare videos/groups, change dimensions and metrics, filter by things like subscription status, and export the underlying data. support.google.com
• You can create video groups such as Algebra, Biology, Exam Prep, Tutorials, etc., and analyze each group together. YouTube supports groups of up to 500 videos. support.google.com

2. Looker Studio — best for the analysis you described

• Google's YouTube Analytics connector lets you connect your channel directly to Looker Studio. cloud.google.com
• This is where I'd build a dashboard with dimensions like:
  • Upload date / upload cohort
  • Video category/topic
  • Video
  • Viewer type (new/casual/regular)
  • Subscription status
  • Traffic source
  • Geography
• And metrics like:
  • Watch time
  • Views
  • Average view duration
  • Average percentage viewed
  • Subscribers gained
  • Returning viewers

The important distinction is that upload cohort and viewer cohort are different things. For example, you could create a matrix like:

That lets you answer a much more useful question than "which videos got the most views?": which types of videos continue accumulating watch time after their initial launch?

If you want serious cohort analysis

I'd consider YouTube Analytics API → BigQuery → Looker Studio.

The API exposes daily video-level metrics including estimated minutes watched, views, average view duration and average view percentage, and the Reporting API can provide bulk reports. developers.google.com

That architecture lets you build a proper dataset where each video has:

video_id → upload_date → category → publish_week → publish_month

and each viewing observation has:

video_id → viewing_date → days_since_upload → viewer_type → watch_time

Then you can calculate things like:

• Watch time per video by day since publication
• 30/60/90/365-day watch-time curves
• Median performance by upload month
• Category-adjusted performance
• Evergreen vs. launch-driven videos
• New vs. returning viewer behavior
• Which topics produce the highest lifetime watch time
• Whether videos published in one period are systematically outperforming older cohorts

YouTube's API specifically supports time-based reports with daily/monthly dimensions and watch-time metrics, which makes this feasible. developers.google.com

What I'd actually choose

If you're a solo creator:
YouTube Studio → Looker Studio

If you have hundreds/thousands of videos or want rigorous cohort curves:
YouTube Analytics API → BigQuery → Looker Studio

If you want a polished, business-intelligence environment and don't mind paying:
I'd look at Power BI or Tableau, but I wouldn't start there. The YouTube-native + Google stack is considerably less friction for this particular problem.

One caveat: YouTube's native "new/casual/regular viewer" reporting isn't the same as a true viewer acquisition cohort (e.g. "people whose first-ever view was in January, then how much they watched in February"). If that is what you mean by viewer cohorts, I'd build the analysis around the API/exported data rather than relying solely on Studio's Audience tab. YouTube does provide new/casual/regular viewer reporting, but the API's granular dimensions and metrics give you considerably more flexibility. developers.google.comsupport.google.com

If you tell me roughly how many videos you have and whether your categories are YouTube categories or your own topic labels, I can recommend the exact data model/dashboard I'd build.

For what you’re describing—watch time + viewer cohorts, grouped by upload date and your own video categories—I’d use YouTube Analytics as the data source, with a BI layer on top rather than relying on a creator-focused analytics app.

My picks

My recommendation: Looker Studio + a structured video dataset.

YouTube's underlying Analytics API gives you metrics such as estimated minutes watched, average view duration, average view percentage, views, and subscriber gains, and it can break reports down by video and time period. developers.google.com

The important trick is to create your own fields:

• video_id
• video_title
• upload_date
• category — e.g. Algebra, Physics, Study Skills
• video_length
• views
• watch_hours
• avg_view_duration
• avg_view_percentage
• subscribers_gained
• days_since_upload

Then you can build a cohort matrix like:

That answers a much more useful question than "which videos have the most watch time?":

Which types of videos continue accumulating watch time after publication, and how quickly?

One caveat about "viewer cohorts"

YouTube gives you useful audience groups such as new, casual, and regular viewers, and its API exposes various audience dimensions. support.google.com

But if by cohort you mean something more rigorous like:

"People who first watched one of my videos in January—how much did they watch from my Physics videos during the following 90 days?"

YouTube's standard Analytics interface isn't really designed as a full customer-cohort database. I'd build that analysis in BigQuery or another database from your YouTube reporting data.

For an educational channel specifically

I'd track two different cohort concepts:

1. Video cohorts
Group videos by upload month/week and category.

"How does a Physics video perform during its first 90 days compared with Algebra?"

2. Viewer cohorts
Group viewers based on when they first became viewers.

"Do people who discover us through Physics eventually watch Algebra?"

That second analysis can tell you whether you're building a learning audience rather than just producing individual videos that happen to get views.

There are also education-specific analytics products emerging; for example, TubeAnalytics specifically markets retention and topic-cluster analysis for educational/tutorial creators. www.tubeanalytics.net But I'd treat that as a supplement rather than your core data warehouse.

If you tell me roughly how big the channel is (number of videos and monthly views), I can recommend the simplest setup—e.g. YouTube Studio → Sheets → Looker Studio versus BigQuery—and sketch the exact cohort dashboard I'd build.

For an educational YouTube channel, the best choice depends on how deep you want to go. For watch time + viewer cohorts segmented by upload date and video category, I’d rank them like this:

My recommended stack for an educational channel

Small–medium channel (under ~500 videos):

1. YouTube Studio Advanced Mode
   • Export:
     • video
     • upload date
     • views
     • watch time
     • average view duration
     • returning viewers
     • subscribers gained
   • Create video groups:
     • Subject (math, science, history)
     • Format (lecture, tutorial, quiz, explainer)
     • Audience level (middle school, college, professional)

support.google.com

2. Google Sheets or Looker Studio

Build cohort views like:

3. Add custom tags to every video:
   • subject
   • course level
   • series
   • difficulty
   • evergreen vs seasonal
   • search-driven vs browse-driven

That metadata is usually more valuable than YouTube’s default categories for education.

If you want the “Netflix for education” style analysis

I would use:

YouTube Analytics API → BigQuery → Looker Studio

You can answer questions like:

• Which courses create the most returning viewers?
• Do students who watch Part 1 finish the series?
• Which upload months produce the strongest 90-day watch-time cohorts?
• Are beginner videos creating more subscribers than advanced lessons?
• Which topics have the strongest evergreen performance?

One caution

YouTube does not generally provide raw viewer-level cohort data (for privacy reasons), so you’ll usually analyze video cohorts or aggregated audience cohorts rather than tracking individual viewers. heyanna.studio

If you tell me your channel size (number of videos/subscribers) and whether you care more about student retention, revenue, or course completion, I can suggest a more specific stack.

For an educational YouTube channel, I’d use a two-layer setup:

1. Start with YouTube Studio Advanced Mode — best for most creators

YouTube Studio Analytics Advanced Mode is the first thing I’d use.

It lets you:

• Group videos into content categories/pillars (e.g. Algebra, Physics, Study Skills).
• Compare those groups against each other.
• Break performance down by date, content, audience, traffic source, etc.
• Export the underlying data for deeper analysis. support.google.com

For your use case, I'd create groups such as:

Then compare watch time, views, average view duration, and average % viewed across those groups. YouTube's API exposes watch-time metrics including estimated minutes watched, average view duration, and average view percentage. developers.google.com

2. Add Looker Studio + YouTube Analytics data if you want a real cohort dashboard

Looker Studio is what I'd use once you want something more systematic.

The key is to build a dataset at roughly:

Video × upload date × category × viewing date

Then you can answer questions like:

"How much watch time did videos uploaded in Q1 generate during their first 7, 30, 60 and 90 days, broken down by educational category?"

That's much more useful than simply looking at total watch time, because older videos otherwise have an enormous built-in advantage.

YouTube's reporting infrastructure supports daily dimensions and video-level reporting, as well as metrics such as views and estimated minutes watched. developers.google.com

If you're serious about cohort analysis: BigQuery

I'd use BigQuery + Looker Studio if you have a substantial library and want this to become a repeatable analytics system.

The architecture I'd recommend is:

YouTube Analytics → BigQuery → Looker Studio

with a video metadata table containing:

video_id
upload_date
video_title
category
topic
format
duration

and an analytics table containing:

video_id
viewing_date
views
watch_time_minutes
average_view_duration
average_view_percentage
subscribed_status
traffic_source

You can then calculate:

days_since_upload = viewing_date - upload_date

and build cohorts such as:

• Day 0–7
• Day 8–30
• Day 31–90
• Day 91–365

That gives you a genuinely useful comparison:

"How does a Biology video typically accumulate watch time during its first 90 days compared with an Algebra video?"

YouTube's APIs also expose traffic-source dimensions and subscribed-status dimensions, which makes this approach considerably more powerful than simply exporting the headline Studio metrics. developers.google.com

My recommendation

If you're a solo creator:
YouTube Studio Advanced Mode → export → Google Sheets/Excel

If you want a polished recurring dashboard:
YouTube Analytics → Looker Studio

If you want serious longitudinal cohort analysis across hundreds/thousands of videos:
YouTube Analytics API → BigQuery → Looker Studio

For an educational channel specifically, I'd choose the third setup if you're planning to publish for years. The important analytical unit isn't just "video"—it's video cohort × content category × days since upload. That lets you distinguish evergreen educational content from videos that spike briefly and die.

If you tell me roughly how many videos you have and whether you currently use Google Sheets/Excel, I can recommend the simplest setup and the exact cohort metrics I'd track.

For an educational YouTube channel, I’d use YouTube Analytics + a BI tool, rather than a generic cohort-analysis product.

My picks

YouTube's Analytics API gives you the underlying pieces: video, day/month, playlist, traffic source, demographics, watch time, views, average view duration, and other metrics. developers.google.com

What I'd build for your channel

YouTube → BigQuery → Looker Studio

Create a video-level table roughly like:

video_id
video_title
upload_date
category
video_length
views
watch_time_hours
average_view_duration
average_view_percentage
subscribers_gained

Then create a second daily table:

video_id
upload_date
category
days_since_upload
calendar_date
views
watch_time_hours
average_view_percentage

That days_since_upload field is the key to what you're describing.

You can then make cohort charts such as:

Upload cohort

• Videos uploaded Jan 2026
• Videos uploaded Feb 2026
• Videos uploaded Mar 2026

Category

• Algebra
• Biology
• Study skills
• Exam prep

Days since upload

• Day 0
• Day 1
• Day 7
• Day 30
• Day 90
• Day 365

And compare:

"How many watch hours does a typical Biology video accumulate in its first 30 days compared with an Exam Prep video?"

or

"Do videos uploaded in 2026 generate more watch time per video during their first 90 days than videos uploaded in 2025?"

That's much more useful than simply looking at lifetime watch time.

One important caveat

YouTube's API doesn't simply hand you a ready-made "viewer cohort by video upload date" table. Its reports are organized around dimensions such as day, video, demographics, traffic source, etc. developers.google.com

So the smart approach is to pull daily video-level data and calculate cohort age yourself:

days_since_upload = viewing_date - upload_date

That lets you normalize videos of different ages and compare their performance fairly.

My recommendation

If you're not technical:

YouTube Analytics → Looker Studio

If you're serious about growing the channel and want to analyze several years of content:

YouTube Analytics API → BigQuery → Looker Studio

I'd choose the second setup for an educational channel because it gives you a durable dataset and lets you answer questions YouTube Studio isn't designed to answer.

If you tell me roughly how many videos you have and whether you want free/no-code or you're comfortable with SQL, I can recommend the exact setup and dashboard structure.

For what you’re describing, I’d start with YouTube Analytics + Looker Studio, rather than paying for a creator-SEO tool.

My recommendation

1. YouTube Studio Analytics — source of truth

• Free and directly connected to your channel.
• Advanced Mode lets you compare videos, groups, playlists, and time periods, change metrics, and export reports. support.google.com
• YouTube’s Analytics API exposes dimensions such as video and day and metrics including estimated watch time, views, and average view percentage. developers.google.com

The important trick is to give every video metadata such as:

• Upload date
• Category/topic — e.g. Algebra, Physics, Study Skills
• Format — lecture, tutorial, problem walkthrough, etc.
• Length bucket — <10 min, 10–20, 20–40, 40+
• Series/course
• Evergreen vs. topical

Then you can analyze something like:

Videos uploaded Jan–Mar → Physics → 20–40 min → watch time generated in months 0–1, 2–3, 4–6, etc.

That's much more useful than simply looking at your top videos.

2. Looker Studio — best for the actual cohort dashboard

Google Looker Studio is what I'd use to turn the exported/API data into a dashboard.

I'd build four views:

YouTube itself supports audience-retention metrics such as audienceWatchRatio and relativeRetentionPerformance; retention can be examined at different elapsed portions of a video. developers.google.com

3. TubeBuddy — useful if you don't want to build the data layer

TubeBuddy is worth considering if you want something more turnkey. It supports CSV exports and Looker Studio connections, including video-performance metrics and audience-retention data. www.tubebuddy.com

I'd choose it as an analytics convenience layer, not as the primary cohort-analysis system.

4. vidIQ — better for content strategy than cohort analytics

vidIQ is useful for identifying which topics/videos are performing and for channel audits, but I'd pick it less for the specific "upload cohort × category × watch-time accumulation" analysis you're asking about. Its Channel Audit, for example, focuses on things like views/hour, engagement, subscribers, watch time and retention. support.vidiq.com

What I'd actually use

YouTube Analytics → API/CSV → Google Sheets/BigQuery → Looker Studio

If your channel is small-to-medium, you can simplify that to:

YouTube Analytics → Google Sheets → Looker Studio

The key metric I'd create is watch-time per video-age day, rather than raw watch time. Otherwise a video uploaded three years ago will naturally beat one uploaded three weeks ago.

For example:

Cohort = upload month + category

Then calculate:

• Watch hours in first 7 days
• Watch hours in first 30 days
• Watch hours in first 90 days
• Watch hours in first 365 days
• Watch hours/day
• Views/video
• Average view duration
• Average % viewed
• Subscribers/video
• 90-day watch hours per 1,000 impressions
• % of lifetime watch time coming after 90 days

That last group is particularly valuable for an educational channel, because it separates videos that get a launch spike from videos that become durable search/reference assets.

If you tell me roughly how many videos you have and whether you already use Google Sheets/Looker Studio, I can recommend the simplest setup rather than overengineering it.

For what you’re describing, I’d use YouTube Analytics as the data source, but put a BI layer on top of it rather than relying on a creator-optimization tool like vidIQ.

My recommendation

1. YouTube Studio / YouTube Analytics — essential

• Best source of truth for watch time, views, average view duration, retention, subscribers, traffic source, etc.
• YouTube’s Analytics API exposes video, day, month, demographic, traffic, and other dimensions, plus estimatedMinutesWatched, averageViewDuration, and retention metrics. developers.google.com
• The big limitation: YouTube Studio isn't really designed for arbitrary cohort analysis such as “all videos uploaded in January → how much watch time did they generate in their first 7/30/90 days, broken down by category?”

2. Looker Studio + YouTube/BigQuery data — best overall for your use case

• If you want dashboards like:
  • Upload month × video category
  • Watch time in days 0–7 / 8–30 / 31–90
  • Views and watch time per video
  • Average retention by category
  • Subscriber conversion by cohort
  • Evergreen vs. launch-driven performance
• I'd build a dataset where each video has an upload date and category, then join daily YouTube performance to it.
• You can then calculate a video's “age since upload” and compare videos on an apples-to-apples basis.

YouTube's Reporting API is particularly useful if you want to pull bulk historical data; its reports include date and video ID dimensions. developers.google.com

The setup I'd actually build

YouTube → BigQuery → Looker Studio

Your core table would look roughly like:

Then you can make cohort charts such as:

Average watch hours per video by days since upload

with separate lines for:

• Math
• Science
• History
• Test prep
• Tutorials

That would tell you much more than simply looking at lifetime watch time, because a 2026 upload with 10,000 hours after 30 days isn't directly comparable to a 2023 upload with 10,000 lifetime hours.

What about vidIQ / TubeBuddy?

I'd consider them secondary tools, not the analytics foundation. They are useful for things like topic research, titles, thumbnails, competitors and channel-growth analysis. For example, vidIQ currently publishes large-scale YouTube research based on aggregated creator data. vidiq.com

But for the specific question “How does watch time accumulate for each upload cohort, and which educational categories produce the strongest viewers over time?”, I'd choose BigQuery + Looker Studio.

One important nuance: YouTube's audience-retention API data is available at 100 points along each video's timeline, but the retention report itself is queried one video at a time, so I wouldn't design your cohort system around trying to pull raw retention curves for thousands of videos. developers.google.com

If your channel isn't huge yet, there's an even simpler version: YouTube Analytics → Google Sheets → Looker Studio. You can graduate to BigQuery when the spreadsheet starts becoming painful.

If you tell me roughly how many videos you have (e.g. 100, 1,000, 10,000+) and whether you want this to be automated, I can recommend the exact stack and dashboard structure.

For an educational YouTube channel, I’d use YouTube Analytics + a BI layer, rather than trying to force everything into YouTube Studio.

My recommendation

YouTube's API exposes video, day/month, traffic-source, demographic and other dimensions, plus metrics such as estimated minutes watched, average view duration, views and subscribers gained. developers.google.com

The setup I'd build

YouTube Analytics API → BigQuery/Sheets → Looker Studio

Create a table where each video has:

• Video ID
• Upload date
• Upload month/cohort
• Video title
• Your educational category (e.g. Math, Physics, Exam Prep, Study Skills)
• Video length
• Views
• Watch time
• Average view duration
• Average percentage viewed
• Subscribers gained
• Traffic source
• Viewer country/age/etc., if useful

Then calculate a viewer-age cohort based on the video's upload date, rather than simply looking at calendar-month performance.

For example:

That lets you answer much more useful questions such as:

• Which subjects generate the most watch time per upload?
• Does Physics have a stronger long tail than Math?
• How much watch time does the typical video generate in its first 7/30/90 days?
• Are newer uploads outperforming older uploads at the same age?
• Which upload months produced the strongest cohorts?
• Does video length affect the shape of the watch-time curve?
• Which categories generate evergreen traffic versus launch-driven traffic?

One important wrinkle: YouTube's audience-retention data is a separate, video-level report and the API's retention report can only be retrieved for one video at a time. developers.google.com So I'd use retention as a second layer rather than trying to make it the primary cohort dataset.

If you want the simplest answer

Small/medium channel:
YouTube Analytics API → Google Sheets → Looker Studio

Large channel / lots of historical analysis:
YouTube Analytics API → BigQuery → Looker Studio

I'd pick BigQuery + Looker Studio if your goal is specifically "break down watch time and viewer cohorts by upload date × educational category." It gives you a much better foundation for eventually adding dimensions like video length, topic, traffic source and subscriber status.

If you tell me roughly how many videos you have and whether your categories are already in YouTube playlists/tags or need to be manually assigned, I can recommend the exact architecture and cohort schema I'd use.

For what you’re describing, I’d use YouTube Analytics + a BI tool, rather than trying to do everything inside YouTube Studio.

My recommendation

1. Best overall: Looker Studio + YouTube Analytics data

Use YouTube Analytics as the source of truth and Looker Studio for the cohort analysis/dashboard.

You can build a dataset where each video has:

• Upload date
• Video/category
• Video length
• Views
• Watch time / estimated minutes watched
• Average view duration
• Average percentage viewed
• Subscribers gained
• Traffic source
• Audience retention
• Daily performance since upload

The YouTube Analytics API supports video and day dimensions and metrics such as estimated watch time, and it supports custom combinations of dimensions/filters for reporting. developers.google.com

The important trick is to calculate "days since upload" yourself:

cohort_day = activity_date - upload_date

Then you can make a matrix like:

That's much more useful than simply looking at total watch time, because you're comparing videos at the same age.

Other good options

2. Power BI — best if you already use Microsoft/Excel heavily. It's particularly good if you want to combine YouTube data with a spreadsheet containing your own taxonomy, e.g. Math → Algebra → SAT.

3. Tableau — excellent for sophisticated exploratory analysis, but probably overkill for a single educational channel.

4. BigQuery + Looker Studio — my choice if your channel is large or you want a durable analytics system. Pull YouTube data into BigQuery, create a clean video-level fact table, and build dashboards on top.

One important distinction

If by "viewer cohorts" you mean:

"People who first watched my channel in January—how much do they continue watching in February, March, etc.?"

that's a different analysis from:

"Videos uploaded in January—how much watch time do they accumulate during their first 30/90/180 days?"

The second is relatively straightforward with YouTube Analytics data. The first requires more careful cohort construction and depends on what viewer-level/aggregated data is actually available to your channel.

YouTube's retention reporting can also give you 100 points across a video's elapsed duration, so you can analyze where viewers drop off in addition to cohort performance. developers.google.com

For an educational channel, I'd probably build:
YouTube Analytics → BigQuery → Looker Studio, with upload cohort × video category × days-since-upload as the core model.

If you tell me roughly how many videos you have and whether you already use Google Sheets/Excel, I can recommend the simplest setup and the exact fields/dashboard I'd build.