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Long-form, and searchable

YouTube Data, Delivered

Public YouTube content and engagement metrics, normalised to the same schema as every other platform we cover and pushed into your stack. Nothing to build, nothing to maintain.

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source specification

Source
youtube.com
Objects
Videos, Channels, Comment threads, Transcripts, View and engagement counts
Formats
JSON · CSV · Parquet
Refresh
Continuous to daily
Scope
Public content only

about the platform

What makes YouTube different

YouTube is the only major social platform that is also a search engine, and that changes what its data is good for. Content here has a long tail — a review published three years ago still drives purchase decisions today, which means the archive matters as much as the new uploads, the reverse of every other network. The distinguishing asset is the transcript: long-form spoken content converted to text gives you minutes of substantive discussion per video instead of a caption, and a fifteen-minute product review contains more usable detail about a product than a hundred short-form posts about it.

why teams track it

Why YouTube data is worth having

For any considered purchase — software, electronics, vehicles, tools, anything expensive enough to research — YouTube reviews are a primary influence on the decision, and their transcripts are the richest public description of how your product is actually perceived in use. Product and competitive teams mine them for feature-level feedback, and AI teams use transcript corpora as one of the largest sources of natural spoken-register text available.

Visit youtube.com

applications

What teams build on YouTube data

Transcript mining for product feedback

A long review discusses specific features, specific failures, and specific comparisons, in sentences. Running transcripts through extraction gives you feature-level sentiment that no engagement metric could produce.

Share of voice in considered purchases

For categories where buyers research before buying, review coverage and its reception is a direct read on commercial perception — and on which competitor the reviewers keep reaching for as the comparison.

Creator and channel evaluation

Subscriber count, view history per video, and comment engagement across a channel’s recent output, which together price a sponsorship far better than a subscriber number alone.

Long-tail archive analysis

Unlike every other platform, old YouTube content keeps working. Tracking view accrual on older videos shows which narratives about your product are still compounding.

Speech and dialogue training corpora

Transcripts in natural spoken register, with topic, duration, and engagement metadata attached for filtering. One of the largest usable sources of unscripted spoken language.

Comment-thread audience research

Comments under a review are people arguing about whether to buy the thing, in public, with reasons. That is qualitative research you would otherwise pay a panel for.

what you get back

Fields in the YouTube feed

Platform-specific detail on top of the unified social schema, so a dashboard built on one platform works on the next without a rewrite.

  • Video title, description, tags, and publication date
  • View, like, and comment counts
  • Full transcripts where captions are available
  • Channel attributes — subscriber count, description, country, total views
  • Comment threads with author handle, like count, and reply nesting
  • Video duration and category

Public surfaces only

We collect what a logged-out visitor can see. No authentication is bypassed, no private content is touched, and no attempt is made to deanonymise anyone.

One schema across platforms

A post from any network arrives with the same field names, so adding a platform does not mean rewriting anything downstream.

Deletion propagates

When content is removed at source it drops out of subsequent deliveries, and retention windows on delivered data are configurable per engagement.

10+
Platforms covered
<60s
Detection latency
3
Delivery formats
99.9%
Uptime SLA

related sources

Tracked alongside YouTube

One platform is a partial view. These deliver on the same schema, so combining them is a configuration change rather than a project.

LinkedIn

The professional graph

Reddit

Unfiltered community discussion

Pinterest

Intent, months ahead

All platforms

Instagram, TikTok, X, YouTube and more

49 retail sources

Pair social signal with catalogue and price data

AI datasets

Use this feed as model training data

See every data product

questions

Frequently Asked Questions

Everything you need to know before you send us your first request.

Yes, where captions are available — which covers the large majority of substantive long-form content, since the platform auto-generates them broadly. Transcripts arrive as text with timing information preserved, so a passage can be located back in the video it came from. Where no captions exist we say so per video rather than returning an empty field without explanation.
Both. Channel monitoring picks up every new upload from a named set of creators or competitors, and term monitoring finds new videos matching your product and category keywords wherever they are published. Most engagements run the two together.
To the beginning of a channel, in most cases. YouTube’s long tail is one of the reasons to use it, so historical backfill is a normal part of an engagement rather than an exception — the three-year-old review still shaping purchase decisions is often the most valuable record in the set.
By default no — we deliver metadata, engagement counts, comments, and transcripts, which is what nearly every use case actually needs. Media collection is scoped explicitly where a use case requires it, since storage cost and licensing both change materially once binaries are involved.
Into the systems you already run. Normalised records land as JSON, CSV, or Parquet in S3, GCS, or Azure Blob, or straight into Snowflake or BigQuery, on whatever cadence you set. Webhooks push matches to your endpoints as they are detected, which is how most customers wire alerting. A solutions engineer sets the schema, cadence, and destination with you during onboarding.
Keeping the collector working is our responsibility. Extractors are monitored continuously, and when the platform changes its structure we patch upstream while the schema we deliver to you stays fixed. If a change causes a coverage gap we tell you which window was affected rather than quietly returning fewer records.
Pricing is scoped per engagement — the number of tracked terms, handles, or communities, the refresh cadence, and the delivery destinations involved. Historical backfill is quoted separately. Every engagement starts with a free sample run against your own brand terms, so you can judge the data before committing.

Still have questions?

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