YouTube GenZ Mobile Viewing Dataset

Overview

The YouTube GenZ Mobile Viewing Dataset provides real-time behavioral data on how Gen Z users consume YouTube content on their mobile devices. The dataset is produced by Verb AI (by Generation Lab), which operates a consented panel of real US mobile device users. Viewing sessions are captured passively and in real time as panelists organically browse YouTube — no surveys, no recall bias, no simulated environments.

This dataset is ideal for market researchers, media strategists, alternative data analysts, and brand teams seeking ground-truth insights into Gen Z digital behavior.

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Methodology

Data Collection

Verb AI maintains a consented opt-in panel of US-based mobile device users. When a panelist watches a YouTube video, the session metadata is captured, enriched with publicly available YouTube API data (video details, engagement metrics, channel info), and written to Snowflake.

Key Properties

• Passive observation: No surveys or self-reporting. Data reflects actual viewing behavior.

• Real-time ingestion: Sessions appear in the dataset within minutes of occurring.

• Mobile-first: All data originates from mobile devices, reflecting Gen Z's dominant consumption mode.

• Consented panel: All participants have explicitly opted in to data collection.

Privacy

This dataset contains no personal identifiers. Specifically:

• No user IDs, device IDs, or IP addresses

• No tracking URLs or advertising identifiers

• Geographic data is included at the city/region level only, for aggregate analysis

• All data is derived from consented panelists

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Schema Reference

The dataset is served through a single table: YOUTUBE_VIDEO_SESSIONS.

Column	Type	Description
VIDEO_ID	VARCHAR	YouTube video identifier (the v= parameter from a YouTube URL). Primary key for joining with external YouTube data.
API_VIDEO_ID	VARCHAR	Video ID as returned by the YouTube Data API. Typically matches VIDEO_ID.
API_TITLE	VARCHAR	Title of the video as returned by the YouTube Data API.
API_KIND	VARCHAR	YouTube API resource type (e.g., youtube#video).
CHANNEL_ID	VARCHAR	Unique identifier of the YouTube channel that published the video.
CHANNEL_TITLE	VARCHAR	Display name of the YouTube channel.
CATEGORY_ID	VARCHAR	YouTube video category ID (e.g., 10 = Music, 20 = Gaming, 22 = People & Blogs). See YouTube category reference.
DESCRIPTION_TOP_LEVEL	VARCHAR	Full video description as set by the creator.
DESCRIPTION_SNIPPET	VARCHAR	Truncated snippet of the video description, suitable for previews and search results.
VIEW_COUNT	NUMBER	Total view count of the video at the time of the session (from YouTube API).
LIKE_COUNT	NUMBER	Total like count of the video at the time of the session.
COMMENT_COUNT	NUMBER	Total comment count of the video at the time of the session.
DEFINITION	VARCHAR	Video quality definition (e.g., hd, sd).
DURATION	VARCHAR	Video duration in ISO 8601 format (e.g., PT5M30S = 5 minutes 30 seconds).
TAGS	VARCHAR	Comma-separated list of tags assigned to the video by the creator.
THUMBNAIL_URL	VARCHAR	URL to the video's default thumbnail image.
PUBLISHED_AT	TIMESTAMP	Timestamp when the video was originally published on YouTube.
EVENT_TIME	TIMESTAMP	Timestamp when the viewing session was observed by Verb AI's panel.
COUNTRY	VARCHAR	Country of the panelist at the time of viewing (ISO 3166-1 alpha-2).
REGION	VARCHAR	State or region of the panelist.
CITY	VARCHAR	City of the panelist.

Note: Column availability may expand over time. Check the Snowflake table definition for the latest schema.

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Data Coverage & Freshness

Property	Value
Update frequency	Near real-time (minutes from session to table)
Geographic coverage	Global, primarily US-based panel, with international YouTube content observed
Historical depth	Rolling, contact Verb AI for exact date range
Approximate volume	Thousands of sessions per day (varies with panel size)

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Getting Started

1. Attach the listing

After subscribing through the Snowflake Marketplace, a shared database will appear in your account. You can rename the database to anything you prefer.

2. Query the data

All data lives in a single table. Reference it as:

SELECT * FROM <YOUR_DATABASE_NAME>.YOUTUBE_VIDEO_SESSIONS LIMIT 10;

Replace <YOUR_DATABASE_NAME> with whatever you named the shared database.

3. Trial access

A 7-day free trial is available. During the trial you have full access to the dataset with no restrictions on query volume.

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Sample Queries

Top videos by view count

Find the most popular videos among Gen Z viewers ranked by total view count.

SELECT 
    VIDEO_ID,
    API_TITLE AS TITLE,
    CHANNEL_TITLE,
    VIEW_COUNT,
    LIKE_COUNT,
    COMMENT_COUNT
FROM YOUTUBE_VIDEO_SESSIONS
ORDER BY VIEW_COUNT DESC
LIMIT 100;

Trending videos in the last 24 hours

Discover what Gen Z is watching right now.

SELECT 
    VIDEO_ID,
    API_TITLE AS TITLE,
    CHANNEL_TITLE,
    VIEW_COUNT,
    LIKE_COUNT,
    EVENT_TIME
FROM YOUTUBE_VIDEO_SESSIONS
WHERE EVENT_TIME >= DATEADD('day', -1, CURRENT_TIMESTAMP())
ORDER BY VIEW_COUNT DESC
LIMIT 100;

Top performing channels

Identify the most popular creators among Gen Z audiences for influencer partnerships and sponsorship opportunities.

SELECT 
    CHANNEL_ID,
    CHANNEL_TITLE,
    COUNT(DISTINCT VIDEO_ID) AS VIDEOS_WATCHED,
    SUM(VIEW_COUNT) AS TOTAL_VIEWS,
    SUM(LIKE_COUNT) AS TOTAL_LIKES
FROM YOUTUBE_VIDEO_SESSIONS
GROUP BY CHANNEL_ID, CHANNEL_TITLE
ORDER BY VIDEOS_WATCHED DESC
LIMIT 50;

Content category breakdown

Understand which content categories dominate Gen Z viewing time.

SELECT 
    CATEGORY_ID,
    COUNT(*) AS SESSION_COUNT,
    COUNT(DISTINCT VIDEO_ID) AS UNIQUE_VIDEOS,
    AVG(VIEW_COUNT) AS AVG_VIEWS
FROM YOUTUBE_VIDEO_SESSIONS
GROUP BY CATEGORY_ID
ORDER BY SESSION_COUNT DESC;

Geographic viewing patterns

Analyze regional content preferences across the US.

SELECT 
    REGION,
    CITY,
    COUNT(*) AS SESSION_COUNT,
    COUNT(DISTINCT VIDEO_ID) AS UNIQUE_VIDEOS
FROM YOUTUBE_VIDEO_SESSIONS
WHERE COUNTRY = 'US'
GROUP BY REGION, CITY
ORDER BY SESSION_COUNT DESC
LIMIT 50;

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Use Cases

• Trend detection: Identify emerging content trends and viral videos before they peak by tracking real-time Gen Z viewing patterns.

• Content strategy: Understand what video formats, durations, and topics drive engagement with Gen Z audiences.

• Geographic insights: Analyze regional content preferences and discover location-based viewing trends.

• Creator & brand analysis: Track channel performance and identify rising creators popular with Gen Z viewers.

• Market research: Gain insights into Gen Z interests, preferences, and cultural moments as they happen.

• Advertising intelligence: Optimize ad placements by understanding which content categories and channels capture Gen Z attention.

• Alternative data: Use real-time behavioral signals for investment research, competitive intelligence, and consumer sentiment analysis.

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FAQ

How is this different from YouTube Analytics or the YouTube API? YouTube Analytics shows creators their own channel data. The YouTube API provides public metadata. Neither reveals who is watching what, when, and where at a demographic level. This dataset captures actual viewing sessions from a consented Gen Z panel, providing demand-side behavioral data that doesn't exist in any YouTube-provided product.

Can I identify individual users? No. The dataset contains no user-level identifiers. All geographic data is at the city/region level for aggregate analysis only.

How large is the panel? Panel size is proprietary and changes over time. Contact Verb AI for current panel metrics.

What does "near real-time" mean? Viewing sessions typically appear in the Snowflake table within minutes of occurring on the panelist's device.

Can I get historical backfill? Contact Verb AI to discuss historical data availability.

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Support & Contact

For questions about the dataset, partnership inquiries, or support:

• Documentation: https://docs.generationlab.org

• Email: [email protected]

• Website: https://verbai.generationlab.org