During the first quarter of 2024, YouTube shorts recorded the highest engagement rate across all short video platforms and in-app features analyzed. Content hosted on YouTube in form of shorts had an engagement rate of **** percent, while TikTok reported an engagement rate of approximately **** percent. Facebook Reels had an engagement rate of around two percent, making the platform rank last for short-format user engagement.

Find the latest video marketing statistics, covering statistics on video content marketing, user trends and habits, B2B video marketing and more.

Introduction

Video Marketing Statistics: It has become a dominant force in the digital marketing world, offering a compelling way to deliver messages that engage and resonate with audiences. With its ability to captivate viewers and leave a lasting impression, video content has become an essential tool for brands looking to grab consumer attention.

As more businesses adopt video as a core marketing strategy, understanding the key statistics behind its rise and influence is crucial. These insights highlight how video is enhancing viewer engagement and driving higher conversion rates, ultimately transforming how brands interact with their customers. By staying updated on the latest trends and data in video marketing, businesses can harness its power to improve brand visibility and achieve tangible outcomes.

From March 2023 to August 2023, TikTok videos with a duration of over ** seconds saw approximately *** percent engagement rate. Videos of a duration of less than ** seconds saw engagement rates of around *** percent, while videos with a length of between ** and ** seconds saw an engagement rate of **** percent.

The YouTube Insights dataset offers valuable data for researchers, data scientists, and YouTube enthusiasts to explore video performance and engagement. This dataset focuses on key elements such as video titles, view counts, analytics, and subtitles.

With a wide range of YouTube videos, spanning various genres and upload dates, this dataset provides insights into video popularity and audience engagement. Researchers can analyze video titles to understand effective strategies for capturing viewer attention. View counts offer quantitative measures of video popularity, while analytics data provides metrics like likes, dislikes, comments, and shares.

The inclusion of subtitles enhances the dataset, enabling language pattern analysis, sentiment analysis, and keyword extraction. Researchers can uncover correlations between subtitles and video content to gain a deeper understanding of audience preferences and behavior.

The YouTube Insights dataset empowers users to discover valuable insights into YouTube's ecosystem, optimizing content creation and engagement strategies. It serves as a foundation for research, analysis, and innovation in the realm of online video platforms.

During the first quarter of 2024, Huge YouTube accounts, which had over 50,000 followers, reported an engagement rate of approximately *** percent on their short-format content. In comparison, engagement was sensibly lower on long-format videos, which reported an engagement rate of **** percent for Huge accounts. Medium YouTube accounts, which had a following between 2,001 and 10,000 users, reported engagement ratings of almost ***** percent on their Shorts, while long videos had an engagement of around **** percent.

In September 2022, social media users in the United States generated two billion likes and interactions on Instagram Reels. In comparison, TikTok generated approximately one billion likes in the examined period. Content posted on YouTube's own short-video feature - YouTube Shorts - generated 29 million likes in the examined period.

This dataset provides a comprehensive collection of metadata and performance statistics for a variety of YouTube videos. Each entry represents a single video and includes key information that can be used for a wide range of analytical and machine learning tasks.

Key Features:

~**video_id:** Unique identifier for each video.

~**title:** The title of the video, offering insight into its content.

~**description:** A detailed description of the video.

~**published_at:** The date and time the video was published.

~**channel_title:** The name of the YouTube channel that published the video.

~**tags:** Keywords associated with the video, useful for content analysis.

~**category_id:** A numerical ID representing the video's content category.

~**view_count:** The number of times the video has been viewed.

~**like_count:** The number of likes the video has received.

~**comment_count:** The number of comments on the video.

Potential Use Cases:

~**Performance Analysis:** Analyze video engagement and popularity over time.

~**Content and Trend Analysis:** Identify popular themes, channels, and tags.

~**Predictive Modeling:** Build models to predict video performance based on metadata like title, description, and tags.

~**Channel Benchmarking:** Compare the performance of different channels and content categories.

Note: This Data is valid, authentic and up to date till today (20-Aug-25)

YouTube Video Analytics Dataset

This dataset contains structured metadata and engagement statistics for YouTube videos. It is designed for data analysis, visualization, and machine-learning tasks such as trend forecasting, recommendation modeling, and engagement prediction.

🔍 Dataset Overview

Each row represents a single YouTube video and includes:

• Video metadata
• Channel information
• User engagement metrics
• Video category and duration details
• Computed ratios (engagement rate, likes/views, comments/views)
• Video age and publication characteristics

📌 Use Cases

• Predict video performance
• Analyze factors contributing to virality
• Study viewer engagement behavior
• Support content strategy and optimization
• Train machine-learning models for ranking, recommendations, or engagement prediction

In 2023, over 56 percent of the time spent on social media platforms for users in the United States was spent on social video activities. This represents an increase from the previous year, when the time spent engaging with social video among U.S. users was of around 53.3 percent, compared to 46.7 percent of the total social media time spent on other social network activities.

Comprehensive YouTube channel statistics for LIKE VIDEO, featuring 221,000 subscribers and 8,883,176 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Technology category and is based in BD. Track 125 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

Introduction

Online Video Platform Statistics: An Online Video Platform (OVP) is a crucial digital infrastructure for hosting, managing, and delivering video content online.

It facilitates content uploading, organization, and playback across various devices with adaptive streaming capabilities.

OVPs support monetization through advertising, subscriptions, or pay-per-view models alongside robust analytics for tracking viewer engagement and performance metrics.

They offer customization options for branding and player interfaces, ensuring a seamless user experience. Security features like encryption and DRM safeguard content, while integration with other platforms and APIs enables extended functionality and automation.

OVPs also cater to live streaming needs, making them versatile tools for media, entertainment, education, and corporate sectors seeking reliable video distribution solutions.

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Comprehensive YouTube channel statistics for Lion Family English, featuring 5,880,000 subscribers and 924,822,729 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Entertainment category and is based in US. Track 749 videos with daily and monthly performance data, including view counts, subscriber changes, and earnings estimates. Analyze growth trends, engagement patterns, and compare performance against similar channels in the same category.

YouTube Trending Videos Dataset

Exploring YouTube Trending Videos

By dskl [source]

About this dataset

Moreover it also reveals various engagement metrics such as the number of views the video has received, likes and dislikes it has garnered from viewership. Additionally information related to comment count on particular videos enables analysis regarding viewer interaction and response. Furthermore this dataset describes whether comments or ratings are disabled for a particular video allowing examination into how these factors impact engagement.

By exploring this dataset in-depth marketers can gain valuable insights into identifying trends in content popularity across different countries while taking into account timing considerations based on published day of week. It also opens up avenues for analyzing public sentiment towards specific videos based on likes vs dislikes ratios and comment count which further aids in devising suitable marketing strategies.

Overall,this informative dataset serves as an invaluable asset for researchers,data analysts,and marketers alike who strive to gain deeper understanding about trending video patterns,relevant metrics influencing content virality,factors dictating viewer sentiments,and exploring new possibilities within digital marketing space leveraging YouTube's wide reach

How to use the dataset

How to Use This Dataset: A Guide

In this guide, we will walk you through the different columns in the dataset and provide insights on how you can explore the popularity and engagement of these trending videos. Let's dive in!

Column Descriptions:

• title: The title of the video.
• channel_title: The title of the YouTube channel that published the video.
• publish_date: The date when the video was published on YouTube.
• time_frame: The duration of time (e.g., 1 day, 6 hours) that the video has been trending on YouTube.
• published_day_of_week: The day of week (e.g., Monday) when the video was published.
• publish_country: The country where the video was published.
• tags: The tags or keywords associated with the video.
• views: The number of views received by a particular video
• likes: Number o likes received per each videos
• dislike: Number dislikes receives per an individual vidoe 11.comment_count: number of comments

Popular Video Insights:

To gain insights into popular videos based on this dataset, you can focus your analysis using these columns:

title, channel_title, publish_date, time_frame, and** publish_country**.

By analyzing these attributes together with other engagement metrics such as views ,likes,**dislikes,**comments),comment_count you can identify trends in what type content is most popular both globally or within specific countries.

For instance: - You could analyze which channels are consistently publishing trending videos - Explore whether certain types of titles or tags are more likely to attract views and engagement. - Determine if certain days of the week or time frames have a higher likelihood of trending videos being published.

Engagement Insights:

To explore user engagement with the trending videos, you can focus your analysis on these columns:

likes, dislikes, comment_count

By analyzing these attributes you can get insights into how users are interacting with the content. For example: - You could compare the like and dislike ratios to identify positively received videos versus those that are more controversial. - Analyze comment counts to understand how users are engaging with the content and whether comments being disabled affects overall

Research Ideas

• Analyzing the popularity and engagement of trending videos: By analyzing the number of views, likes, dislikes, and comments, we can understand which types of videos are popular among YouTube users. We can also examine factors such as comment count and ratings disabled to see how viewers engage with trending videos.
• Understanding video trends across different countries: By examining the publish country column, we can compare the popularity of trending videos in different countries. This can help content creators or marketers understand regional preferences and tailor their content strategy accordingly.
• Studying the impact of video attributes on engagement: By exploring the relationship between video attributes (such as title, tags, publish day) and engagement metrics (views, likes), we can identify patterns or trends that influence a video's success on YouTube. This information can be...

Video Ad Engagement Prediction: 3 Million Labeled Impressions Dataset

Description

This dataset consists of 3 million labelled advertising auction lines, aimed at fostering advancements in Machine Learning, particularly in user engagement prediction with video ads.

This dataset is a product of extensive work by Cyrille Dubarry and was initially used for a Machine Learning class competition at École Polytechnique.

Objective

This dataset is designed to facilitate the prediction of the duration for which a user will engage with a video advertisement. Each entry in the dataset, marked by a unique AuctionID, represents an individual ad impression and includes a variety of contextual information about the user, publisher, and advertiser.

Content and Features

• auction_id: unique id for identifying each line
• timestamp: the timestamp (in seconds) of the ad impression
• creative_duration: the total duration of the video that has been played
• campaign_id: the advertising campaign id
• advertiser_id: the advertiser id
• placement_id: the id of a zone in the web page where the video was played
• placement_language: the language of this zone
• website_id: the corresponding website id
• referer_deep_three: the URL of the page where the video was played, truncated at its 3rd level
• ua_country: the country of the user who watched the video
• ua_os: the user's Operating System
• ua_browser: the user's internet browser
• ua_browser_version: the user's browser version
• ua_device: the user's device
• user_average_seconds_played: the average duration the user watched video ads in the past. It can be null if the user never watched any ad.
• seconds_played: the observed time the video has been watched. This is the quantity we are trying to predict.

Use Case

This dataset is highly valuable for data scientists and researchers aiming to build predictive models for user engagement with video advertisements. It provides insights into how various factors such as device type, user preferences, and ad placement can influence ad-watching behaviour.

License This dataset is shared under the CC0 1.0 Universal (CC0 1.0) Public Domain Dedication, which allows for unrestricted use, adaptation, and distribution in any medium for any purpose.

This dataset provides an in-depth look at YouTube video analytics, capturing key metrics related to video performance, audience engagement, revenue generation, and viewer behavior. Sourced from real video data, it highlights how variables like video duration, upload time, and ad impressions contribute to monetization and audience retention. This dataset is ideal for data analysts, content creators, and marketers aiming to uncover trends in viewer engagement, optimize content strategies, and maximize ad revenue. Inspired by the evolving landscape of digital content, it serves as a resource for understanding the impact of YouTube metrics on channel growth and content reach.

Video Details: Columns like Video Duration, Video Publish Time, Days Since Publish, Day of Week.

Revenue Metrics: Includes Revenue per 1000 Views (USD), Estimated Revenue (USD), Ad Impressions, and various ad revenue sources (e.g., AdSense, DoubleClick).

Engagement Metrics: Metrics such as Views, Likes, Dislikes, Shares, Comments, Average View Duration, Average View Percentage (%), and Video Thumbnail CTR (%).

Audience Data: Data on New Subscribers, Unsubscribes, Unique Viewers, Returning Viewers, and New Viewers.

Monetization & Transaction Metrics: Details on Monetized Playbacks, Playback-Based CPM, YouTube Premium Revenue, and transactions like Orders and Total Sales Volume (USD).

In 2024, the engagement rate on YouTube content experienced***************** compared to the previous year. The average engagement rate on YouTube was of **** percent in the last examined period, down from the **** percent recorded in 2023.

YouTube Video and Channel Analysis

YouTube Video and Channel Statistics

By VISHWANATH SESHAGIRI [source]

About this dataset

This dataset contains valuable information about YouTube videos and channels, including various metrics related to views, likes, dislikes, comments, and other related statistics. The dataset consists of 9 direct features and 13 indirect features. The direct features include the ratio of comments on a video to the number of views on the video (comments/views), the total number of subscribers of the channel (subscriberCount), the ratio of likes on a video to the number of subscribers of the channel (likes/subscriber), the total number of views on the channel (channelViewCount), and several other informative ratios such as views/elapsedtime, totalviews/channelelapsedtime, comments/subscriber, views/subscribers, dislikes/subscriber.

The dataset also includes indirect features that are derived from YouTube's API. These indirect features provide additional insights into videos and channels by considering factors such as dislikes/views ratio, channelCommentCount (total number of comments on the channel), likes/dislikes ratio, totviews/totsubs ratio (total views on a video to total subscribers of a channel), and more.

The objective behind analyzing this dataset is to establish statistical relationships between videos and channels within YouTube. Furthermore, this analysis aims to form a topic tree based on these statistical relations.

For further exploration or utilization purposes beyond this dataset description document itself, you can refer to relevant repositories such as the GitHub repository associated with this dataset where you might find useful resources that complement or expand upon what is available in this dataset.

Overall,this comprehensive collection provides diverse insights into YouTube video and channel metadata for conducting statistical analyses in order to better understand viewer engagement patterns varies parameters across different channels. With its range from basic counts like subscriber counts,counting no.of viewership per minute , timing vs viewership rate ,text related user responses etc.,this detailed Youtube Dataset will assist in making informed decisions regarding channel optimization,more effective targeting and creation of content that will appeal to the target audience

How to use the dataset

This dataset provides valuable information about YouTube videos and their corresponding channels. With this data, you can perform statistical analysis to gain insights into various aspects of YouTube video and channel performance. Here is a guide on how to effectively use this dataset for your analysis:

• Understanding the Columns:
  • totalviews/channelelapsedtime: The ratio of total views of a video to the elapsed time of the channel.
  • channelViewCount: The total number of views on the channel.
  • likes/subscriber: The ratio of likes on a video to the number of subscribers of the channel.
  • views/subscribers: The ratio of views on a video to the number of subscribers of the channel.
  • subscriberCount: The total number of subscribers of the channel.
  • dislikes/views: The ratio

Research Ideas

• Predicting the popularity of YouTube videos: By analyzing the various ratios and metrics in this dataset, such as comments/views, likes/subscriber, and views/subscribers, one can build predictive models to estimate the popularity or engagement level of YouTube videos. This can help content creators or businesses understand which types of videos are likely to be successful and tailor their content accordingly.
• Analyzing channel performance: The dataset provides information about the total number of views on a channel (channelViewCount), the number of subscribers (subscriberCount), and other related statistics. By examining metrics like views/elapsedtime and totalviews/channelelapsedtime, one can assess how well a channel is performing over time. This analysis can help content creators identify trends or patterns in their viewership and make informed decisions about their video strategies.
• Understanding audience engagement: Ratios like comments/subscriber, likes/dislikes, dislikes/subscriber provide insights into how engaged a channel's subscribers are with its content. By examining these ratios across multiple videos or channels, one can identify trends in audience behavior and preferences. For example, a high ratio of comments/subscriber may indicate strong community participation and active discussion around the videos posted by a particular YouTuber or channel

Acknowledgements

If you use this dataset in y...

YouTube Video Analytics Dataset (Cleaned)

This dataset contains detailed metadata and statistics for 23,059 YouTube videos across various categories. It has been cleaned and enhanced for research and machine learning purposes.

📦 Dataset Overview

• Total records: 23,059
• Total features: 25
• Cleaned and enriched with:
  • Engagement rate
  • Formatted duration
  • Category mapping
  • Tag count
  • Short video indicator
  • Location detection in title

📑 Features

Column Name	Description
video_id	Unique video identifier
title	Video title
description	Video description
published_at	Publish datetime
channel_id	Channel identifier
channel_title	Channel name
tags	List of video tags
category_id	Numerical category ID
views	Total views
likes	Total likes
comments	Total comments
duration_seconds	Duration in seconds
dimension	2d/3d video format
definition	SD/HD
caption	Captions available?
licensed_content	Indicates copyright content
topic	Associated YouTube topic
search_keyword	Keyword used to find the video
delete_col	Possibly deprecated info (mostly nulls)
duration_formatted	Human-readable duration
is_short	Boolean: Is it a YouTube Short?
Category	Mapped category name
engagement_rate	Calculated as (likes + comments) / views
count_tags	Number of tags
location in title	City/country extracted from title (if any)

💡 Potential Use Cases

• YouTube recommendation engine
• Sentiment or tag analysis on video titles and descriptions
• Engagement prediction model
• Time series analysis of views
• Content classification by category or topic

📊 Inspiration

• Which tags or topics have the highest engagement?
• Do shorter videos (Shorts) perform better in terms of views or engagement?
• What are the patterns in publishing time vs. views?
• Can we predict video category based on title/tags?

⚠️ License & Source

You should ensure any model or derivative dataset respects YouTube’s API Terms of Service. This dataset is for research and educational use only.

The dataset is first introduced in the following paper: Siqi Wu, Marian-Andrei Rizoiu, and Lexing Xie. Beyond Views: Measuring and Predicting Engagement in Online Videos. In AAAI International Conference on Weblogs and Social Media (ICWSM), 2018. Tweeted videos dataset This dataset contains YouTube videos published between July 1st and August 31st, 2016. To be collected, the video needs (a) be mentioned on Twitter during aforementioned collection period; (b) have insight statistics available; (c) have at least 100 views within the first 30 days after upload. Quality videos datasets These datasets contain videos deemed of high quality by domain experts. Vevo videos: Videos of verified Vevo artists, as of August 31st, 2016. Billboard16 videos: Videos of 2016 Billboard Hot 100 chart. Top news videos: Videos of top 100 most viewed News channels. freebase_mid_type_name.csv It maps a freebase mid to a real-world entity. See more details in this data description.