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.

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.

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.

During a 2025 survey among marketers worldwide, approximately ** percent reported plans to increase their use of YouTube for video marketing purposes in the near future. Similarly, ** percent said they would use Instagram more for that purpose. According to the same study, increased exposure and traffic were the leading benefits of social media marketing worldwide.

When Maya launched her handmade jewelry business, she poured hours into perfecting her craft, but her online sales stalled. One day, she filmed a simple 60-second product demo on her phone, posted it on social, and forgot about it. Within a week, that video had brought in five times her...

A curated dataset of 2025 video marketing and GenAI-in-ads statistics: online video time, short vs long video share, business adoption and ROI, YouTube CTV reach, and GenAI usage and projections.

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.

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.

A dataset containing statistics and survey results about the effectiveness and usage of explainer videos in the USA and UK for the period of 2024-2025.

Introduction

Online Video Consumption Statistics: Video is now the top choice for content. With 93% of marketers using video in their overall marketing plans, the role of video in marketing has grown significantly in recent years. Social media companies have also boosted this trend by focusing on tools for creating video content.

If you need more clarification about investing in video marketing, this article gathers the latest trends from various studies. Video marketing gives marketers many ways to grow their business and promote their brands. This article will shed more light on "Online Video Consumption Statistics†.

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)

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.

Comprehensive YouTube channel statistics for Video Game Remixes, featuring 268,000 subscribers and 155,322,994 total views. This dataset includes detailed performance metrics such as subscriber growth, video views, engagement rates, and estimated revenue. The channel operates in the Music category and is based in US. Track 211 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 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

This dataset contains a collection of detailed statistics from MrBeast's YouTube videos, one of the most popular content creators on the platform. The data includes various attributes of each video, such as title, publication date, view counts, likes, comments, and video duration. This dataset provides a comprehensive look into the performance metrics of MrBeast’s content, allowing for analysis of viewer engagement, content trends, and popularity growth.

Key Features:

title: The title of the YouTube video. description: The video description, as provided on the video’s page. published_at: The date and time the video was published on YouTube (in YYYY-MM-DD HH:MM format). views: The number of views the video has received. likes: The number of likes the video has received. comments: The number of comments on the video. duration_in_minutes: The total duration of the video in minutes. unique_thumbnail_urls: URLs to the unique thumbnails used for the video. unique_thumbnail_url_count: The count of unique thumbnail URLs per video. Usage:

This dataset can be useful for:

Analyzing the growth and engagement metrics of one of YouTube’s top creators. Performing time series analysis to understand trends in video performance over time. Building predictive models on video performance based on title, description, and engagement metrics. Analyzing video duration effects on views, likes, and comments.

Source: All data was collected from publicly available YouTube videos on the MrBeast channel.

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.

Description:

This dataset contains statistics for a selection of YouTube videos, capturing metrics such as views, comments, likes, dislikes, and the timestamp when the data was recorded. The dataset provides insights into the popularity and engagement levels of these videos as of April 15, 2019. This data can be useful for analyzing trends in video performance, user engagement, and the impact of content over time.

File Description: This CSV file contains detailed statistics for a set of YouTube videos, including unique video identifiers and various engagement metrics. Each row represents a different video, and the columns provide specific data points related to the video's performance.

Column Descriptions

videostatsid: Unique identifier for each video statistics entry. ytvideoid: Unique YouTube video identifier. views: The total number of views the video has received. comments: The total number of comments posted on the video. likes: The total number of likes the video has received. dislikes: The total number of dislikes the video has received. timestamp:The date and time when the statistics were recorded, in the format YYYY-MM-DD HH:MM

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.

About the Dataset : Youtube Views Prediction

This dataset contains information about trending YouTube videos from multiple countries, providing valuable insights for predicting video popularity based on various attributes. The dataset includes both numerical and categorical features that are essential for analyzing viewer behavior, engagement, and trends in content creation. The original source of this dataset can be found at : https://www.kaggle.com/datasets/datasnaek/youtube-new/data

Columns and Their Descriptions:

title: The title of the YouTube video.
channel_title: Name of the channel that published the video.
trending_date: The date the video started trending.
publish_date: The original upload date of the video.
publish_time: The exact time the video was published.
views: The total number of views the video received.
likes: The number of likes the video received.
dislikes: The number of dislikes the video received.
comment_count: The total number of comments on the video.
tags: Keywords or tags associated with the video, helping discoverability.
description: A detailed text description provided by the uploader.
category_id: The category assigned to the video (e.g., Music, Gaming, News).

Business Goals :

Predicting the number of views on youtube videos based on video attributes. The goal is to develop a model that can accurately predict the number of views a video will receive, using various video attributes such as likes, shares, comments, video duration, and more.

Business Metrics :

1. RMSE (Root Mean Squared Error) RMSE is a metric that measures the magnitude of the error between the values predicted by the model (Predicted Views) and the actual values (Actual Views). The lower the RMSE value, the more accurate the model's predictions.

2. R² (Coefficient of Determination) R² measures the extent to which the model can explain the variation in the data. R² values range from 0 to 1, where 1 means the model can explain all the variation in the number of views based on the given attributes, and 0 means the model cannot explain the variation. The higher the R², the better the model is at predicting views and the more relevant the features used in the model.

Model & Evaluation

The machine learning model was evaluated using several approaches, including different pre-processing techniques and multiple ML models. Ultimately, the chosen model for this analysis is the Random Forest Regressor. The final evaluation results show an RMSE of 630.741, indicating an average prediction error of approximately 630.741 units. Additionally, the R² score is 0.9623, meaning that the model explains 96.23% of the variance in the data (number of views). These results were deemed satisfactory and were selected as the final modeling approach for the system and its potential future applications.

📢 Published on : My LinkedIn