The idea is the figure out the success ratio of youtube content creators and answer some of the basic questions like, how videos is takes for a channel to become successful, what language to choose, what type of content works and establish proof of success with Data and help them make a decision.

Hence the entire team of Business Analyst Interns at KultureHire took the responsibility of collecting and cleaning the data and brought it to an decent shape.

The dataset has 22 fields/columns and over 900 rows or 900 different videos from various youtube channels to it.

Preferred file format is Xlsx or CSV.

Dataset Description: Top 100 Influencers

The dataset provides structured information about the top 100 influencers from various countries globally. Each entry represents an influencer and includes the following attributes:

• Rank: The ranking of the influencer in the top 100 list.
• Name: The name or pseudonym of the influencer.
• Follower Count: The total number of followers or subscribers the influencer has on their primary - platform(s).
• Engagement Rate: The level of interaction that the influencer's content receives from users on social media platforms, expressed as a percentage.
• Country: The geographical location or country where the influencer is based or primarily operates.
• Topic Of Influence: The niche or category in which the influencer specializes or creates content, such as fashion, beauty, technology, fitness, etc.
• Reach: The primary social media platform(s) where the influencer is active, such as Instagram, YouTube, TikTok, Twitter, etc.

YouTube is an American online video sharing and social media platform headquartered in San Bruno, California. It was launched on February 14, 2005, by Steve Chen, Chad Hurley, and Jawed Karim. It is owned by Google, and is the second most visited website, after Google Search. YouTube has more than 2.5 billion monthly users who collectively watch more than one billion hours of videos each day. As of May 2019, videos were being uploaded at a rate of more than 500 hours of content per minute.

In October 2006, 18 months after posting its first video and 10 months after its official launch, YouTube was bought by Google for $1.65 billion. Google's ownership of YouTube expanded the site's business model, expanding from generating revenue from advertisements alone, to offering paid content such as movies and exclusive content produced by YouTube. It also offers YouTube Premium, a paid subscription option for watching content without ads. YouTube and approved creators participate in Google's AdSense program, which seeks to generate more revenue for both parties. YouTube reported revenue of $19.8 billion in 2020. In 2021, YouTube's annual advertising revenue increased to $28.8 billion.

This dataset consists details on top 1000 influencers all over the world.

Important : its a 3 month gap data Starting from March 2022 to Dec 2022

Influencers are categorized by the number of followers they have on social media. They include celebrities with large followings to niche content creators with a loyal following on social-media platforms such as YouTube, Instagram, Facebook, and Twitter.Their followers range in number from hundreds of millions to 1,000. Influencers may be categorized in tiers (mega-, macro-, micro-, and nano-influencers), based on their number of followers.

Businesses pursue people who aim to lessen their consumption of advertisements, and are willing to pay their influencers more. Targeting influencers is seen as increasing marketing's reach, counteracting a growing tendency by prospective customers to ignore marketing.

Marketing researchers Kapitan and Silvera find that influencer selection extends into product personality. This product and benefit matching is key. For a shampoo, it should use an influencer with good hair. Likewise, a flashy product may use bold colors to convey its brand. If an influencer is not flashy, they will clash with the brand. Matching an influencer with the product's purpose and mood is important.

https://sceptermarketing.com/wp-content/uploads/2019/02/social-media-influencers-2l4ues9.png">

Use our YouTube profiles dataset to extract both business and non-business information from public channels and filter by channel name, views, creation date, or subscribers. Datapoints include URL, handle, banner image, profile image, name, subscribers, description, video count, create date, views, details, and more. You may purchase the entire dataset or a customized subset, depending on your needs. Popular use cases for this dataset include sentiment analysis, brand monitoring, influencer marketing, and more.

YouTube is a global online video sharing and social media platform headquartered in San Bruno, California. It was launched on February 14, 2005, by Steve Chen, Chad Hurley, and Jawed Karim. It is owned by Google, and is the second most visited website, after Google Search. YouTube has more than 2.5 billion monthly users who collectively watch more than one billion hours of videos each day.

File containing two dataset about 100 top YouTube channels in world and India, based upon subscription. Both the dataset contains 6 columns. Column is named as ranking, channel_name, category, subscribers and average view.

1. T-series channel ranked first in the world and it gained 234 M subscriptions it is music based channel.
2. Cocomelon - Nursery Rhymes ranked second it is education based channel made for little babies and having 152 M subscriptions.
3. SET- India entertainment based channel has 150 M subscription. Among top 3 YouTube channels 2 channels are Indians channels. In all 100 YouTube channel 36% Entertainment channel 35% Music and only 29% from other category.

url="https://www.noxinfluencer.com/youtube-channel-rank/top-100-all-all-youtuber-sorted-by-subs-weekly"

Extensive Creator Coverage: Our dataset includes a diverse range of YouTube content creators, spanning various genres, subscriber counts, and regions. Access information on creators from a wide spectrum of content categories.

Creator Profiles: Explore detailed creator profiles, including biographies, subscriber counts, video counts, and contact information.

Customizable Data Delivery: The dataset is available in flexible formats, such as CSV, JSON, or API integration, allowing seamless integration with your existing data infrastructure. Customize the data to meet your specific research and analysis needs.

This dataset tracks influencer marketing campaigns across major social media platforms, providing a robust foundation for analyzing campaign effectiveness, engagement, reach, and sales outcomes. Each record represents a unique campaign and includes details such as the campaign’s platform (Instagram, YouTube, TikTok, Twitter), influencer category (e.g., Fashion, Tech, Fitness), campaign type (Product Launch, Brand Awareness, Giveaway, etc.), start and end dates, total user engagements, estimated reach, product sales, and campaign duration. The dataset structure supports diverse analyses, including ROI calculation, campaign benchmarking, and influencer performance comparison.

Columns: - campaign_id: Unique identifier for each campaign
- platform: Social media platform where the campaign ran
- influencer_category: Niche or industry focus of the influencer
- campaign_type: Objective or style of the campaign
- start_date, end_date: Campaign time frame
- engagements: Total user interactions (likes, comments, shares, etc.)
- estimated_reach: Estimated number of unique users exposed to the campaign
- product_sales: Number of products sold as a result of the campaign
- campaign_duration_days: Duration of the campaign in days

Getting Started with the Data

1. Load and Inspect the Dataset

import pandas as pd

df = pd.read_csv('influencer_marketing_roi_dataset.csv', parse_dates=['start_date', 'end_date'])
print(df.head())
print(df.info())

2. Basic Exploration

# Overview of campaign types and platforms
print(df['campaign_type'].value_counts())
print(df['platform'].value_counts())

# Summary statistics
print(df[['engagements', 'estimated_reach', 'product_sales']].describe())

3. Engagement and Sales Analysis

# Average engagements and sales by platform
platform_stats = df.groupby('platform')[['engagements', 'product_sales']].mean()
print(platform_stats)

# Top influencer categories by product sales
top_categories = df.groupby('influencer_category')['product_sales'].sum().sort_values(ascending=False)
print(top_categories)

4. ROI Calculation Example

# Assume a fixed campaign cost for demonstration
df['campaign_cost'] = 500 + df['estimated_reach'] * 0.01 # Example formula

# Calculate ROI: (Revenue - Cost) / Cost
# Assume each product sold yields $40 revenue
df['revenue'] = df['product_sales'] * 40
df['roi'] = (df['revenue'] - df['campaign_cost']) / df['campaign_cost']

# View campaigns with highest ROI
top_roi = df.sort_values('roi', ascending=False).head(10)
print(top_roi[['campaign_id', 'platform', 'roi']])

5. Visualizing Campaign Performance

import matplotlib.pyplot as plt
import seaborn as sns

# Engagements vs. Product Sales scatter plot
plt.figure(figsize=(8,6))
sns.scatterplot(data=df, x='engagements', y='product_sales', hue='platform', alpha=0.6)
plt.title('Engagements vs. Product Sales by Platform')
plt.xlabel('Engagements')
plt.ylabel('Product Sales')
plt.legend()
plt.show()

# Average ROI by Influencer Category
category_roi = df.groupby('influencer_category')['roi'].mean().sort_values()
category_roi.plot(kind='barh', color='teal')
plt.title('Average ROI by Influencer Category')
plt.xlabel('Average ROI')
plt.show()

6. Time-Based Analysis

# Campaigns over time
df['month'] = df['start_date'].dt.to_period('M')
monthly_sales = df.groupby('month')['product_sales'].sum()
monthly_sales.plot(figsize=(10,4), marker='o', title='Monthly Product Sales from Influencer Campaigns')
plt.ylabel('Product Sales')
plt.show()

Use Cases

• ROI Analysis: Quantify the return on investment for influencer campaigns across platforms and categories.
• Campaign Benchmarking: Compare campaign performance by type, influencer niche, or platform.
• Trend Analysis: Track engagement, reach, and sales trends over time.
• Influencer Selection: Identify high-performing influencer categories and campaign types for future partnerships.

This dataset contains anonymized YouTube comment data associated with the 2019 online controversy known as Dramageddon, involving beauty influencers James Charles, Tati Westbrook, and Jeffree Star. The dataset was created for research on online hostility, cancel culture, and competitive communication dynamics among influencers.

The dataset includes public user comments collected from 14 YouTube videos posted during May–June 2019, including primary source videos from the influencers involved and reaction videos from commentary channels. A total of ~15,000 comments were collected using the YouTube Data API v3. All comments are anonymized and contain no personally identifiable information.

Each comment record is enriched with metadata and derived variables, including: - Sentiment score (range −1 to +1) - Toxicity score (probability 0–1) - Cancel behavior classification (cold, cool, hot) - Moral language category - Engagement metrics (likes, reply depth) - Time of posting - Video-level metadata (creator, phase of controversy)

This dataset supports research in computational social science, communication studies, digital sociology, and platform governance. It has been used in studies on cancel culture, moral contagion, algorithmic amplification, and influencer reputation dynamics. This dataset contains only publicly available YouTube comments retrieved in accordance with the YouTube Terms of Service. All usernames, channel IDs, and profile references were hashed or removed during preprocessing to ensure anonymization. No attempts were made to identify or contact any YouTube users. The dataset is provided strictly for research purposes. Users must agree to comply with ethical guidelines for internet research (AoIR 2019) and cite the dataset appropriately.

Use our YouTube Videos dataset to extract detailed information from public videos and filter by video title, views, upload date, or likes. Data points include video URL, title, description, thumbnail, upload date, view count, like count, comment count, tags, and more. You can purchase the entire dataset or a customized subset, tailored to your needs. Popular use cases for this dataset include trend analysis, content performance tracking, brand monitoring, and influencer campaign optimization.

A collection of YouTube giants, this dataset offers a perfect avenue to analyze and gain valuable insights from the luminaries of the platform. With comprehensive details on top creators' subscriber counts, video views, upload frequency, country of origin, earnings, and more, this treasure trove of information is a must-explore for aspiring content creators, data enthusiasts, and anyone intrigued by the ever-evolving online content landscape. Immerse yourself in the world of YouTube success and unlock a wealth of knowledge with this extraordinary dataset.

Key Features:

• rank: Position of the YouTube channel based on the number of subscribers • Youtuber: Name of the YouTube channel • subscribers: Number of subscribers to the channel • video views: Total views across all videos on the channel • category: Category or niche of the channel • Title: Title of the YouTube channel • uploads: Total number of videos uploaded on the channel • Country: Country where the YouTube channel originates • Abbreviation: Abbreviation of the country • channel_type: Type of the YouTube channel • video_views_rank: Ranking of the channel based on total video views • country_rank: Ranking of the channel based on the number of subscribers within its country • channel_type_rank: Ranking of the channel based on its type video_views_for_the_last_30_days: Total video views in the last 30 days • lowest_monthly_earnings: Lowest estimated monthly earnings from the channel • highest_monthly_earnings: Highest estimated monthly earnings from the channel • lowest_yearly_earnings: Lowest estimated yearly earnings from the channel • highest_yearly_earnings: Highest estimated yearly earnings from the channel • subscribers_for_last_30_days: Number of new subscribers gained in the last 30 days • created_year: Year when the YouTube channel was created • created_month: Month when the YouTube channel was created • created_date: Exact date of the YouTube channel's creation • Gross tertiary education enrollment (%): Percentage of the population enrolled in tertiary education in the country • Population: Total population of the country • Unemployment rate: Unemployment rate in the country • Urban_population: Percentage of the population living in urban areas • Latitude: Latitude coordinate of the country's location • Longitude: Longitude coordinate of the country's location

To help the influencer Marketing campaigns for Brands and agencies to analyze the trust worthiness of Influncers across India, we at YourExcelguy took this initiative to collect and analyze the data of the influencers (micro & macro influencers).

File Format is Xlsx

Adolescent obesity remains a public health concern, exacerbated by unhealthy food marketing, particularly on digital platforms. Social media influencers are increasingly utilized in digital marketing, yet their impact remains understudied. This research explores the frequency of posts containing food products/brands, the most promoted food categories, the healthfulness of featured products, and the types of marketing techniques used by social media influencers popular with male and female adolescents. By analyzing these factors, the study aims to provide a deeper understanding of how social media influencer marketing might contribute to dietary choices and health outcomes among adolescents, from a gender perspective, shedding light on an important yet underexplored aspect of food marketing. A content analysis was conducted on posts made between June 1, 2021, and May 31, 2022, that were posted by the top three social media influencers popular with males and female adolescents (13–17) on Instagram, TikTok, and YouTube (N = 1373). Descriptive statistics were used to calculate frequencies for posts containing food products/brands, promoted food categories, product healthfulness, and marketing techniques. Health Canada’s Nutrient Profile Model was used to classify products as either healthy or less healthy based on their content in sugar, sodium, and saturated fats. Influencers popular with males featured 1 food product/brand for every 2.5 posts, compared to 1 for every 6.1 posts for influencers popular with females. Water (27% of posts) was the primary food category for influencers popular with females, while restaurants (24% of posts) dominated for males. Influencers popular with males more commonly posted less healthy food products (89% vs 54%). Marketing techniques varied: influencers popular with females used songs or music (53% vs 26%), other influencers (26% vs 11%), appeals to fun or coolness (26% vs 13%), viral marketing (29% vs 19%), and appeals to beauty (11% vs 0%) more commonly. Influencers popular with males more commonly used calls-to-action (27% vs 6%) and price promotions (8% vs 1%). Social media influencers play a role in shaping adolescents’ dietary preferences and behaviors. Understanding gender-specific dynamics is essential for developing targeted interventions, policies, and educational initiatives aimed at promoting healthier food choices among adolescents. Policy efforts should focus on regulating unhealthy food marketing, addressing gender-specific targeting, and fostering a healthy social media environment for adolescents to support healthier dietary patterns.

YouTube is an American online video sharing and social media platform headquartered in San Bruno, California. It was launched on February 14, 2005, by Steve Chen, Chad Hurley, and Jawed Karim. It is owned by Google, and is the second most visited website, after Google Search. YouTube has more than 2.5 billion monthly users who collectively watch more than one billion hours of videos each day. As of May 2019, videos were being uploaded at a rate of more than 500 hours of content per minute.

Youtube is very much used to influence, educate, free university (for me also) people (the users followers) in a particular way for a specific issue - which can impact the order in some ways.

Description

The dataset, obtained through data extraction from top YouTube streamers using the HypeAuditor platform, contains valuable information related to the presence and performance of these content creators on the world's largest video-sharing platform. Below is a description of each of the variables included in the dataset:

• Rank: This variable indicates the position or ranking of the streamer on the list of top YouTube streamers. A lower number signifies a higher ranking.

• Username: It is the streamer's username on YouTube, allowing for the unique identification of each content creator.

• Categories: Represents the categories in which the streamer has tagged their content. Categories can span a wide variety of topics, including gaming, beauty, fashion, travel, comedy, and more.

• Subscribers: Indicates the average number of subscribers the streamer's YouTube channel has. This value represents the regular following of content by the audience.

• Country: Country where the content creator is located. This can provide insights into the primary audience of the creator and their base of operations.

• Visits: This variable records the average number of accumulated visits to the streamer's channel. It represents the average number of times the creator's videos have been viewed by viewers.

• Likes: Indicates the average number of "Likes" received on the streamer's videos. "Likes" are an engagement metric that shows how many viewers appreciate the content.

• Comments: Reflects the average number of comments left on the streamer's videos. Comments are an important form of audience interaction and participation.

• Links: Provides links or URLs to the streamer's YouTube channels, allowing direct access to their content.

Potential Uses of the Dataset:

• Trend Analysis: The data can be used to identify emerging trends in the most popular content categories and the growth of new streamers on the platform.

• Audience Study: It helps in understanding the average geography of the audience and content preferences in different regions worldwide.

• Marketing Strategy: Brands and companies can use this data to identify suitable streamers for collaborations and marketing campaigns based on their average performance metrics.

• Benchmarking: Streamers can compare their average performance with that of other creators in terms of subscribers, visits, likes, and comments.

• Content Creator Community Research: Researchers can utilize this dataset to study the YouTube content creator community and its impact on the platform.

• Content Recommendations: Recommendation platforms can use this data to enhance video recommendations to users based on average categories and performance metric averages.

• Audience Engagement Analysis: Average comments and likes can be analyzed to gain insights into average audience participation and engagement.

You can check the scraping code here

This dataset provides comprehensive social media profile links discovered through real-time web search. It includes profiles from major social networks like Facebook, TikTok, Instagram, Twitter, LinkedIn, Youtube, Pinterest, Github and more. The data is gathered through intelligent search algorithms and pattern matching. Users can leverage this dataset for social media research, influencer discovery, social presence analysis, and social media marketing. The API enables efficient discovery of social profiles across multiple platforms. The dataset is delivered in a JSON format via REST API.

This dataset captures the pulse of viral social media trends across TikTok, Instagram, Twitter, and YouTube. It provides insights into the most popular hashtags, content types, and user engagement levels, offering a comprehensive view of how trends unfold across platforms. With regional data and influencer-driven content, this dataset is perfect for:

• Trend analysis 🔍
• Sentiment modeling 💭
• Understanding influencer marketing 📈

Dive in to explore what makes content go viral, the behaviors that drive engagement, and how trends evolve on a global scale! 🌍

This dataset showcases the top 100 YouTube channels in 2024, including key metrics such as subscribers, total views, and video count. It has been manually curated to provide a snapshot of the leading channels as of December 2024. The data reflects the performance of channels across various genres, from entertainment to educational content, offering insights into the most influential creators of the year. This data is useful for marketers, content creators, and data scientists who want to analyze trends, audience engagement, and growth strategies of top YouTube influencers. By examining these metrics, users can gain valuable insights into content creation, viewer preferences, and platform dynamics. The dataset is based on publicly available YouTube statistics and third-party platforms, ensuring transparency and accuracy.

Context

This dataset represents real performance metrics of content on a YouTube channel. Each row seems to correspond to an individual video or campaign, with various KPIs (Key Performance Indicators) like views, watch time, reach, CTR, engagement, and earnings. The dataset can be used for YouTube performance analysis, influencer marketing evaluation, or content strategy optimization.

Name
Date : Date of video publishing or performance snapshot Video Title : Title or name of the video content Platform : Social platform, likely YouTube Influencer Name : Name of the content creator/influencer Subscribers : Total subscriber count at the time Views : Total video views Watch Time (Hours) : Total watch time in hours Average View Duration : Average watch duration per viewer Reach : Unique accounts reached Click Through Rate (%) : CTR for the video thumbnail Likes : Number of likes received Comments : Number of comments Shares : Number of shares Revenue : Revenue or earnings from video (AdSense or influencer payout)

Use Cases

Performance monitoring of YouTube content

ROI and ROAS analysis for influencer campaigns

A/B testing for video titles/thumbnails

Trend identification across different video types

Techsalerator’s YouTube & Video Data for Colombia

Techsalerator’s YouTube & Video Data for Colombia aggregates comprehensive insights from video platforms to provide a detailed view of the country’s digital video ecosystem. This dataset captures video metadata, creator activity, audience engagement, and content performance signals across Colombia, helping organizations analyze content trends, viewer behavior, and digital media consumption patterns.

This dataset is designed to support media platforms, advertisers, researchers, NGOs, and policy analysts seeking insights into video engagement, creator growth, and content distribution across Colombia.

For access to the full dataset, contact us at info@techsalerator.com or visit Techsalerator Contact Us.

Top 5 Key Data Fields

1. Video Metadata (Title, Category, Language, Upload Date)
   Identifies core attributes of videos including topic, format, and publication timing.

2. Channel & Creator Information
   Captures data on content creators, subscriber counts, upload frequency, and channel growth.

3. Engagement Metrics (Views, Likes, Comments, Shares)
   Measures audience interaction and content performance across videos.

4. Audience Demographics & Geography
   Provides insights into viewer location, age distribution, and engagement behavior.

5. Watch Time & Retention Metrics
   Tracks average viewing duration, retention rates, and drop-off points.

Top 5 YouTube & Video Market Trends in Colombia

1. Strong Spanish-Language Content Consumption
   Spanish dominates across entertainment, education, and news categories.

2. High Engagement with Music, Entertainment, and Influencer Content
   Music videos, vlogs, and influencer-driven content generate strong viewership.

3. Rapid Growth of the Creator Economy
   Colombian creators and influencers are increasingly shaping digital culture and brand engagement.

4. Increasing Demand for Educational and Tutorial Content
   Learning-based content, online courses, and how-to videos are gaining popularity.

5. Mobile-First and Social Media Integrated Viewing
   Most users consume video content via smartphones with strong cross-platform sharing.

Top 5 Applications of YouTube & Video Data in Colombia

1. Digital Advertising & Audience Targeting
   Enables advertisers to optimize campaigns based on audience demographics and engagement.

2. Influencer Marketing & Creator Partnerships
   Supports brands in identifying high-performing creators and partnerships.

3. Media & Entertainment Industry Insights
   Helps production companies understand content demand and viewer preferences.

4. Social Research & Cultural Trend Analysis
   Provides insights into public sentiment, digital behavior, and content trends.

5. Education & Public Awareness Campaigns
   Supports institutions and NGOs in delivering effective video-based communication.

Accessing Techsalerator’s YouTube & Video Data

To obtain Techsalerator’s YouTube & Video Data for Colombia, contact us at info@techsalerator.com with your specific data requirements. Custom datasets, historical records, and real-time analytics are available, with delivery within 24 hours and flexible access agreements upon request.

Included Data Fields

• Video Title & Description
• Channel Name & ID
• Upload Date & Time
• Video Category & Tags
• View Count
• Like, Comment, and Share Counts
• Subscriber Count
• Watch Time Metrics
• Audience Demographics
• Geographic View Distribution
• Engagement Rate
• Content Language
• Timestamp of Data Capture

Contact Information

For actionable insights into video performance, audience behavior, and digital media trends in Colombia, Techsalerator’s YouTube & Video Data empowers advertisers, researchers, media companies, NGOs, and policymakers with reliable, structured, and scalable intelligence.

📩 Email: info@techsalerator.com