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.

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.

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.

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"

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

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.

Babbl Labs' YouTube Experts, Influencers, and Executives dataset provides systematic intelligence on C-suite communications outside traditional investor relations channels, capturing strategic insights and market signals that never appear in earnings calls or SEC filings.

With data for more than 1,000 publicly traded companies monitored across over 25,000 curated channels and more than 1 million videos monitored per month, this dataset tracks when, where, and what CEOs and senior executives say across podcasts, industry conferences, media interviews, and YouTube creator content.

Our platform employs advanced AI/ML technologies including proprietary voice-print verification that accurately identifies executives across channels and appearances. Beyond simple speaker identification, the system analyzes topic themes, competitive mentions, and strategic discussions that often precede major corporate announcements by days or weeks. Multi-dimensional sentiment analysis detects shifts in executive confidence, crisis communication patterns, and competitive positioning statements.

The dataset captures executive commentary that moves markets—from unguarded moments in long-form interviews to strategic revelations at industry events. Investment firms use this intelligence for trading signals and risk assessment, while PR teams monitor for crisis communications and messaging consistency. Executive search firms track leadership transitions and succession planning discussions, and competitive intelligence teams analyze how executives discuss rivals and market positioning.

All data is structured for enterprise consumption with flexible delivery, enabling seamless integration with existing investment workflows and monitoring systems.

MrBeast Youtube Video Stats

MrBeast is one of the biggest youtubers ever. His videos are some of the most viewed of all time and he has perfected the art of gaining views.

This dataset was created using youtube's official api and shows the date created, view count, comments, and upvote counts for all of MrBeast's videos as of December 20, 2021.

Babbl Labs' YouTube Product Reviews & Consumer Sentiment dataset transforms the world's largest video platform into a real-time market product research engine, capturing authentic consumer reactions and expert opinions at the critical research phase of the purchase journey - before buyers commit.

With over 5K+ curated product review channels and more than 500K+ videos monitored per month, this dataset tracks product sentiment across all major consumer categories including tech, gaming, beauty, automotive, fitness, entertainment, home appliances, and more. Unlike post-purchase reviews on e-commerce sites, YouTube captures consumers actively researching and considering purchases, providing early signals of market success or failure.

Our proprietary platform identifies and tracks both professional reviewers like MKBHD and everyday consumers sharing unboxing experiences. Advanced NLP analyzes specific product features, purchase intent signals ("I'm buying this" vs. "skip it"), and detailed comparisons between competing products. The system captures nuanced sentiment that text reviews miss; visible disappointment, genuine excitement, and real-world usage demonstrations.

For consumer brands, this intelligence reveals how products perform in the wild before sales data arrives. Investment firms leverage early review signals to predict earnings surprises and market share shifts. Market research teams access authentic consumer voice at scale, while PR teams monitor for potential issues before they escalate. The dataset includes explicit purchase recommendations, feature-level sentiment scoring, comparison data against competitors, and influencer reach metrics.

All data is structured with entity resolution linking products to publicly traded companies, delivered through enterprise-grade dashboards, S3, or API access for seamless integration with existing market intelligence workflows.

This dataset provides comprehensive analytics on Irfan Malik's YouTube channel for 2024, capturing video metrics such as views, likes, comments, and engagement rates. Irfan Malik is known for his engaging travel and lifestyle content, sharing experiences and stories from Pakistan and beyond. This dataset is ideal for analyzing trends in travel content, social media engagement, and audience preferences. It is suitable for projects in data science, social analytics, and trend analysis, offering insights into the impact and reach of a prominent YouTube influencer.

A curated list of YouTube channels from English-speaking countries (US, UK, Canada, Australia, etc.) focused on the business niche, each with between 20,000 to 25,000 subscribers. This dataset includes valuable contact information such as public email addresses, making it ideal for outreach, collaborations, sponsorships, or influencer marketing in the business space.

Description: The Marketing Campaign Performance Dataset provides valuable insights into the effectiveness of various marketing campaigns. This dataset captures the performance metrics, target audience, duration, channels used, and other essential factors that contribute to the success of marketing initiatives. With 200000 unique rows of data spanning two years, this dataset offers a comprehensive view of campaign performance across diverse companies and customer segments.

Columns: Company: The company responsible for the campaign, representing a mix of fictional brands. Campaign_Type: The type of campaign employed, including email, social media, influencer, display, or search. Target_Audience: The specific audience segment targeted by the campaign, such as women aged 25-34, men aged 18-24, or all age groups. Duration: The duration of the campaign, expressed in days. Channels_Used: The channels utilized to promote the campaign, which may include email, social media platforms, YouTube, websites, or Google Ads. Conversion_Rate: The percentage of leads or impressions that converted into desired actions, indicating campaign effectiveness. Acquisition_Cost: The cost incurred by the company to acquire customers, presented in monetary format. ROI: Return on Investment, representing the profitability and success of the campaign. Location: The geographical location where the campaign was conducted, encompassing major cities like New York, Los Angeles, Chicago, Houston, or Miami. Language: The language used in the campaign communication, including English, Spanish, French, German, or Mandarin. Clicks: The number of clicks generated by the campaign, indicating user engagement. Impressions: The total number of times the campaign was displayed or viewed by the target audience. Engagement_Score: A score ranging from 1 to 10 that measures the level of engagement generated by the campaign. Customer_Segment: The specific customer segment or audience category that the campaign was tailored for, such as tech enthusiasts, fashionistas, health and wellness enthusiasts, foodies, or outdoor adventurers. Date: The date on which the campaign occurred, providing a chronological perspective to analyze trends and patterns.

Scope: By leveraging this dataset, marketers and data analysts can uncover valuable insights regarding campaign performance, audience preferences, channel effectiveness, and ROI. This dataset serves as a valuable resource for market research, campaign optimization, and data-driven decision-making, enabling businesses to refine their marketing strategies and drive targeted growth.

**Note:** This is a fictional dataset.

📱 About Dataset Overview This Social Media Engagement Dataset contains comprehensive engagement metrics from 5,000 social media posts across six major platforms: Instagram, Twitter, Facebook, LinkedIn, TikTok, and YouTube. The dataset spans over 2 years (2024-2025) and provides valuable insights into content performance, audience engagement patterns, and influencer analytics.

Dataset Contents The dataset includes 20 detailed features covering various aspects of social media engagement:

Post Information Post_ID: Unique identifier for each post Timestamp: Date and time when the post was published Platform: Social media platform (Instagram, Twitter, Facebook, LinkedIn, TikTok, YouTube) Content_Type: Type of content (Photo, Video, Reel, Tweet, Story, etc.) Category: Content category (Technology, Fashion, Food, Travel, Fitness, Education, Entertainment, Business, Lifestyle, Gaming, Health, Sports) Engagement Metrics Likes: Number of likes/reactions received Comments: Number of comments on the post Shares: Number of shares/retweets/reposts Views: Total number of views Saves: Number of bookmarks/saves Engagement_Rate: Calculated engagement rate percentage Account Information Follower_Count: Number of followers of the account Influencer_Tier: Classification (Nano, Micro, Mid-tier, Macro) Is_Verified: Whether the account is verified (True/False) Content Characteristics Hashtag_Count: Number of hashtags used Content_Length: Length in characters (text) or seconds (video) Sentiment: Sentiment analysis (Positive, Neutral, Negative) Has_Media: Whether post contains media (True/False) Temporal Features Hour_of_Day: Hour when the post was published (0-23) Day_of_Week: Day of the week (Monday-Sunday) Use Cases This dataset is perfect for:

📊 Predictive Analytics: Build ML models to predict engagement rates 📈 Data Visualization: Create insightful dashboards and charts 🤖 Machine Learning: Classification, regression, and clustering tasks ⏰ Time Series Analysis: Analyze posting patterns and optimal timing 🎯 Content Strategy: Optimize content strategy based on data insights 🔍 Sentiment Analysis: Study correlation between sentiment and engagement 📱 Platform Comparison: Compare performance across different platforms 💼 Influencer Marketing: Analyze influencer tier performance Technical Details Format: CSV Size: ~651 KB Rows: 5,000 Columns: 20 Time Period: January 2024 - December 2025 Missing Values: None Potential Research Questions What time of day generates the most engagement? Which platform has the highest engagement rates? How does content type affect performance? Does verified status impact engagement? What's the optimal hashtag count? How does sentiment correlate with engagement? Notes Engagement metrics are platform-realistic and proportional All data is synthetically generated for educational and research purposes Suitable for beginners and advanced data scientists

Comprehensive YouTube channel statistics for Video Influencers, featuring 640,000 subscribers and 34,104,307 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 US. Track 625 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.