Dataset

This dataset was created by Nguyên Nguyễn Nhật

Contents

This database, updated daily, contains ads that ran on Facebook and were submitted by thousands of ProPublica users from around the world. We asked our readers to install browser extensions that automatically collected advertisements on their Facebook pages and sent them to our servers. We then used a machine learning classifier to identify which ads were likely political and included them in this dataset.

As the 2025 Midterm Election in the Philippines nears, Facebook Meta Ad dataset was gathered from https://www.facebook.com/ads/library/.

This dataset should be helpful for gathering insights on current trends in Philippine politics based on Facebook Ad Campaigns.

The dataset was collected starting from Jan 1, 2024 until Dec 31, 2024 with a search query "election 2025". It includes active and inactive ads.

Context

This dataset contains campaign data for both Facebook Ads and AdWords, offering a side-by-side comparison of performance metrics, costs, and conversions. It's an ideal resource for A/B testing in marketing, especially for analyzing the effectiveness of ad campaigns across platforms.

Source

This dataset was created from scratch using Mockaroo, ensuring it is tailored for practical use.

Inspiration

While watching a YouTube tutorial 👉 [https://youtu.be/iCj4lT5KvJk?si=FijILsrbxBrcE3pw])(url), I noticed that the tutorial lacked an uploaded dataset, and many viewers in the comment section requested one. To help others follow along and practice, I decided to create a mock dataset from scratch. Now, you can easily replicate the tutorial and enhance your skills!

Analysis Ideas

Platform Performance Comparison: Compare key metrics like CTR, conversion rate, and cost per click between Facebook Ads and AdWords.

Trend Over Time: Analyze changes in ad performance metrics across different years.

A/B Testing Insights: Assess simultaneous campaigns to identify the better-performing platform.

Cost Efficiency: Identify campaigns with low costs but high conversions on each platform.

Visualization of Metrics: Create charts to visually compare campaign performance. Statistical Insights: Perform hypothesis testing to check for significant differences in performance metrics. Recommendations for Marketing Strategy: Provide actionable suggestions based on the data analysis. # Enjoy exploring and testing this dataset for your marketing analyses!

Simple Dataset from different marketing campaigns.

The total conversion number shows the total number of signups or installs for instance while approved conversions tells how many became actual active users.

Courtesy of Bunq.

The Social Media Ad Optimization Dataset provides a comprehensive collection of user interaction data related to digital advertising campaigns. It is designed to support research in predictive modeling, targeted advertising, and AI-driven campaign optimization.

Key Features: User Demographics: Age, gender, location, and interests.

Ad Metadata: Ad ID, category, platform, type, and textual content.

User Engagement Data: Impressions, clicks, conversions, and time spent on ads.

Temporal Information: Interaction timestamps and day of the week.

Device Insights: Device type used for accessing the ad.

Applications: Ad engagement prediction and conversion modeling.

Behavioral analysis for personalized targeting.

Optimization of ad delivery strategies using AI.

The dataset includes 1,000 rows of PPC campaign performance data, and the following columns:

• Campaign_ID: Unique identifier for each campaign.
• Budget: The allocated budget for the campaign (in USD).
• Clicks: The number of user clicks generated by the campaign.
• CTR (Click-Through Rate): The ratio of clicks to impressions, indicating the effectiveness of the campaign in attracting clicks.
• CPC (Cost Per Click): The average cost per click, calculated as the budget divided by the number of clicks.
• Conversions: The number of actions or desired outcomes (e.g., purchases, sign-ups) generated by the campaign.
• CPA (Cost Per Acquisition): The average cost to acquire one conversion, calculated as the budget divided by conversions.
• Conversion_Rate: The ratio of conversions to clicks, representing the effectiveness of the campaign in turning clicks into actions.
• Duration: The length of the campaign in days.
• Platform: The digital platform where the campaign was run (e.g., Instagram, LinkedIn, YouTube, Facebook, Google).
• Content_Type: The type of content used in the campaign (e.g., Image, Video, Carousel, Text).
• Target_Age: The target age group for the campaign (e.g., 18-24, 25-34, etc.).
• Target_Gender: The target gender for the campaign (e.g., Male, Female, Other).
• Region: The geographic region where the campaign was targeted (e.g., North America, Europe, Asia).
• Revenue: The total revenue generated by the campaign from conversions.
• Spend: The total amount spent on the campaign.
• ROAS (Return on Ad Spend): A key performance metric, calculated as the revenue divided by the spend, representing how effectively the ad spend generated revenue.
• Date: The date when the data was recorded.
• Impressions: The total number of times the ad was displayed, calculated as clicks divided by CTR.

Problem Statement

The following Data Analysis of Marketing Campaigns is a part of the Assignment for Data Science Intern at Merkle Sokrati.

Objectives of the Task

• Carry out EDA and build ML model to evaluate the insights automatically.
• Prepare a summary of your Analysis and put that into a professional looking deck.

Content

Marketing campaigns containing data from Oct’19 to July’20. This data is from Google and Facebook campaigns which shows the performance of different Age-groups for different dimensions.

All the key fields like Platform, Type, Medium, Sub Channel, Audience, Creative have already been mapped to the data. - Platform: Marketing platforms on which campaigns are running majorly: Google Ads and Facebook Ads. - Type: Type of campaign, In this data, only Google search and Facebook Conversion campaigns have been considered. - Medium: The way we are connecting to people in our Marketing campaigns either via some Keywords or Creatives. - Sub Channel: Subchannel is under Google Search which type of keywords have been targeted, In Facebook which on subchannel we are targeting. - Audience: Multiple Type of audiences are getting targeted in different campaigns and those have been encrypted as Audience 1,2,3. - Creative: This if for Facebook what type of Image/Video/Carousel we are using in our Ads.

Acknowledgements

Merkle Sokrati has been sending these assignments for data analyst, data science intern positions and not replying back after the submission. So why not democratize my work and save other's time.

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.

Dataset Description — Education Marketing Campaign Performance Dataset (2024–2025)

Overview

This dataset represents hypothetical marketing campaign performance data created specifically for an educational institution’s marketing analytics dashboard. It simulates real-world digital marketing campaigns across multiple platforms such as Google Ads, Facebook, Instagram, LinkedIn, and YouTube.

The dataset includes campaign metrics recorded between January 2024 and October 2025, covering key performance indicators such as:

• Impressions
• Clicks
• Leads
• Applications
• Enrollments
• Cost (₹)
• Revenue (₹)
• Target audience demographics
• Campaign objectives
• Platforms and regions

This dataset was artificially generated using ChatGPT for academic, analytical, and dashboard development purposes, particularly for Power BI projects.

Files Included

1. CampaignPerformance

Daily/periodic tracking of campaign results:

• Date
• Campaign ID & Name
• Platform (Google Ads, Facebook, Instagram, etc.)
• Target Audience
• Impressions
• Clicks
• Leads
• Applications
• Enrollments
• Campaign Cost
• Revenue Generated
• Region (North, South, East, West, Pan India)

2. CampaignMeta

Detailed metadata for each campaign:

• Campaign ID
• Objective
• Start & End Dates
• Budget Allocation
• Campaign Type
• Creative Type
• Marketing Manager
• Channel
• Conversion Goal

3. ChannelRates

Average benchmark metrics for each platform:

• Channel
• Avg CPC (Cost Per Click)
• Avg CPM (Cost Per Mille)
• Notes

Purpose of the Dataset

This dataset was designed for:

• Marketing analytics practice
• ROI and campaign effectiveness analysis
• Data modeling and DAX calculations
• Academic and portfolio projects

Because no real institutional data can be shared, this dataset provides a safe, anonymous, and realistic alternative for learning and experimentation.

Intended Use Cases

• Data Modeling (Star Schema, Fact & Dimension tables)
• Digital Marketing Case Studies
• Machine Learning Practice
• Dashboard UI/UX Projects
• Kaggle Notebooks & tutorials

Important Note

This is a hypothetical dataset generated entirely by ChatGPT based on realistic marketing patterns and industry KPIs. It should not be interpreted as real performance data of any educational institution or marketing department.

Description:

The Social Media Advertising dataset is a comprehensive collection of data related to various social media advertising campaigns. It includes information such as ad impressions, clicks, spend, demographic targeting, and conversion rates. The dataset encompasses multiple social media platforms such as Facebook, Instagram, Pinterest, and Twitter, providing a diverse range of advertising campaign data.

Potential Uses for Data Analysis:

Campaign Performance Analysis: Analyze the performance of advertising campaigns across different social media platforms to identify the most effective channels and strategies. Audience Segmentation: Utilize demographic targeting data to segment the audience and tailor advertising campaigns to specific demographic groups. ROI Calculation: Calculate the return on investment (ROI) for advertising campaigns by comparing ad spend to conversion rates and revenue generated. Optimization Strategies: Identify optimization opportunities by analyzing click-through rates, engagement metrics, and conversion funnels to improve campaign effectiveness. Predictive Modeling: Build predictive models to forecast future campaign performance and optimize advertising strategies for maximum impact.

NOTE: This is a fictional database.

Context

Cluster Analysis for Ad Conversions Data

Content

The data used in this project is from an anonymous organisation’s social media ad campaign. The data file can be downloaded from here. The file conversion_data.csv contains 1143 observations in 11 variables. Below are the descriptions of the variables.

1.) ad_id: an unique ID for each ad.

2.) xyz_campaign_id: an ID associated with each ad campaign of XYZ company.

3.) fb_campaign_id: an ID associated with how Facebook tracks each campaign.

4.) age: age of the person to whom the ad is shown.

5.) gender: gender of the person to whim the add is shown

6.) interest: a code specifying the category to which the person’s interest belongs (interests are as mentioned in the person’s Facebook public profile).

7.) Impressions: the number of times the ad was shown.

8.) Clicks: number of clicks on for that ad.

9.) Spent: Amount paid by company xyz to Facebook, to show that ad.

10.) Total conversion: Total number of people who enquired about the product after seeing the ad.

11.) Approved conversion: Total number of people who bought the product after seeing the ad.

Acknowledgements

Thanks to the Anonymous data depositor

Inspiration

Social Media Ad Campaign marketing is a leading source of Sales Conversion and i have made this data available for the benefit of Businesses using Google Adwords to track Conversions