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.

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.

This dataset contains campaign-level advertising performance data across three major digital advertising platforms: Google Ads, Meta Ads (Facebook/Instagram), and TikTok Ads.

It is designed specifically for:

• Marketing analytics
• ROAS & CPA optimization
• Budget allocation modeling
• Platform performance comparison
• Time-series analysis

The dataset simulates real-world advertising behavior by maintaining realistic relationships between impressions, clicks, spend, conversions, and revenue.

Kerala Political Ads Spending Dataset

This dataset contains political advertisement spending data related to Kerala-based political entities, collected from the Meta Ad Library. It provides insights into how different political parties allocate their advertising budgets across regions in India during a defined campaign period.

The dataset captures spending patterns, geographic distribution, and source transparency indicators, making it useful for analyzing political marketing strategies, regional outreach, and digital campaign intensity.

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This data can be used for: - Political campaign analysis - Regional ad spending comparisons - Digital marketing trends in politics - Data visualization and exploratory data analysis (EDA) - Machine learning tasks such as clustering or classification

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⚠️ Disclaimer

This dataset was collected on April 8, 2026, and reflects data available at that point in time.

The ad spending values represent activity within the date range January 6, 2026 – April 5, 2026 , as reported in the Meta Ad Library.

All data has been sourced from the Meta Ads Library Report: https://www.facebook.com/ads/library/report/?source=nav-header

Please note:

• The dataset may include estimated values provided by the source.
• Data is subject to updates or changes on the original platform after the collection date.
• This dataset is intended for informational and analytical purposes only and does not claim absolute accuracy or completeness.

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.

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.

Data Definition

campaign_item_id : unique id of each adevertising campaign no_of_days : number of days campaign has been running time : timestamp on which the data was captured ext_service_id : id of each advertising platforms used ext_service_name : name of each advertising platforms used creative_id : id of the creative images used for ads creative_height : height of the creative image for the ad in pixels creative_width : width of the creative image for the ad in pixels search_tags : search tags used for displaying ads template_id : template used in the creative image landing_page : landing page url on which users clicked or browsed through advertiser_id : id of the advertiser advertiser_name : name of the place of the advertiser ( city , country , state ) network_id : id of the each agency advertiser_currency : currency of the country in which the advertiser operates in channel_id : id of each channel used for placed ads channel_name : name of the channel ( display , search , social , mobile video ) max_bid_cpm : maximum value of bid for optimizing cpm campaign_budget_usd : overall budget of the campaign or the amount of money that the campaign can spend impressions : the number of times an advertisement is displayed on a website or social media platform. clicks : the number of times an advertisement is clicked on by a user, leading them to the advertiser's website or landing page. currency_code : the currency code of the advertiser exchange_rate : a relative price of one currency expressed in terms of another currency. media_cost_usd : the amount of money that the campaign has spent on that particuar day position_in_content : position where the ad was placed on the website page unique_reach : the number of unique users who see your post or page. total_reach : the number of people who saw any content from your page or about your page. search_tags : a word or set of words a person enters when searching on Google or one of our Search Network sites. cmi_currency_code : campaign currency code time_zone : timezone in which the campaign is running weekday_cat : weekday / weekend catgeory keywords : a word or set of words that Google Ads advertisers can add to a given ad group so that your ads are targeting the right audience.

The concern with this Dataset is that views and likes are our attributs of interest so they shouldn't be loaded as row values. Instead, they must be displayed as column names. As a result, we have addressed this issue in the related notebook.

Data Dictionary

Dataset Overview

• Rows: 10,000 campaigns
• Columns: 41 variables (35 base + 6 derived metrics)
• Date Range: January 1, 2024 - January 31, 2026
• Purpose: Multiple linear regression analysis of advertising campaign profitability

Variables

Campaign Identification (2)

Variable	Type	Description	Values
campaign_id	String	Unique campaign identifier	CAMP_00001 - CAMP_10000
campaign_objective	Categorical	Primary campaign goal	Brand Awareness, Lead Generation, Conversions, App Installs, Engagement

Platform & Placement (4)

Variable	Type	Description	Values
platform	Categorical	Advertising platform	Google Ads, Facebook, LinkedIn, TikTok, Twitter, Instagram
ad_placement	Categorical	Ad display location	Feed, Stories, Search, Display Network, In-Stream Video, Sidebar
device_type	Categorical	User device	Desktop, Mobile, Tablet
operating_system	Categorical	Device OS	iOS, Android, Windows, macOS, Other

Creative Attributes (6)

Variable	Type	Description	Values
creative_format	Categorical	Ad format type	Video, Image, Carousel, Text, Interactive, Story
creative_size	Categorical	Ad dimensions (pixels)	1080x1080, 1920x1080, 300x250, 728x90, 320x50, 1200x628
ad_copy_length	Categorical	Text length category	Short, Medium, Long
has_call_to_action	Boolean	CTA button present	True, False
creative_emotion	Categorical	Emotional tone	Fear, Joy, Urgency, Trust, Curiosity, Neutral
creative_age_days	Integer	Days since creative launch	1-90

Audience Targeting (6)

Variable	Type	Description	Values
target_audience_age	Categorical	Age bracket	18-24, 25-34, 35-44, 45-54, 55-64, 65+
target_audience_gender	Categorical	Gender targeting	Male, Female, All
audience_interest_category	Categorical	Interest segment	Tech Enthusiasts, Business Professionals, Gamers, Students, Shoppers, Health & Fitness
income_bracket	Categorical	Household income	<$50K, $50K-$100K, $100K-$200K, >$200K
purchase_intent_score	Categorical	Behavioral intent signal	Low, Medium, High
retargeting_flag	Boolean	Retargeting campaign	True, False

Temporal (5)

Variable	Type	Description	Range
start_date	Date	Campaign start date	2024-01-01 to 2026-01-31
quarter	Integer	Calendar quarter	1-4
day_of_week	Categorical	Day campaign ran	Monday-Sunday
hour_of_day	Integer	Hour ad shown (24-hour)	0-23
campaign_day	Integer	Day in campaign lifecycle	1-90

Auction & Quality (2)

Variable	Type	Description	Range
quality_score	Integer	Platform quality rating	1-10
actual_cpc	Float	Actual cost per click paid ($)	$0.25 - $17.00

Performance Metrics (5)

Variable	Type	Description	Range	Constraints
impressions	Integer	Times ad displayed	5,000 - 500,000	-
clicks	Integer	Number of ad clicks	10+	≤ impressions
conversions	Integer	Completed actions	0+	≤ clicks
ad_spend	Float	Campaign spend ($)	Variable	clicks × actual_cpc
revenue	Float	Revenue generated ($)	$0+	-

Engagement Quality (3)

Variable	Type	Description	Range
bounce_rate	Float	% immediate exits	10.0 - 90.0
avg_session_duration_seconds	Integer	Average time on site (seconds)	10 - 600
pages_per_session	Float	Average pages viewed	1.0 - 10.0

Industry Context (2)

Variable	Type	Description	Values
industry_vertical	Categorical	Business sector	SaaS, E-commerce, Healthcare, Finance, Education, Gaming
budget_tier	Categorical	Budget classification	Low, Medium, High

Derived Metrics (6)

Variable	Formula	Description
CTR	(clicks / impressions) × 100	Click-through rate (%)
CPC	ad_spend / clicks	Cost per click ($)
conversion_rate	(conversions / clicks) × 100	Conversion rate (%)
CPA	ad_spend / conversions	Cost per acquisition ($)
ROAS	revenue / ad_spend	Return on ad spend (multiplier)
profit	revenue - ad_spend	PRIMARY DEPENDENT VARIABLE ($)

Performance Benchmarks (2025 Industry Data)

| Metric | This Dataset | Industry Benchmark | Source | |--------|-...

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.

This dataset offers a comprehensive, synthetic view into the performance of a social media advertising campaign. Crafted to mirror real-world scenarios on platforms like Facebook and Instagram, it provides a rich, multi-table foundation for practicing a wide range of data analysis techniques.

The core of this dataset is a relational database modeled around four key entities:

Users: Detailed individual profiles, including age, gender, country, location, and interests. This allows for granular demographic analysis and segmentation.

Campaigns: High-level campaign data, including budget, duration, and a clear start/end date, providing the strategic context for all ad-related activities.

Ads: Specific creative assets tied to campaigns, complete with their own targeting parameters for age, gender, and interests. This is essential for evaluating targeting effectiveness.

Ad Events: The central transactional log, capturing every user interaction from a simple Impression to a final Purchase. This table is the key to understanding the full conversion funnel. ** Context, Sources, and Inspiration 💡 This dataset was inspired by the typical data models used by digital marketers and data analysts working with platforms like Meta Ads Manager. The structure is designed to simulate a realistic ad campaign, where a business runs multiple ads under various campaigns and tracks a user's journey from their first exposure to a potential final conversion.

The data itself is entirely synthetic, generated using Python libraries such as Faker and NumPy. This approach allows for a clean, privacy-conscious dataset that is free from any PII (Personally Identifiable Information). Key features of the data generation process include:

Realistic Distributions: We used a log-normal distribution for user ages and weighted probabilities for attributes like country, time of day, and ad engagement events to reflect genuine user behavior.

Interconnected Relationships: All tables are meticulously linked via primary and foreign keys, ensuring data integrity and allowing for complex join operations.

How to Use This Dataset This dataset is perfect for anyone looking to build a data portfolio or practice their skills. Here are some of the analyses you can perform:

Data Exploration & SQL Practice: Use SQL to join the tables and explore relationships between campaigns, ads, and user engagement.

Market & Strategic Analysis: Evaluate which campaigns, ad types, or targeting strategies yielded the best results.

Cohort & Behavioral Analysis: Track how different user segments (cohorts) behave over time.

Predictive Modeling: Build models to predict user behavior or ad performance based on campaign parameters.

Whether you're a data science student, an aspiring analyst, or a seasoned professional, this dataset provides a robust environment to build and showcase your skills. I hope you find it a valuable and engaging resource!

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.

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

🎯 Context

This dataset contains comprehensive metrics and performance data for various digital marketing campaigns. It is designed to help data analysts, marketers, and data scientists understand the relationship between marketing budgets, customer engagement (clicks, conversions), and financial outcomes (Revenue, ROI) across different marketing channels.

📊 Content

The dataset includes detailed information on campaign performance, promotional events (Flash Sales), and customer satisfaction. Key columns include:

• Campaign_ID & Product_ID: Unique identifiers for the marketing campaigns and products.
• Budget: The total amount spent on the marketing campaign.
• Clicks & Conversions: Engagement metrics showing how many users clicked the ads and successfully converted.
• Revenue_Generated & ROI: Financial metrics indicating the total revenue brought in and the Return on Investment.
• Subscription_Tier & Length: Details regarding the customer's subscription plan (e.g., Standard, Premium).
• Flash_Sale_ID, Discount_Level & Units_Sold: Information on promotional events and their impact on sales.
• Bundle_ID & Bundle_Price: Details about product bundles offered during the campaigns.
• Customer_Satisfaction_Post_Refund: Customer feedback score after the transaction/refund process.
• Marketing Channel (Kênh Marketing): The platform where the ad was run (e.g., Facebook, etc.).

💡 Potential Use Cases

This dataset is perfect for building data analysis portfolios. You can use it to: 1. Exploratory Data Analysis (EDA): Discover which marketing channels drive the highest ROI and most conversions. 2. Budget Optimization: Analyze the correlation between campaign budget and revenue generated to optimize future ad spend. 3. Predictive Modeling: Build machine learning models to predict campaign ROI or customer conversion rates based on discount levels and subscription tiers. 4. Dashboard Creation: Create interactive BI dashboards (using Power BI, Tableau, or Python libraries) to visualize marketing performance.

Dataset Description:

DateTime: Timestamp indicating when the customer interaction with the advertisement occurred. Precision to the hour allows for analysis of peak engagement times.

CustomerID: A unique identifier assigned to each customer who interacted with the ads, ensuring user privacy while allowing detailed behavioral analysis.

AgeGroup: The age group of the customer (e.g., 18-24, 25-34, etc.), providing vital insights into the demographic appeal of different products and campaigns.

Gender: The customer's self-identified gender. This demographic information is crucial for tailoring and optimizing marketing messages.

Country: The country from which the ad interaction was made, highlighting geographical strengths and areas for international market expansion.

ProductCategory: Categories of advertised FitBit Inc. products, such as Wearables, Accessories, and Health Apps, indicating product popularity and market trends.

CampaignID: Identifies the specific marketing campaign, allowing for performance tracking and ROI analysis on a campaign-by-campaign basis.

AdPlatform: The digital platform (Google, Facebook, Instagram, Twitter) where the advertisement was displayed, showcasing the effectiveness of each platform in engaging potential customers.

Impressions: The number of times an ad was displayed, offering insights into the reach of each campaign.

Clicks: The number of times an ad was clicked, a direct measure of customer engagement and interest. Conversions: Indicates whether an ad click resulted in a conversion (e.g., product purchase, subscription sign-up), key for assessing the immediate impact of marketing efforts.

ConversionValue: The monetary value generated from each conversion, crucial for evaluating the financial success of campaigns and calculating marketing ROI.