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.

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.

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.

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.

This dataset contains simulated digital marketing campaign performance data for three major Indian beauty ecommerce brands: - Nykaa - Purplle - Tira Beauty The dataset enables comparative marketing analytics across multiple platforms and campaign strategies.

It is designed for: - Marketing Performance Analysis - ROI Optimization - Cross-Brand Comparison - Budget Efficiency Analysis - Dashboard Creation - Predictive Modeling

Power BI Dashboard Project

This dataset was used to build an interactive Power BI dashboard that includes: - Brand-wise ROI comparison - Platform performance analysis - Campaign profitability tracking - KPI cards (CTR, CPC, CPA, ROI) - Revenue distribution visualization - Budget vs Spend analysis The dashboard enables business users to monitor marketing performance and optimize campaign allocation decisions.

Dataset Structure

The dataset includes campaign-level metrics such as: - Brand - Campaign_ID - Campaign_Type - Platform (Instagram, Facebook, Google Ads, Influencer, etc.) - Target_Audience - Budget_Allocated - Amount_Spent - Impressions - Clicks - Conversions - Revenue_Generated - CTR (Click Through Rate) - CPC (Cost Per Click) - CPA (Cost Per Acquisition) - ROI - Campaign_Duration_Days

Column Definitions

• CTR = Clicks / Impressions
• CPC = Amount_Spent / Clicks
• CPA = Amount_Spent / Conversions
• ROI = (Revenue_Generated - Amount_Spent) / Amount_Spent

Possible Use Cases

• Compare marketing efficiency across brands
• Identify most profitable platform
• Analyze high ROI campaigns
• Perform customer targeting analysis
• Build regression model to predict revenue
• Optimize budget allocation
• Practice SQL queries and joins
• Build Power BI / Tableau dashboards

Who Can Use This Dataset?

• Data Analysts
• Marketing Analysts
• MBA Students
• Business Analytics Projects
• Portfolio Projects
• SQL Practice

Disclaimer

This dataset is synthetically generated for educational and portfolio purposes. It does not represent actual financial data of the brands.

1.COMPANY PROFILE: ELSA CORP

Company Name: ELSA (English Language Speech Assistant) Industry: Education Technology (EdTech) / Artificial Intelligence

Overview: ELSA is a world-leading EdTech company headquartered in Silicon Valley. Its flagship product, ELSA Speak, is an AI-powered mobile application designed to help English learners improve their pronunciation and speaking skills. Using proprietary speech recognition technology, ELSA provides real-time feedback to millions of users worldwide.

Core Product: Subscription-based language learning plans (Basic, Standard, and Premium tiers).

Business Model: A Software-as-a-Service (SaaS) model driven by digital marketing performance and user retention.

Key Strategies: Leveraging multi-channel advertising (Facebook, Google, TikTok, Affiliate) and data-driven promotional campaigns (Flash Sales, Product Bundles) to optimize Customer Acquisition Cost (CAC) and Lifetime Value (LTV).

2. DATASET DESCRIPTION

Title: ELSA Marketing ROI & Product Performance Integrated Dataset Volume: 10,000 records (Raw data).

Objective: This dataset provides a comprehensive look at the intersection of marketing expenditure and sales performance. It is specifically curated to challenge students in the fields of Accounting, Auditing, and Business Information Systems to apply Decision Support System (DSS) tools.

Data Cleaning Focus: Includes missing values (nulls) in critical fields like Campaign_ID and Product_ID, requiring students to perform data scrubbing and normalization.

Predictive Modeling: Facilitates the use of Regression Analysis to determine how Budget and Discounts impact Revenue.

Optimization & Simulation: Designed for practicing Excel Solver, Goal Seek, and Scenario Manager to find the optimal balance between marketing spend and profit margins.

Key Data Fields:

Marketing Metrics: Budget, Clicks, Conversions, Marketing Channels.

Financial Performance: Revenue Generated, ROI (Return on Investment), Units Sold.

Product & Promotion: Subscription Tier, Subscription Length, Flash Sale ID, Discount Level, Bundle Price.

User Experience: Customer Satisfaction Score (Post-Refund).

3. DATA FIELD DESCRIPTIONS (DATA DICTIONARY)

1. Identification Group

Campaign_ID: A unique identifier for each marketing campaign. (Note: This column contains missing values to simulate real-world data entry errors).

Product_ID: A unique identifier for the specific ELSA subscription product or course associated with the campaign.

Flash_Sale_ID: A unique code for time-limited promotional events. If present, the transaction occurred during a high-discount period.

Bundle_ID: An identifier for bundled product offers (e.g., ELSA Premium + Lifetime dictionary), used to track cross-selling performance.

2. Marketing Performance Group

Budget: The total amount of money spent on a specific campaign (expressed in currency).

Marketing Channel: The platform used for the advertisement, including Facebook, Google, TikTok, and Affiliate networks.

Clicks: The total number of times users clicked on the campaign advertisement, indicating the "attractiveness" of the creative content.

Conversions: The number of users who completed a desired action (e.g., signing up or purchasing a subscription) after clicking the ad.

3. Financial & Business Metrics Group

Revenue_Generated: The total gross income resulting from the campaign. This is the Primary Target Variable for ROI analysis.

ROI (Return on Investment): A profitability ratio calculated as Revenue / Budget. It measures how many units of currency are earned for every unit spent.

Units_Sold: The total quantity of subscription plans sold during the campaign period.

Subscription_Tier: The level of the service plan, categorized into Basic, Standard, or Premium.

Subscription_Length: The duration of the plan (e.g., 1 month, 6 months, or 12 months/Annual).

4. Pricing & Promotional Group

Discount_Level: The percentage of discount applied to the original price. This is a key variable for Regression Analysis to find the price elasticity of demand.

Bundle_Price: The final price of the bundled products offered to the customer.

5. Quality & Experience Group

Customer_Satisfaction_Post_Refund: A numerical rating (usually 1-5 stars) representing customer feedback after considering any refund requests. This serves as a proxy for product-market fit and long-term brand health.

4. EDUCATIONAL SIGNIFICANCE

This dataset serves as a real-world Business Intelligence case study. Students are expected to transition from "Data Cleaners" to "Strategic Advisors." By analyzing this data, learners will gain practical experience in:

Standardizing messy administrative data for system integration.

Quantifying the efficiency of various marketing platforms.

Simulating business outcomes under different economic conditions (Best-case vs. Wors...