This project focuses on performing Exploratory Data Analysis (EDA) on a marketing campaign dataset to understand campaign effectiveness, customer engagement, and return on investment (ROI).
The objective is to identify high-performing marketing campaigns, analyze the conversion funnel, and provide data-driven recommendations for budget allocation.
- Clean and preprocess marketing campaign data
- Analyze campaign performance metrics
- Evaluate marketing ROI
- Visualize the conversion funnel
- Identify top-performing campaigns and categories
- Provide business recommendations for budget optimization
The dataset contains the following attributes:
- Campaign Name
- Campaign ID
- Category
- Date
- Impressions
- Clicks
- Leads
- Orders
- Marketing Spend
- Revenue
ROI = ((Revenue - Marketing Spend) / Marketing Spend) × 100
CTR = (Clicks / Impressions) × 100
Lead Rate = (Leads / Clicks) × 100
Order Rate = (Orders / Leads) × 100
The analysis includes:
- Data Cleaning
- Missing Value Handling
- Campaign-wise Performance Analysis
- Category-wise Analysis
- Revenue Analysis
- Marketing Spend Analysis
- ROI Analysis
- Funnel Analysis
- Business Insights Generation
- Revenue by Campaign
- ROI by Campaign
- Revenue by Category
- Marketing Funnel Analysis
- Conversion Performance Analysis
- Identified campaigns generating the highest ROI
- Analyzed conversion drop-offs across the marketing funnel
- Evaluated spending efficiency
- Recommended budget allocation strategies
- Python
- Pandas
- NumPy
- Matplotlib
- Jupyter Notebook
This analysis helps businesses optimize marketing budgets by identifying campaigns that deliver the highest returns and conversion efficiency.