Repository Description:
This project focuses on analyzing customer lifetime value using various data visualization techniques. By leveraging Python libraries such as Pandas, Seaborn, Plotly, and Matplotlib, the analysis provides insights into customer acquisition costs, revenue generation, conversion rates, and return on investment across different marketing channels.
Key Features:
- Data Overview: Detailed examination of customer acquisition data, including summary statistics and initial data cleaning.
- Cost Analysis: Visualization of acquisition costs with histograms, highlighting cost distributions across channels.
- Revenue Analysis: Distribution and comparison of revenue generated by customers, identifying key revenue ranges.
- Channel Performance: Evaluation of marketing channels based on average costs, conversion rates, and total revenue, utilizing bar charts and pie charts for clear comparisons.
- ROI Calculation: Calculation and visualization of return on investment (ROI) for each channel, identifying the most cost-effective marketing strategies.
This project serves as a comprehensive guide for understanding customer lifetime value through data analysis and visualization, making it a valuable resource for marketers and data analysts.