This project simulates a professional engagement within the Analytics Department of Showz, an event ticketing platform. The core challenge was to evaluate historical data to optimize marketing budgets and improve customer acquisition strategies.
The analysis addresses critical business questions through a data-driven lens:
User Behavior: How do users interact with the platform across different devices?
Conversion Funnels: Identifying the exact touchpoints and time-to-conversion for first-time buyers.
Customer Lifetime Value (LTV): Quantifying the long-term financial contribution of each customer.
Profitability & Payback Period: Determining the exact point when revenue offsets Customer Acquisition Costs (CAC).
Analysis of multi-source data spanning from January 2017 to December 2018:
Web Server Logs (Visits): Session-level data including Uid, device type, timestamps, and ad source IDs.
Transactional Data (Orders): Comprehensive record of purchases and revenue per user.
Marketing Spend (Expenses): Granular investment data categorized by acquisition source and date.
Data Engineering & Preparation: Robust ETL process focusing on data type optimization and quality assurance.
Product Metrics: Calculated DAU, WAU, and MAU, session frequency, and ASL (Average Session Length) to determine retention rates.
Cohort Analysis: Segmented users by acquisition date to track conversion velocity, order volume, and average ticket size over time.
Marketing Performance (Unit Economics): * Calculated CAC (Customer Acquisition Cost) per source.
Modeled LTV (Lifetime Value) to understand long-term health.
Evaluated ROMI (Return on Marketing Investment) to identify the "break-even" point of various ad channels.
Data Visualization: Developed comparative dashboards to visualize performance trends across devices and advertising sources.
The project concludes with a strategic roadmap for the marketing team. By identifying high-yield sources and inefficient channels, the analysis provides a data-backed recommendation on where to scale investment to maximize ROI and shorten the CAC payback period.