High-impact SQL projects transforming raw marketing and product event data into actionable business strategies. This folder highlights my ability to handle complex data architectures and extract core performance KPIs.
To ensure data consistency across platforms, the following business logic was implemented in the analytical scripts:
| Metric | Definition | Business Value |
|---|---|---|
| ROMI | (Value - Spend) / Spend * 100 |
Measures the direct profitability of ad spend. |
| Reach Growth | (Current Reach - Previous Reach) / Previous Reach |
Tracks the velocity of audience expansion MoM. |
| CVR (Funnel) | Purchases / Total Sessions |
Identifies the efficiency of the e-commerce user journey. |
| Pearson Corr | CORR(Engagement, Purchase) |
Statistically validates if user activity leads to revenue. |
Tools: PostgreSQL | DBeaver
File: marketing_performance_analysis.sql
Highlights:
- Gaps & Islands Analysis: Engineered a "Streak" calculation to identify the longest continuous run of active adsets, ensuring high delivery reliability.
- Spend Volatility: Aggregated multi-source data to identify daily minimum/maximum spend outliers across Google and Facebook.
Tools: BigQuery | GA4 Event-Model
File: ga4_ecommerce_user_behavior.sql
Highlights:
- Funnel Diagnostics: Reconstructed a 7-step conversion pipeline to pinpoint exactly where users drop off before checking out.
- Landing Page Efficiency: Used Regex to parse URL paths and identify the highest converting entry points for new users.
- Advanced Queries: CTEs, subqueries, and complex multi-table joins.
- Window Functions: Expert use of
LAG(),ROW_NUMBER(), andPARTITION BY. - Schema Handling: Proficiency in BigQuery
UNNESTfor handling nested JSON event parameters.