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DataBackend
Web Metrics Analytics System
Automated weekly analytics reports for 50+ publishers, cutting manual reporting effort by 80% and surfacing hidden traffic anomalies.
PythonSQLData WarehousingAutomationPandas
The Problem
50+ publishers had no unified visibility into their traffic, engagement, and monetization metrics. Reports were manually compiled in spreadsheets, taking 2 days per cycle.
The Solution
Built a centralized analytics system with automated data ingestion pipelines, standardized publisher metrics (sessions, bounce rate, CTR, RPM), and scheduled report generation. Integrated anomaly detection to flag traffic spikes and drops.
The Impact
Delivered weekly automated reports to 50+ publishers. Reduced manual reporting effort by 80%. Identified 3 major traffic anomalies that would have gone undetected for weeks.
Tech Details
- Centralized data ingestion pipelines pulling from GA4, ad networks, and CMS APIs
- Standardized metric schema: sessions, pageviews, bounce rate, CTR, RPM, revenue
- Pandas-based transformation layer normalizing data across publisher properties
- Anomaly detection using Z-score and IQR methods to flag traffic spikes/drops
- Scheduled report generation (cron) producing PDF + CSV outputs per publisher
- SQL-based data warehouse with publisher-partitioned tables for fast per-client queries
- Email delivery system dispatching personalized reports to 50+ publisher contacts