5. Google Analytics Anomaly Detection & Alerting System
Overview
Architected an automated statistical anomaly detection and incident alerting engine monitoring key business metrics across high-volume digital properties. Developed automated extraction pipelines leveraging the Google Analytics Reporting REST API to ingest daily session, conversion, and bounce rate time series into a high-performance MongoDB and MySQL data store. Implemented seasonal decomposition and statistical outlier detection algorithms in Python using pandas and NumPy, benchmarking incoming metrics against rolling baseline ranges. Integrated automated notification dispatchers interfacing with Mandrill and transactional email APIs, alerting marketing directors and site reliability engineers within minutes of traffic drops or checkout anomalies.
Challenge
Marketing and product teams lacked automated visibility into sudden drops or spikes in traffic, conversion rates, and revenue across complex web properties, often discovering tracking bugs or business anomalies days after they occurred.
Approach
- Built automated data collection jobs querying the Google Analytics Core Reporting API and storing historical metrics in MongoDB and MySQL.
- Applied time-series statistical models and rolling z-score analysis in Python to establish dynamic confidence intervals accounting for day-of-week seasonality.
- Designed a multi-channel alert dispatcher sending structured anomaly reports and diagnostic deep-links via email (Mandrill API) and internal messaging hooks.
- Created a self-service management UI where stakeholders could configure custom sensitivity thresholds, metric pairings, and alert recipients.
Outcome
Replaced manual dashboard audits with proactive automated monitoring, reducing anomaly detection latency from days to hours and helping clients recover tens of thousands of dollars in lost conversion revenue.