6. Mobile Pixel Tracking & Churn/Uninstall Analytics
Overview
Architected a purpose-built mobile telemetry and engagement data platform to capture high-resolution user interaction, silent push receipts, and uninstallation signals across native Android applications. Engineered a lightweight client SDK with memory-efficient event queueing, persistent SQLite staging, and resilient batch transmission to prevent device battery depletion and application runtime latency. Built cloud ingestion infrastructure on Amazon Web Services utilizing Amazon CloudFront, API Gateway, and Amazon S3 to reliably collect millions of daily device heartbeats and lifecycle markers without server bottlenecks. Developed serverless ETL processing workflows in Python and Amazon Athena to analyze telemetry streams, identify silent push notification drop-offs, and compute predictive user churn scores. Integrated automated cohort generation pipelines with customer engagement tools, empowering product managers and growth analysts to proactively trigger targeted retention campaigns weeks before anticipated user churn occurred.
Challenge
Mobile product teams lacked reliable, low-overhead mechanisms to detect early user churn patterns, app uninstallations, and engagement drop-offs across heterogeneous Android device fleets without draining device batteries or impacting app startup times.
Approach
- Engineered a lightweight Android client SDK featuring SQLite-backed batch queueing, adaptive backoff timers, and silent FCM push receipt listeners.
- Constructed a resilient cloud ingestion endpoint using AWS CloudFront, API Gateway, and Amazon S3 capable of processing bursty mobile heartbeat traffic.
- Developed serverless data transformation and query pipelines using AWS Lambda, Python, and Amazon Athena to process daily telemetry partitions.
- Built predictive heuristic churn scoring models that flagged disengaged user segments based on silent push delivery failures and declining session frequencies.
Outcome
Empowered consumer mobile application teams to detect app uninstallations within hours and proactively engage at-risk cohorts, lifting 30-day user retention rates across participating mobile apps.