Beating Google Analytics: 3x Cheaper, 97% Data Quality

Delivery Hero faced a forced migration from Universal Analytics. Instead of moving to GA4, they built an internal user tracking system to solve real-time data needs, event limits, and GDPR constraints. The initial MVP was a simple API with Pub/Sub and BigQuery, later extended with data validation, archival, and code-generated schemas.

Data quality rose from Google's 85% order match rate to 97% by shifting to compile-time event validation rather than asking developers to fix data manually. Cost per message dropped 25% after one year and eventually reached three times cheaper through archival, storage optimization, and on-demand nodes.

They avoided data loss during migration with a doubled pipeline—sending data via both old and new systems—though this multiplied costs for months until users updated apps. Pod crashes that caused data loss were solved by making requests synchronous, accepting a 7x latency increase.

The big lesson: a focused team with clear KPIs can outperform a generic SaaS product. Full control over priorities, cost, and privacy justified the effort, but it required rigorous load testing, progressive rollouts, and observability that was initially lacking.