Platform Evaluation & Architecture Design: We began with a thorough assessment of the client's experimentation needs and infrastructure requirements. After evaluating multiple options, GrowthBook emerged as the leading candidate due to its flexibility and open-source foundation. The critical decision between cloud and self-hosted deployment hinged on several factors. While the cloud option offered simplicity, the self-hosted version provided critical advantages: enhanced data security for sensitive customer information, complete control over infrastructure scaling, deep integration capabilities with existing systems, and significantly lower long-term operational costs. We designed a comprehensive architecture that positioned GrowthBook within their existing Google Cloud infrastructure, planning data flows to minimize redundancy and optimize performance.

Infrastructure Implementation & Security Configuration: Following architecture approval, we deployed GrowthBook in a containerized environment within the client's private cloud infrastructure. Security was paramount, so we implemented end-to-end encryption, strict access controls, and comprehensive audit logging. The deployment included high-availability configuration to ensure uninterrupted experimentation capabilities. We established secure API connections to their existing data sources, implementing proper authentication mechanisms and data access protocols. This phase required close collaboration with their security team to ensure compliance with internal policies and external regulations regarding customer data protection.

Data Integration & Event Tracking Setup: The core of our implementation involved integrating GrowthBook with the client's Google-based analytics stack. We configured Google Tag Manager to properly dispatch experiment triggers and variation assignments. A critical component was the implementation of the experiment_view event within their event tracking schema, which captured both experiment identifiers and variation assignments. This event streamed simultaneously to Google Analytics 4 and Firebase, creating a unified experimentation data layer across all platforms. We developed custom dimension mappings to ensure experiment data maintained consistency throughout the analytics ecosystem, allowing for seamless cross-platform analysis and visualization.

Metrics Framework Development & Optimization: Perhaps the most technically challenging aspect of the implementation was developing an efficient metrics calculation framework. Initial tests revealed that unoptimized BigQuery queries for experiment analysis were both slow and expensive, with some complex metrics taking over 30 minutes to calculate and costing hundreds of dollars per analysis. We addressed this by designing specialized data marts and materialized views in BigQuery that pre-aggregated key metrics at appropriate intervals. We implemented advanced query optimization techniques, including proper partitioning, clustering, and aggregation strategies. Our optimization reduced query complexity by 85% while maintaining statistical accuracy, dramatically reducing both computation time and associated costs.

Statistical Model Implementation & Training: The final phase focused on implementing the client's preferred Bayesian statistical approach. Unlike traditional frequentist methods that provide binary significance decisions, Bayesian analysis offered several advantages for their business context: more intuitive result interpretation through probability distributions, better handling of small sample sizes during early experiment stages, continuous monitoring capabilities without statistical penalty, and more nuanced decision-making information including expected loss calculations. We configured GrowthBook's statistical engines to implement these models and conducted extensive training sessions with product and data teams to ensure proper interpretation of results. Custom documentation was developed to guide experiment design and analysis within this statistical framework.