Telemetry Solution For Advanced In-App Feature Usage Analytics
Problem Statement:
Stilbon’s client, required an efficient and scalable data pipeline to onboard new features quickly. Additionally, they needed a solution that would enable them to analyze feature usage insights to make data-driven product investment decisions and improve customer experience.
Solution:
Stilbon designed and developed an end-to-end data pipeline solution using Google Cloud’s powerful tools. We leveraged BigQuery for data warehousing and Looker for analytical modeling, allowing the business analytics team and feature owners to perform self-service analytics. We also used GKE (Google Kubernetes Engine) for container orchestration to ensure efficient and scalable data processing.
Our solution enabled extensibility, and other Microsoft Box Products telemetry (e.g., ASDE) could easily onboard the same solution. Stilbon also created custom dashboards and reports using Looker to provide an intuitive, real-time view of the data, enabling senior leadership to make informed product investment decisions.
Outcome:
By implementing our solution, Stilbon delivered significant benefits to the Telemetry team. The self-service analytical model reduced the effort of the PM and engineering teams by 45%, enabling them to focus on other critical tasks. The custom dashboards and reports provided senior leadership with real-time data-driven insights, enabling them to prioritize product features that improved customer experience.
Overall, our solution helped the Telemetry team improve their data pipeline architecture, streamline their feature onboarding process, and make informed product investment decisions.