Building an A/B testing analysis framework for mobile gaming on Databricks

2026-03-25 · Source: Databricks · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Advanced, medium

Summary

HARDlight has implemented a Databricks-native experimentation framework that automates data processing, statistical inference, and insight delivery for A/B testing. This system ingests experiment definitions, player telemetry, and outcome metrics into governed tables, then uses notebooks to compute statistical models. Analytical outputs are materialized into a unified experiment analytics model, creating a stable semantic layer. Databricks AI/BI provides an accessible interface, generating daily LLM summaries for non-technical stakeholders and offering progressive disclosure through multiple layers of metrics, diagnostics, and segment analysis. This architecture has reduced manual analysis effort by over eight hours per week and saved approximately one day per experiment, enabling a two-times increase in monthly A/B testing capacity without additional headcount.

Key takeaway

For Product Managers and Data Scientists running A/B tests, adopting a structured, automated experimentation framework like Databricks AI/BI can significantly reduce manual effort and increase testing capacity. You should prioritize creating a stable semantic layer for analytical outputs and leverage AI/BI dashboards to serve diverse audiences with layered insights, from LLM summaries to deep diagnostics, ensuring consistent interpretation and historical context.

Key insights

A Databricks-native framework automates A/B testing, standardizing analysis and delivering AI/BI-powered insights.

Principles

• Separate data processing, inference, and consumption.
• Materialize analytical outputs into a unified model.
• Provide progressive disclosure for diverse audiences.

Method

Ingest data into governed tables, compute statistical models via notebooks, materialize outputs, and deliver insights through an AI/BI dashboard with LLM summaries and layered detail.

In practice

• Use LLM summaries for non-technical stakeholders.
• Archive dashboards for auditable experiment records.
• Break down results by revenue stream and player segment.

Topics

• Experimentation Framework
• Databricks AI/BI
• LLM Summarization
• A/B Testing
• Data Governance

Best for: Data Scientist, Product Manager, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Databricks.