Marketing Personalization · Real-time Analytics
Flo Health needed a better way to support women's health predictions, experimentation, and self-service analytics because a decentralized analytics estate could not keep up with the scale, experimentation cadence, and product intelligence required by tens of millions of users.
Reported impact
Flo Health improved women's health predictions, experimentation, and self-service analytics with measurable results including 45% building a better future for female health, 400 increase in internal monthly active users of the platform, and 46% analytics experiments run per quarter, while giving teams a faster path from governed data to production decisions.
Flo Health uses Databricks Data + AI Platform to support women's health predictions, experimentation, and self-service analytics. The implementation replaced a legacy state where a decentralized analytics estate could not keep up with the scale, experimentation cadence, and product intelligence required by tens of millions of users. Health app events and cycle and symptom data move through Lakeflow Jobs and land in Delta Lake, giving teams one trusted data foundation. Unity Catalog governs access, lineage, and compliance across sensitive datasets. Databricks SQL, MLflow, and Genie operationalize models, agents, and analytics that deliver health predictions and recommendations and product experiment insights.
Health app events and cycle and symptom data land on a shared platform, pipeline services clean and standardize them, governance controls enforce trusted access, and AI, model-serving, and analytics services deliver health predictions and recommendations and product experiment insights.
Databricks enables us to innovate without limits. The scalability and security have been game changers for our team,
Our previous systems couldn’t handle the scale and complexity of our data. Databricks provided the centralized platform we needed to innovate faster,
The ability to run experiments at this scale has fundamentally changed our approach to product development,
What is our MAU for last month?
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
Related
Databricks Sample Sep 21, 2026 KakaoStyle KakaoStyle needed a more reliable and governed data foundation because fragmented infrastructure and data quality issues were slowing AI-powered search, recommendations, and commerce operations. Batch failure alerts for the data engineering team fell by approximately 90% after migrating to Databricks… Databricks Data + AI Platform · Genie · Lakeflow Databricks Sample Jul 24, 2026 Highstreet Insurance Partners To meet that challenge, Highstreet created Ora, a proprietary, unified digital intelligence experience on Databricks that brings together trusted data, analytics, intelligent automation, and Al in a single environment. Imagine the daunting task of integrating billions of disparate data points, from over 200 local agency… Delta Lake · Lakeflow Jobs · Unity Catalog Databricks Sample Jul 21, 2026 Zapier Zapier needed a better way to support AI-powered self-service analytics, conversational data access, and product personalization because legacy data silos and reliance on a central insights team meant many questions… 2–3 hours delivering ai-powered product and business insights Agent Bricks · Databricks Data + AI Platform · MLflow 