Flipp democratizes shopping data with a Databricks lakehouse for personalized offers
Flipp built a lakehouse architecture on Databricks and Delta Lake to unify siloed retail partner content and user-generated behavioral data, using MLflow for experiment tracking and Tableau for reporting, powering recommendation models for real-time personalized offers and contributing to a 10% increase in in-store traffic with retailers.
Overview
Flipp built a lakehouse architecture on Databricks and Delta Lake to unify siloed retail partner content and user-generated behavioral data, using MLflow for experiment tracking and Tableau for reporting, powering recommendation models for real-time personalized offers and contributing to a 10% increase in in-store traffic with retailers.
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Inspect the highlighted sourceThe challenge
Flipp's primary data sources — retail partner content and user-generated behavior — were siloed, unstructured and hard to access; individual events had to be manually logged and parsed, and data scientists relied on spreadsheets to track experiments, causing reproducibility problems and duplicated work.
The solution
Flipp built a lakehouse architecture on Databricks and Delta Lake for fast data access via Apache Spark APIs, used MLflow to automatically track experiments, and visualized data with Tableau, enabling data science teams to build predictive analytics for recommendation engines that power real-time personalized offers.
Reported business value
Flipp's data teams gained rapid, democratized access to data for both regular reporting and ad hoc analysis, replacing tedious spreadsheet tracking, and the platform contributed to a 10% increase in in-store traffic with retailers.
Sources
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