Case Study: Grab achieves unified consumer insights and faster personalized experiences with Databricks Lakehouse Platform

Personalising‌ ‌experiences‌ ‌with‌ ‌data‌ ‌and‌ ‌ML‌

Grab, Southeast Asia’s largest online-to-offline platform with over 200 million downloads and six billion transactions, faced fragmented consumer data across 351 cities and multiple product teams. Disparate pipelines and inconsistent consumer segmentation prevented cross-functional insights, increased engineering overhead, and slowed development of personalized features and marketing ROI improvements.

Using the Databricks Lakehouse on Azure to build its C360 platform, Grab created a single source of truth with Delta Lake to ingest, secure and optimize thousands of user signals. The self-service portal and APIs democratized consumer attributes, cutting data refresh times from weeks to hours, reducing costs and enabling faster feature development (for example, a contact-center segment feature built in weeks), leading to richer personalization, better recommendations and more efficient cross-team collaboration.

Grab

Nikhil Dwarakanath

Head of Analytics