Problem Statement
The client is a global retail enterprise with a GCC focused on data, analytics, and ML operations across global markets. Polestar Analytics worked as an embedded analytics partner to strengthen loyalty intelligence, promotion visibility, data infrastructure modernization, and Customer 360 initiatives.
Key Challenges
- Disconnected dashboards limited promotion performance visibility.
- Anonymous shoppers were difficult to identify and target for loyalty programs.
- Legacy Hive-based data created weak governance and table management issues.
- ML models lacked proper versioning, rollback, and audit trails.
- Production pipelines depended on individual engineers.
- High pipeline costs and long runtimes slowed analytics delivery.
Architecting with the Best Tech Stack
Solution Implemented
- A2K Customer Intelligence: Identified non-loyal shoppers and built scoring logic to surface high-value loyalty targets.
- Promotion Analytics: Built a unified pre vs. post promotion dashboard to compare planned and actual outcomes.
- Unity Catalog Migration: Migrated legacy Hive data to Databricks Unity Catalog for lineage, access control, and governance.
- ML Model Versioning: Introduced MLflow for model rollback, auditability, and production reliability.
- CI/CD Enablement: Set up GitHub, CI/CD pipelines, and service principals to reduce engineer dependency.
- Pipeline Optimization: Right-sized datasets, caching, and job clusters to reduce cost and runtime.
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