Problem Statement
A leading alco-beverage organization operates in one of the most competitive categories where market share is won at the point of distribution, through outlet presence, channel reach, and on-premise execution.
The organization set a clear ambition: a 5% market share increase over 3 years. Translating that into week-on-week progress required sharper channel visibility, tighter business-IT alignment, and a reporting foundation built for the pace of the growth agenda.
To solve this, Polestar Analytics designed and delivered the Commercial Analytics Accelerator, a KPI-led framework built on Modern Data Warehousing principles and a governed Snowflake foundation.
- Undefined KPI Framework: No shared vocabulary linking commercial ambition to measurable drivers of market share.
- Reach and Distribution Blind Spots: Inconsistent reporting on outlet presence and distribution effectiveness across the national on-premise footprint.
- Spreadsheet-Led Reporting: Manual, error-prone workflows caused delayed cycles and inconsistent KPI interpretation across functions.
- Disconnected Data Ecosystem: Multiple internal and external datasets, each with different structures and refresh cadences, prevented a single source of truth.
- Missing Execution Architecture: Growth ambition was clear, but no operating model existed to translate strategy into measurable action.
Solution Implemented
- KPI Driver Tree: Mapped commercial goals to operational levers across Customer Classification, Channel Reach, Distribution Effectiveness, and Sales Performance.
- Centralized Data on Snowflake: Unified internal and external sources under a single, governed foundation.
- Refined Legacy Data: dbt-driven pipelines cleaned and structured legacy data into the Snowflake lakehouse.
- Automated Data Flow: End-to-end orchestration via Azure Data Factory with embedded governance and quality checks.
- Decision-Ready Dashboards: Power BI dashboards built for business users, moving teams from data to decision faster.
- Scalable Architecture: Engineered for scalability, ease of access, and future extensibility.
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