Retail Sales Data Analytics
End-to-end analytics platform for customer segmentation and marketing strategy optimization.
I. Problem Statement & Operational Challenge
Transforming siloed retail sales data from clients (LGS) into actionable insights for marketing and budgeting strategies.
II. Implementation Strategy
Provisioned a PostgreSQL database via Docker to host raw data. Used Jupyter Notebooks to establish a data connection and employed Pandas for cleaning and exploratory data analysis. Crucially, I integrated an RFM (Recency, Frequency, Monetary) segmentation model to categorize customer behavior.
III. Technical Stack & Tooling
IV. Constraints & Edge Cases
Managing data types across the SQL-to-Python bridge and ensuring the segmentation logic aligned with the client's commercial objectives.
V. Operational Outcome & Learnings
The RFM model identified high-value clusters and at-risk customers, directly informing a 15% improvement in marketing budget allocation strategies.