Analytical Case Files

CASE STUDIES

Selected work, explained through the lens of data.

Select Case File To Inspect3 Research Studies Available
CASE FILE NO. 01
April 2026

CUSTOMER SHOPPING BEHAVIOR ANALYSIS

Data Analytics / Business Intelligence
PythonSQLPostgreSQLPower BIPandasNumPyExcelMatplotlibSeaborn
Core Research Question

"How can transactional customer data be transformed into useful business insights?"

Data Lifecycle & Cleaning Summary

DATA CLEANING ACTIONS
Missing Values: Handled via median demographic imputation & flagged nulls
Duplicates: Removed exact transaction key duplicates
Inconsistencies: Resolved category naming variations across regional entries
Data Types: Standardized ISO dates, float currency representations & integer IDs
DATA PIPELINE STEPS
Gathering: Extracted transactional history across multi-channel retail touchpoints.
Validation: Cross-checked aggregated revenue values against primary ledger statements.
Analysis: Performed customer segmentation, revenue trend analysis, and category performance mapping.

SQL Query Implementation

PostgreSQL Analytical Query

Analytical Data Visualization

CUSTOMER REVENUE & SEGMENTATION (RFM)Analytical Publication Style
Monthly Revenue Run-Rate & VIP Contribution
$100k$60k$20kQ1Q2Q3Q4
General CustomersVIP Segment (54% Rev)
RFM Segmentation Output

Key Insights & Impact

Revenue Trend Insights: Identified top 15% VIP demographic driving 54% total revenue
Category Performance: Highlighted 2 underperforming categories for inventory restructuring
Actionable Reporting: Interactive Power BI dashboard for executive decision-making

Result Summary: Created a structured analysis workflow and interactive Power BI dashboard that transformed raw transactional data into management-ready insights.