PYTHON · TABLEAU · CUSTOMER ANALYTICS
Retail Sales & Customer Segmentation Analysis
An end-to-end analytics case study focused on sales performance, discount impact, customer value, and operational efficiency.
Goal
The goal of this project was to understand sales performance, identify high-value customers, evaluate discount impact, and provide actionable recommendations for business improvement.
Background Information
The dataset includes customer orders, sales, profit, discounts, delivery performance, and customer-level information. The analysis focused on understanding what drives revenue, margin, and customer value.
Process
1. Data Cleaning
Cleaned missing values, standardized fields, checked duplicates, and prepared the dataset for analysis.
2. Customer Segmentation
Created RFM segmentation to identify high-value, loyal, at-risk, and low-engagement customer groups.
3. Dashboarding
Built Tableau dashboards to visualize sales performance, discount impact, customer segments, and fulfillment efficiency.
Key Insights
- High discounting reduced profitability in several product groups.
- Repeat customers contributed strongly to overall sales value.
- Delivery delays created operational friction and potential churn risk.
- Customer segmentation helped identify groups for targeted retention.
Recommendations
The business should monitor discount usage more carefully, prioritize high-value customer retention, and improve fulfillment processes for delayed orders.