🧩 Trendora RFM Customer Segmentation Analysis¶
🛍️ Project Overview¶
Trendora is an European online fashion retailer operating via e-commerce and mobile app channels.
The goal of this project is to perform RFM (Recency, Frequency, Monetary) analysis to segment customers based on their purchasing behaviors and help the marketing team design targeted retention and loyalty strategies.
This project demonstrates my ability to:
- Conduct data transformation and customer-level aggregation using SQL and Python
- Apply RFM segmentation to derive behavioral insights
- Visualize results and segment distribution using Tableau
🎯 Business Objective¶
The marketing department wanted to:
- Identify high-value and at-risk customers
- Support personalized marketing campaigns and retention strategies
- Prioritize customer engagement based on behavioral scores
Analytical Question:
How can we categorize Trendora’s customers by their purchasing behavior to optimize marketing actions?
🧭 Problem-Solving Framework (4W1H)¶
| Question | Description |
|---|---|
| Who | The audience of this report are marketing and CRM managers at Trendora |
| What | What do they want to view in this report? Segmented customer groups to identify high-value customers, at-risk customers, and promising customers. |
| Why | Enable data-informed customer retention and improve marketing ROI |
| Where | Across Trendora’s e-commerce and mobile app channels in Europe |
| How | Extract and aggregate sales data with SQL, score customers with Python, visualize insights in PowerBI |
🗂️ Data Source¶
The dataset simulates an online retailer with multiple relational tables.
For this RFM analysis, the key tables used were:
| Table | Description |
|---|---|
| sales | Order-level data (sale_id, customer_id, sale_date, total_amount) |
| customers | Customer demographics (country, age_range, signup_date) |
Dashboard Snapshot¶
