Customer Segmentation & RFM Analysis project

Customer Segmentation & RFM Analysis | Data Analyst Portfolio Project

In this project, I analysed over 1 million transactions from the Online Retail II dataset to identify high-value customer segments, calculate customer lifetime value, and uncover £41M in revenue at risk from churning customers.

This is an end-to-end data analytics project covering data cleaning, exploratory analysis, RFM scoring, cohort retention analysis, CLV calculation, SQL querying, and an interactive Power BI dashboard.
TOOLS USED

- Python — pandas, matplotlib, seaborn (data cleaning, RFM scoring, cohort analysis, CLV calculation)
- MySQL — business queries and customer segmentation
- Power BI — 3-page interactive dashboard

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KEY FINDINGS

- 1,482 Champions (25% of customers) drive 69% of total customer lifetime value
- £41M in revenue at risk from 780 "Can't Lose Them" customers showing churn signals
- 177x value gap between Champions (£177K avg CLV) and Lost customers (£1K)
- 75–80% of customers churn within the first month, revealing a critical onboarding gap
- Strong seasonal spike every November, driven by Christmas shopping behaviour
PROJECT STRUCTURE

- Data cleaning and preparation (1.07M → 805K rows)
- Exploratory data analysis
- RFM scoring and customer segmentation
- Cohort retention analysis
- Customer lifetime value calculation
- SQL business queries
- Interactive Power BI dashboard
LINKS

GitHub Repository: https://github.com/Ahmed-Al-Rafsan
LinkedIn: https://www.linkedin.com/in/ahmed-al-rafsan-/
ABOUT ME

I'm Rafsan, an aspiring Data Analyst based in Melbourne, building real-world portfolio projects to break into the industry. This is Project 3 in my portfolio series.

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Видео Customer Segmentation & RFM Analysis project канала Rafsan Data & AI Lab