📊 Customer Segmentation & Revenue Optimization (RFM Analysis)

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📌 Business Problem

E-commerce businesses need to understand customer behavior to maximize retention, increase lifetime value (LTV), and prevent churn.

However, without proper segmentation, it becomes difficult to:

• Identify high-value customers
• Detect churn risk early
• Optimize marketing and retention strategies
• Improve profitability across product categories

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🎯 Objective

Develop an analytics solution to:

• Segment customers based on behavior (RFM model)
• Identify high-value and at-risk customer groups
• Analyze profitability and sales performance
• Support data-driven marketing and pricing decisions

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🧠 Solution Overview

This project delivers an executive-level analytics dashboard combining:

• RFM segmentation to classify customers by value and engagement
• Sales and profitability analysis to identify revenue drivers
• Operational insights to improve logistics and pricing decisions

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📊 Key KPIs

• Total Revenue: $8.43M
• Total Orders: 54k
• MoM Growth: -9.06%
• Average Order Value (AOV): $156.07

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💡 Key Business Insights

• High-Value Customers (VIPs):
  Small segment driving a disproportionate share of revenue, with high AOV in premium categories

• Conversion Opportunity:
  Large volume of new customers with strong potential for repeat purchases

• Churn Risk (At Risk Segment):
  High freight costs may be negatively impacting customer retention

• Profitability Issues:
  Negative margins identified in key product categories, requiring urgent action

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🎯 Business Impact

This analysis enables:

• Targeted retention strategies to maximize LTV
• Early identification of churn risk
• Optimization of pricing and product profitability
• More efficient marketing segmentation and campaigns

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🛠️ Strategic Recommendations

• 🎯 Retention Strategy:
  Prioritize VIP customers to maximize long-term revenue

• 📣 Marketing Optimization:
  Convert new customers into repeat buyers through targeted campaigns

• 🚚 Logistics Optimization:
  Reduce freight costs in low-margin regions to improve retention

• 💰 Pricing Strategy:
  Review pricing for products with negative margins

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🛠️ Tech Stack

• Power BI (Dashboard & Data Visualization)
• DAX (RFM logic, KPIs, growth calculations)
• Power Query (ETL, data transformation)

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📂 Dataset

Brazilian E-Commerce Public Dataset (Olist) ~100k orders (2016–2018) Access the original dataset here

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📸 Dashboard Preview

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🚀 How to Run

1. Download the .pbix file
2. Open in Power BI Desktop
3. Explore filters and segmentation

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