E-commerce Customer Analytics and Business Insights

This project focuses on analyzing customer behavior, retention, and acquisition trends in an e-commerce dataset. It employs techniques like RFM analysis, cohort analysis, segmentation, and visualization to uncover actionable insights, helping businesses improve their customer lifetime value (CLV) and optimize retention strategies.

📌 Table of Contents

• Overview
• Technologies Used
• Project Type
• Data Description
• Solution Approach
• Key Data Insights
• Business Recommendations

🛠 Technologies Used

• Python (Pandas, NumPy, Matplotlib, Seaborn, lifetimes)
• Jupyter Notebook & Google Colab

Project Type

• Data Preprocessing/Cleaning and Feature Engineering
• Advanced Visualization Techniques, RFM Analysis, CLV Segmentation, and Cohort Analysis

Data Description

Column Name	Description
InvoiceNo	Invoice number that consists of 6 digits. If this code starts with the letter 'C', it indicates a cancellation.
StockCode	Product code that consists of 5 digits.
Description	Product name.
Quantity	The quantities of each product per transaction.
InvoiceDate	This represents the day and time when each transaction was generated.
UnitPrice	Product price per unit.
CustomerID	Customer number that consists of 5 digits. Each customer has a unique customer ID.
Country	Name of the country where each customer resides.

Solution Approach

This project follows a structured data analysis workflow:

1. Data Cleaning & Preprocessing: Handling missing values, outliers, and data transformations.
2. Exploratory Data Analysis (EDA): Visualizing customer behavior, revenue trends, and segmentation insights.
3. RFM Analysis: Categorizing customers based on Recency, Frequency, and Monetary values.
4. Cohort Analysis: Analyzing retention trends over time.

📈 Key Data Insights

1. Customer Segmentation:
   • Identified 1,306 loyal customers contributing significantly to monthly revenue.
   • At-risk customers represent a key area for retention strategies.
2. 📊 Customer Lifecycle Analysis:
   • Retention rates declined sharply after the second cohort month, highlighting the need for stronger engagement post-onboarding.
3. 💰 Revenue Contribution:
   • Loyal customers contribute over 75% of the revenue, emphasizing the value of retention efforts.
4. 🛍️ Purchasing Behavior:
   • Weekdays observed a higher purchasing frequency, suggesting optimal times for targeted campaigns.

🔑 Business Recommendations

1. 🔄 Retention Strategies:
   • Develop loyalty programs and personalized offers for at-risk customers.
   • Automate re-engagement campaigns using email or app notifications.
2. 📢 Acquisition Optimization:
   • Invest in digital ads targeting high-growth regions like California.
   • Run location-based promotional campaigns to tap underperforming regions.
3. ⭐ Customer Experience Enhancement:
   • Analyze feedback from at-risk customers to address concerns and improve satisfaction.
   • Offer exclusive benefits for customers in their first two months to boost retention.
4. 📊 Data-Driven Campaigns:
   • Leverage RFM analysis to run targeted promotions for high-value segments.
   • Use geographic insights to tailor regional marketing campaigns.