https://github.com/sevilaymuni/ecommerce-insights-platform

https://github.com/sevilaymuni/ecommerce-insights-platform

Last synced: 4 months ago
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• Host: GitHub
• URL: https://github.com/sevilaymuni/ecommerce-insights-platform
• Owner: SevilayMuni
• Created: 2025-02-16T13:10:09.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2025-02-19T22:05:54.000Z (4 months ago)
• Last Synced: 2025-02-19T22:24:27.265Z (4 months ago)
• Language: Jupyter Notebook
• Size: 34 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# E-Commerce Customer Segmentation and Marketing Dashboard

## Overview

This project provides an end-to-end data analytics pipeline for customer segmentation, personalized marketing insights, and a real-time economic trends dashboard.      Built using Python, machine learning, and Streamlit, it enables businesses to understand customer behavior, segment customers based on purchasing patterns, and design personalized marketing campaigns. 

The analysis includes RFM (Recency, Frequency, Monetary) and K-Means segmentation, customer lifetime value (CLV) calculation, and other insights to drive strategic marketing decisions.

## 🖥️ Check Out eCommerce-SegmenTrack App

[![eCommerce-SegmenTrack](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://ecommerce-segmentrack.streamlit.app/)

## 🚀 Key Features

- **Customer Segmentation**: Using RFM analysis to segment customers into actionable groups such as "Lost Customers," "Potential Loyalists," "Loyal Customers," and more.   

- **Customer Lifetime Value (CLV)**: Calculate CLV to understand the long-term value of customers and prioritize high-value customers.   

- **Interactive Dashboard**: A Streamlit web app that provides interactive visualizations, including scatter plots, bar charts, heatmaps, and Sankey diagrams.   

- **Geolocation Analysis**: Insights into customer and seller locations, visualizing order flow and revenue distribution by city.   

- **Product Analysis**: Analysis of product categories, purchase counts, and customer reviews to identify top-performing products and categories.   

- **Economic Trends**: Integration with FRED API to analyze economic trends and their potential impact on e-commerce sales.   

## 📂 Repository Structure

📦 eCommerce-Segmentation-Dashboard  

│── data/                             # Contains processed datasets (Parquet and CSV files)   

│── e-commerce-segmentation.ipynb     # Jupyter Notebook for data analysis & model building   

│── streamlit_app.py                  # Streamlit dashboard script   

│── requirements.txt                  # List of dependencies   

│── README.md                         # Project documentation (this file)  

## 🔗 Dataset

The project is based on the Brazilian E-Commerce Public Dataset by Olist, containing over 100,000 orders from 2016 to 2018, including order status, payment details, customer location, product attributes, and reviews.   

[Dataset Link](https://www.kaggle.com/datasets/olistbr/brazilian-ecommerce)

## Acknowledgments

- Olist for providing the Brazilian E-Commerce dataset.

- Streamlit for the interactive web app framework.

- FRED (Federal Reserve Economic Data) for economic trend data.

## 👩‍💻 Author

📌 Developed by Sevilay Munire Girgin   

📧 [Contact Me](https://linktr.ee/sevilaymgirgin)   

🌐 [Portfolio](sevilaymuni.github.io/Girgin)