E-commerce Customer Analytics – Understanding Customer Behavior & Retention Strategies

[Egbe34]

👋 Introduction

Hello everyone! I’m Grace Egbe, a passionate Data Analyst with expertise in Python, SQL, Tableau, and Machine Learning.

I recently completed an E-commerce Customer Analytics Project that explores customer behaviour, sales trends, and retention strategies using Python & Tableau. I’d love to get feedback, suggestions, and collaboration opportunities from the community! 🚀

🔗 GitHub Repository: E-commerce Customer Analytics

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📊 Project Overview

Objective:

Understanding customer behaviour and key factors affecting sales and retention in an e-commerce business.

Tech Stack:

✔️ Python – Data Cleaning & Analysis
✔️ SQL – Querying & Data Exploration
✔️ Tableau – Interactive Dashboards & Insights
✔️ Pandas & Matplotlib – Data Visualization

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🔥 Key Insights from the Project

📊 Customer Retention Analysis
✔️ Returning customers contribute over 60% of total revenue.
✔️ Personalized marketing strategies increase repeat purchases.

📉 Sales Trends & Seasonal Analysis
✔️ Peak sales occur during holiday seasons.
✔️ Discounts and promotions drive higher engagement.

🛒 Customer Segmentation
✔️ High-value customers prefer specific product categories.
✔️ Analysing spending behaviour helps in targeted promotions.

🔗 View the Project on GitHub: E-commerce Customer Analytics

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📢 Looking for Feedback & Suggestions!

I’d love to hear your thoughts on:
✅ How can I improve my data analysis & visualization approach?
✅ Are there additional insights that would make the project more valuable?
✅ Any recommendations for further analysis or feature improvements?

Let’s connect and learn from each other! 😊

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📡 Connect With Me

📊 Portfolio: https://egbe34.github.io/portfolio/
📊 Kaggle: https://www.kaggle.com/graceegbe12
💼 LinkedIn: (https://www.linkedin.com/in/grace-egbe-77820b278/)
📧 Email: (graceegbe3@gmail.com)

I appreciate any feedback and suggestions! Thanks in advance! 🚀🔥