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customer-insights

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This repository contains the Business Intelligence insights generated as part of the final project challenge for the DTU Data Science course 42578: Advanced Business Analytics

  • Updated Feb 14, 2021
  • Jupyter Notebook

This project demonstrates customer segmentation using K-Means clustering, a popular machine learning technique. By analyzing customer data, we group customers into distinct segments to better understand their behaviors and preferences. This segmentation can help businesses tailor their marketing strategies and improve customer satisfaction.

  • Updated Jun 19, 2024
  • Jupyter Notebook

Developed a Power BI Sales Performance and Customer Insights Dashboard to visualize key sales metrics, analyze customer behavior, and identify growth opportunities. The dashboard provided insights into sales trends, product performance, customer retention, and geographic distribution.

  • Updated Oct 11, 2024

Leveraging K-Means clustering, our project categorizes retail customers based on purchasing behaviors and demographics. This provides businesses with actionable insights to tailor marketing efforts, enhancing customer experience and boosting sales.

  • Updated Sep 17, 2023
  • Jupyter Notebook

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