You will get a segmentation of customers
Project details
I performed customer segmentation using K-Means Clustering, a powerful unsupervised machine learning algorithm, to group customers based on their purchasing behavior. The goal was to identify distinct customer segments to help the business optimize marketing strategies, allocate resources effectively, and improve customer engagement.
Tools & Technologies Used:
Python: For data preprocessing, clustering, and analysis.
K-Means Clustering: For unsupervised segmentation.
Data Visualization: To present insights and segment distributions.
Outcome:
This project provided actionable insights into customer behavior, enabling the business to tailor marketing strategies, optimize resource allocation, and improve customer satisfaction. By focusing on high-value segments and reducing costs on low-value ones, the business can achieve better ROI and long-term growth.
Tools & Technologies Used:
Python: For data preprocessing, clustering, and analysis.
K-Means Clustering: For unsupervised segmentation.
Data Visualization: To present insights and segment distributions.
Outcome:
This project provided actionable insights into customer behavior, enabling the business to tailor marketing strategies, optimize resource allocation, and improve customer satisfaction. By focusing on high-value segments and reducing costs on low-value ones, the business can achieve better ROI and long-term growth.
Language
EnglishBusiness Type
StartupPlan Format
ExcelPlan Purpose
Partnerships/Joint Ventures, Personal UseWhat's included $30
These options are included with the project scope.
$30
- Delivery Time 3 days
- Customer Analysis
About Ngoc
Data Analysis | Excel | VBA | Visualization | Python
Hanoi, Vietnam - 12:16 pm local time
Steps for completing your project
After purchasing the project, send requirements so Ngoc can start the project.
Delivery time starts when Ngoc receives requirements from you.
Ngoc works on your project following the steps below.
Revisions may occur after the delivery date.
Data Preparation
Transformed raw transaction data into a structured format, including features like Recency (R), Frequency (F), Monetary Value (M), and Cost. Cleaned and preprocessed the data to ensure accuracy and consistency.
K-Means Clustering
Applied the K-Means algorithm to segment customers into meaningful groups. Determined the optimal number of clusters using data reduction techniques and analysis.