Customer Segmentation Using Machine Learning
Author(s):
Manas Dabhane, Vaishnavi Chamatkar, Shivam Irdande, Vedant Dhatrak, Prof. Madhavi Sadu
Keywords:
Machine learning, Customer segmentation, K-means algorithm, Data Visualisation.
Abstract
In today's competitive market landscape, understanding customer behavior is crucial for businesses to tailor their marketing strategies effectively. This project explores the application of machine learning techniques for customer segmentation, aiming to enhance marketing strategies and improve customer satisfaction. By analyzing diverse datasets encompassing demographic, transactional, and behavioral data, the project employs clustering algorithms such as K-means, hierarchical clustering, and DBSCAN to group customers based on similarities in their attributes and behaviors. Feature engineering techniques are utilized to extract meaningful insights, enhancing the accuracy of segmentation. Moreover, dimensionality reduction methods like PCA facilitate the visualization and interpretation of complex customer data. The project's outcomes include actionable insights for targeted marketing campaigns, personalized recommendations, and product customization. By implementing machine learning-driven customer segmentation, businesses can optimize resource allocation, foster customer loyalty, and ultimately drive sustainable growth in today's dynamic market environment.
Article Details
Unique Paper ID: 164548

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 1558 - 1562
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