Consumer Behavioural Analysis Using Advanced Clustering Techniques
Abstract & Details
Research Area
Computer Science and Engineering
Keywords
Customer Segmentation
K-Means Clustering
MeanShift
DBSCAN
RFM Analysis
Unsupervised Machine Learning
Streamlit
Business Intelligence
Behavioural Analytics
MongoDB
Abstract
The rapid proliferation of digital transactions has generated vast volumes of customer data, creating an urgent need for intelligent and scalable segmentation tools that convert raw information into actionable business intelligence. This paper presents a comprehensive, web-based Customer Behavioural Analysis platform that integrates three complementary unsupervised machine learning algorithms—K-Means, MeanShift, and DBSCAN within a unified, user-friendly interface. The system is built using the Streamlit framework and supports secure multi-user authentication backed by MongoDB, complete activity logging, bulk dataset processing, real-time individual customer profiling, interactive visualizations, personalized discount recommendations, and automated email reporting.
The proposed platform adopts the Recency-Frequency-Monetary (RFM) behavioural scoring model as its analytical foundation, further enriched by Annual Income as an additional feature. A weighted composite scoring scheme is applied and standardized before clustering. K-Means provides fixed-group segmentation, MeanShift automatically discovers optimal clusters, and DBSCAN detects outliers and irregular behavioural patterns.
The system bridges advanced machine learning theory with practical business applications, making enterprise-grade customer analytics accessible to non-technical users across industries such as retail, e-commerce, banking, and services.
License
This work is licensed under a Creative
Commons
Attribution-ShareAlike 4.0 International License.
Author Information
| # | Name | Institute / Affiliation |
|---|---|---|
| 1 | Supriya Thati | Sphoorthy Engineering College |
| 2 | Kaja Masthan | Sphoorthy Engineering College |
| 3 | Pravalika Konduru | Sphoorthy Engineering College |
| 4 | Aishwarya Jakkidi | Sphoorthy Engineering College |
| 5 | Bhanu Prasad Karvanga | Sphoorthy Engineering College |
| 6 | Lasya Priya K | Sphoorthy Engineering College |
How to Cite
Use the following formats to cite this article in your research.
APA Style
Thati, Supriya, Masthan, Kaja, Konduru, Pravalika, Jakkidi, Aishwarya, Karvanga, Bhanu Prasad, & K, Lasya Priya (2026). Consumer Behavioural Analysis Using Advanced Clustering Techniques. International Journal of Advance Research and Innovative Ideas In Education, 12(2), 763-777.
MLA Style
Thati, Supriya, et al. "Consumer Behavioural Analysis Using Advanced Clustering Techniques." International Journal of Advance Research and Innovative Ideas In Education, vol. 12, no. 2, 2026, pp. 763-777.
IEEE Style
Supriya Thati, Kaja Masthan, Pravalika Konduru, Aishwarya Jakkidi, Bhanu Prasad Karvanga, and Lasya Priya K, "Consumer Behavioural Analysis Using Advanced Clustering Techniques," International Journal of Advance Research and Innovative Ideas In Education, vol. 12, no. 2, pp. 763-777, 2026.
Vancouver Style
Thati Supriya, Masthan Kaja, Konduru Pravalika, Jakkidi Aishwarya, Karvanga Bhanu Prasad, K Lasya Priya. Consumer Behavioural Analysis Using Advanced Clustering Techniques. International Journal of Advance Research and Innovative Ideas In Education. 2026;12(2):763-777.
Harvard Style
Thati, Supriya, Masthan, Kaja, Konduru, Pravalika, Jakkidi, Aishwarya, Karvanga, Bhanu Prasad, & K, Lasya Priya (2026) 'Consumer Behavioural Analysis Using Advanced Clustering Techniques', International Journal of Advance Research and Innovative Ideas In Education, 12(2), pp. 763-777.
Chicago Style
Thati, Supriya, et al. "Consumer Behavioural Analysis Using Advanced Clustering Techniques." International Journal of Advance Research and Innovative Ideas In Education 12, no. 2 (2026): 763-777.
Turabian Style
Thati, Supriya, et al. "Consumer Behavioural Analysis Using Advanced Clustering Techniques." International Journal of Advance Research and Innovative Ideas In Education 12, no. 2 (2026): 763-777.
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