Abstract
SegmentIQ is a full-stack, browser-based intelligent analytics platform that automates customer segmentation using RFM (Recency, Frequency, Monetary) scoring, AI-powered behavioral clustering, and a conversational AI chatbot powered by the Anthropic Claude API. The system accepts CSV data, text, and voice inputs, enabling multimodal analysis for a more accurate and engaging user experience. For text and voice inputs, the application utilizes natural language processing and speech-to-text techniques (via Whisper) to extract behavioral context and generate concise customer summaries. For CSV data, RFM methods analyze purchase patterns to detect dominant customer segments. Based on the identified segment, the system recommends relevant business actions using real-time data from the Anthropic Claude AI API. The application is built using modern web technologies including HTML5, CSS3, and Vanilla JavaScript ES2022 for an interactive and user-friendly interface, integrating deep learning models and external APIs to deliver dynamic segmentation results. By combining customer behavior analysis with intelligent AI recommendations, this 0system enhances user engagement, data-driven personalization, and analytical well-being in digital analytics platforms. This project demonstrates the practical application of AI in affective computing, multimedia processing, and recommendation systems.
Keywords
Customer Segmentation, RFM Analysis, AI Chatbot, Anthropic Claude, OTP Authentication, Churn Prediction, LTV Modelling, Data Visualization, Full-Stack Web Application.
Citations
IRE Journals:
Dr. S. Parthasarathy, Suresh R "SegmentIQ: Customer Segmentation Analysis with AI Insight Chatbot" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 2186-2192 https://doi.org/10.64388/IREV9I9-1715505
IEEE:
Dr. S. Parthasarathy, Suresh R
"SegmentIQ: Customer Segmentation Analysis with AI Insight Chatbot" Iconic Research And Engineering Journals, 9(9) https://doi.org/10.64388/IREV9I9-1715505
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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