Current Volume 9
Abstract
The AI-Based Customer Analytics System is a web platform that analyzes customer behavior to support better digital marketing decisions. Businesses generate large amounts of customer data, but extracting useful insights is difficult. This system uses artificial intelligence and machine learning to convert raw data into meaningful business intelligence. Its main goal is to identify high-value customers, predict customer churn, and improve marketing strategies. By combining data from transactions, website activity, and engagement metrics, it gives a complete view of customers. This helps businesses focus on profitable customers, reduce unnecessary marketing costs, and improve ROI. The system uses machine learning models like Random Forest for Customer Lifetime Value prediction, XGBoost for churn prediction, and clustering for customer segmentation. It also generates an overall AI-based customer score based on behavior and engagement, helping businesses prioritize customers and plan targeted campaigns. The frontend is built with React, HTML, CSS, and JavaScript, while the backend uses FastAPI. Data is stored in PostgreSQL and processed using Pandas and Dask, ensuring efficient handling of large datasets. An interactive dashboard displays key metrics like customer count, retention rate, churn probability, and segmentation using charts and graphs. The system also provides recommendations, such as strategies to retain customers or target high-value users. Overall, this platform improves marketing efficiency, supports data-driven decisions, and helps businesses stay competitive.
Keywords
Customer Insightx, AI-Based Customer Analytics, XGBoost, FastAPI, HTML CSS JavaScript frontend, RFM (Recency, Frequency, Monetary), (CRM) systems, Customer LifeTime Value (CLV) Prediction, churn prediction, Plotly, Tableau, and Power BI, , Streamlit, instant insights, fast-paced, data encryption, secure authentication, leveraging, Random Forest, ETL (Extract, Transform, Load) processes, Return On Investment (ROI).
Citations
IRE Journals:
Haritha M, Sharvitha G V, Shri Rithanya S, Subhitcha S, Thanirika G; Vishnu Priya S "Customer InsightX" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 2941-2951 https://doi.org/10.64388/IREV9I10-1717027
IEEE:
Haritha M, Sharvitha G V, Shri Rithanya S, Subhitcha S, Thanirika G; Vishnu Priya S
"Customer InsightX" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1717027
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.
Important Links
For Authors
Volume 8:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12