Year: 2026
Type: article
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.
Authors: Haritha M, Sharvitha G V, Shri Rithanya S, Subhitcha S, Thanirika G; Vishnu Priya S
Language: English
Subfield: Marketing
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W7156206994
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