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@article{194935,
        author = {M.Sivasankari and K.MANIRAJ},
        title = {AI-Enabled Market Analysis and Visualization System for Enterprise Applications},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {6238-6243},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194935},
        abstract = {Contemporary enterprises struggle to derive unified insights from fragmented marketing channels spanning print, digital, and event-based platforms. This paper presents an intelligent analytics system that consolidates marketing data from newspaper classifieds, social media campaigns, and promotional events into a single decision-support platform. The proposed architecture employs a three-tier microservices design integrating a fine-tuned BERT transformer model for sentiment classification, achieving 89.3% accuracy across marketing domain text. Long Short-Term Memory networks deliver engagement rate forecasts with 8.7% MAPE over seven-day horizons, while K-Means clustering identifies six distinct customer segments for targeted campaign planning. A RESTful API gateway orchestrates communication between analytical services and an interactive React.js visualization dashboard. Pilot deployment results demonstrate a 67% reduction in manual reporting effort and a 23% improvement in cross-channel marketing ROI. The system provides enterprises with a scalable, modular solution for data-driven promotional strategy optimization.},
        keywords = {Sentiment Analysis, LSTM Forecasting, Customer Segmentation, Marketing Analytics, Enterprise Dashboard},
        month = {March},
        }