A Personalized Digital Billing For Modern Retailers

UniRetail is an innovative mobile application designed to revolutionize retail operations by integrating advanced machine learning ML capabilities and modern technologies. The platform enables retailers to centralize and streamline critical functions, including inventory management, dynamic pricing, and targeted promotions, while delivering a seamless, personalized shopping experience for customers. Machine learning algorithms are at the core of UniRetail, empowering store owners with predictive analytics for demand forecasting, personalized product recommendations using collaborative filtering, and customer segmentation through clustering techniques. Natural language processing NLP enhances product search and filtering, while sentiment analysis refines customer feedback to improve service quality. Additionally, fraud detection models ensure secure and efficient multi-payment checkouts. UniRetail’s robust, cloud-based architecture, built on microservices, supports scalability and integrates seamlessly with existing retail systems. The use of technologies like TensorFlow, PyTorch, and Apache Spark ensures efficient data processing and real-time ML model deployment through Kubernetes and AWS SageMaker. By uniting operational efficiency, data driven insights, and customer engagement, UniRetail aims to be a transformative tool for modern retail enterprises, helping them thrive in the digital landscape.

Publication Details

Journal
International Journal of Research and Development in Engineering Sciences
Published
2025-03-22
DOI
https://doi.org/10.63328/ijrdes-v7ri2p7
Primary Topic
Diverse Scientific and Economic Studies
Type
article
Field-Weighted Citation Impact
0.00
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article

A Personalized Digital Billing For Modern Retailers

International Journal of Research and Development in Engineering Sciences
Diverse Scientific and Economic Studies
article

A Personalized Digital Billing For Modern Retailers

article en

Abstract

UniRetail is an innovative mobile application designed to revolutionize retail operations by integrating advanced machine learning ML capabilities and modern technologies. The platform enables retailers to centralize and streamline critical functions, including inventory management, dynamic pricing, and targeted promotions, while delivering a seamless, personalized shopping experience for customers. Machine learning algorithms are at the core of UniRetail, empowering store owners with predictive analytics for demand forecasting, personalized product recommendations using collaborative filtering, and customer segmentation through clustering techniques. Natural language processing NLP enhances product search and filtering, while sentiment analysis refines customer feedback to improve service quality. Additionally, fraud detection models ensure secure and efficient multi-payment checkouts. UniRetail’s robust, cloud-based architecture, built on microservices, supports scalability and integrates seamlessly with existing retail systems. The use of technologies like TensorFlow, PyTorch, and Apache Spark ensures efficient data processing and real-time ML model deployment through Kubernetes and AWS SageMaker. By uniting operational efficiency, data driven insights, and customer engagement, UniRetail aims to be a transformative tool for modern retail enterprises, helping them thrive in the digital landscape.

International Journal of Research and Development in Engineering SciencesVol. 7(2)
Openalex Percentile: Top 32%
Diverse Scientific and Economic Studies
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A Personalized Digital Billing For Modern Retailers · International Journal of Research and Development in Engineering Sciences (2025) | TGRS Research Map | TGRS