RetailGPT: A Fine-Tuned LLM Architecture for Customer Experience and Sales Optimization
In today’s fast-paced retail environment, customer experience quality has become a key determinant of business success. RetailGPT is an AI system designed to revolutionize retail operations by offering personalized shopping experiences, automated customer service, and insights into inventory management. Powered by deep learning algorithms, RetailGPT analyzes data from e-commerce transactions, customer interactions, and sales records to predict consumer behavior with high accuracy.RetailGPT’s core feature is personalized product recommendations, which enhance customer satisfaction and boost sales by suggesting products that align with individual preferences. It also employs advanced sentiment analysis to gauge customer emotions and feedback in real time, allowing retailers to swiftly adjust their strategies for improved service quality.RetailGPT streamlines customer interactions, reduces wait times, cuts operational costs, and enhances engagement. Beyond answering FAQs, it learns from each interaction, continually refining its response accuracy. Additionally, RetailGPT provides data-driven sales optimization strategies by analyzing sales trends and forecasting future demand, aiding retailers in making informed decisions on stock levels and marketing campaigns. With visual tools like sales trend graphs, recommendation accuracy plots, customer satisfaction heatmaps, and inventory forecasts, RetailGPT offers managers actionable insights into business performance, enabling them to address issues promptly. As a disruptive solution, RetailGPT not only enhances customer experiences but also optimizes sales operations, helping retailers achieve better business outcomes and foster customer loyalty.
Authors
- Farooq Shareef
- Rishi Ajith
- Parth Kaushal
- Karthik Sengupta
Institutions
- Indraprastha Institute of Information Technology Delhi (IN)
- Indiana Wesleyan University (US)
- Vellore Institute of Technology University (IN)
Publication Details
- Published
- 2024-10-23
- DOI
- https://doi.org/10.1109/icssas64001.2024.10760685
- Citations
- 4
- Primary Topic
- Traffic Prediction and Management Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.06