Abstract
In the fast-evolving landscape of e-commerce, real-time promotions have emerged as a powerful tool for enhancing user experience and driving sales. This paper explores the design and implementation of real-time promotional systems aimed at maximizing user savings while aligning with retailer objectives. By leveraging advanced data analytics, machine learning algorithms, and user behavior insights, these systems dynamically curate personalized discounts, offers, and incentives during a shopper's journey. The proposed framework integrates key components such as predictive modeling for pricing, inventory management, and customer segmentation, ensuring optimal timing and relevance of promotions. Furthermore, the study addresses challenges related to scalability, latency, and ethical considerations, such as ensuring transparency and avoiding exploitative pricing practices. Through simulations and case studies, the findings demonstrate that real-time promotions can significantly increase customer satisfaction and loyalty while enhancing overall profitability for online retailers. This research provides a blueprint for retailers aiming to adopt intelligent promotional strategies in a competitive digital marketplace.
Keywords
Real-time promotions, user savings, personalized discounts, e-commerce, machine learning, customer segmentation, predictive modeling, dynamic pricing, online shopping, digital marketing strategies.
Citations
IRE Journals:
Varun Garg , Lagan Goel
"Designing Real-Time Promotions for User Savings in Online Shopping" Iconic Research And Engineering Journals Volume 8 Issue 5 2024 Page 724-754
IEEE:
Varun Garg , Lagan Goel
"Designing Real-Time Promotions for User Savings in Online Shopping" Iconic Research And Engineering Journals, 8(5)
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