Saurabh Mittal
North Carolina State University
Raleigh, NC 27695, United States
Prof. (Dr) Punit Goel
Maharaja Agrasen Himalayan Garhwal University
Uttarakhand, India
https://orcid.org/0000-0002-3757-3123
Abstract
In the rapidly evolving retail landscape, data science has emerged as a critical tool for revolutionizing operations, marketing, and inventory management. This study delves into the transformative impact of big data on advertisement strategies and inventory forecasting. By integrating advanced analytics, machine learning, and statistical models, retailers are now able to derive actionable insights from vast and diverse data streams. These insights not only enable a more personalized advertising approach but also optimize inventory levels, ensuring that products are available to meet consumer demand without incurring excessive holding costs. The research highlights how predictive analytics can forecast consumer behavior, seasonal trends, and market fluctuations with greater accuracy. Moreover, the deployment of data-driven strategies enhances decision-making processes by bridging the gap between customer expectations and supply chain logistics. The findings indicate that businesses leveraging these innovative techniques gain a competitive edge by improving marketing ROI and reducing wastage through precise demand planning. Furthermore, the study discusses the challenges faced in integrating complex data systems and the importance of maintaining data quality and security. As retail environments become more data-intensive, the role of data science continues to expand, offering new possibilities for dynamic pricing, targeted promotions, and streamlined operations. Ultimately, the integration of big data analytics within the retail sector represents a strategic shift towards more agile, efficient, and customer-centric business models, ensuring long-term sustainability and growth in an increasingly competitive market.
Keywords
Data Science, Big Data, Retail, Advertisement, Inventory Forecasting, Machine Learning, Predictive Analytics, Consumer Behavior, Supply Chain Optimization
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