IJBTT

Enhancing Retail and AdTech Efficiency with Cloud and AI-Driven Customer Insights
	© 2024 by IJCTT Journal
	Volume-72 Issue-10
	Year of Publication : 2024
	Authors : Balaji Thadagam kandavel, Naga Harini Kodey, Navadeep Vempati
	DOI :  10.14445/22312803/IJCTT-V72I10P111

How to Cite?

Balaji Thadagam kandavel, Naga Harini Kodey, Navadeep Vempati, "Enhancing Retail and AdTech Efficiency with Cloud and AI-Driven Customer Insights," International Journal of Computer Trends and Technology, vol. 72, no. 10, pp. 66-71, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I10P111

Abstract
In today's digital age, retail companies rapidly adopt advanced technologies like cloud computing and machine learning to transform customer experience and operational efficiency. This paper explores how cloud infrastructure, combined with machine learning algorithms, enables retailers to streamline processes, offer personalized customer experiences, and optimize operations. By leveraging the flexibility and scalability of cloud services, retailers can gather and process vast amounts of data from multiple touchpoints, such as sales records, customer transactions, and online reviews, leading to insights that enhance decision-making. Tools like regression models and prediction algorithms were employed to analyze data for inventory forecasting, demand prediction, and personalized recommendations. Data was collected from five major retailers: two years' sales worth, customer reviews, and operational reports. Machine learning models significantly improved customer retention rates and inventory prediction accuracy. Our study also includes a proposed architecture for deploying these solutions effectively, followed by a detailed analysis of the results obtained through their implementation. The results suggest combining cloud and machine learning significantly enhances customer engagement and operational metrics, driving growth in the increasingly competitive retail industry.

Keywords
Retail, Cloud computing, Machine learning, Customer experience, Operational efficiency.

Reference

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