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AI-Driven Retail Analytics: Leveraging Predictive Models for Consumer Goods and Retail Optimization

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  • Ruchi Agarwal

Abstract

The increasing complexity of consumer behavior, coupled with the globalized nature of retail, has prompted the adoption of advanced analytics to optimize operations and enhance customer experiences. Artificial Intelligence (AI) has emerged as a cornerstone of this transformation, enabling predictive models that drive data-informed decision-making in the retail and consumer goods sectors. This paper explores the role of AI-driven predictive models in optimizing retail operations, from demand forecasting and inventory management to personalized marketing and supply chain efficiencies. Through a detailed examination of the methodologies, use cases, and challenges in AI adoption, we aim to offer a comprehensive understanding of how predictive analytics is shaping the future of retail and consumer goods.

Suggested Citation

  • Ruchi Agarwal, 2024. "AI-Driven Retail Analytics: Leveraging Predictive Models for Consumer Goods and Retail Optimization," Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, Open Knowledge, vol. 2(1), pages 271-277.
    • Handle: RePEc:das:njaigs:v:2:y:2024:i:1:p:271-277:id:235
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    References listed on IDEAS

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    1. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
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