Predictive analytics for market trends using AI: A study in consumer behavior
1 Independent Researcher, Lagos, Nigeria.
2 Independent Researcher, Seattle, U.S.A.
3 Independent Researcher, London, United Kingdom.
4 MTN, Lagos Nigeria.
Research Article
International Journal of Engineering Research Updates, 2024, 07(01), 036–049.
Article DOI: 10.53430/ijeru.2024.7.1.0032
Publication history:
Received on 02 July 2024; revised on 13 August 2024; accepted on 15 August 2024
Abstract:
Predictive analytics, driven by artificial intelligence (AI), is revolutionizing the understanding and forecasting of market trends, particularly in the realm of consumer behavior. This study explores the application of AIpowered predictive analytics to anticipate market dynamics and consumer preferences, offering insights that enable businesses to make informed strategic decisions. By leveraging vast datasets, AI algorithms analyze historical data, detect patterns, and predict future trends with remarkable accuracy. This capability is especially pertinent in today's fastpaced market environment, where consumer behavior is increasingly influenced by diverse factors ranging from economic conditions to social media trends. The study examines various AI techniques such as machine learning, natural language processing, and deep learning, highlighting their roles in enhancing predictive accuracy. Machine learning algorithms, for instance, can process complex and largescale data to uncover hidden correlations and forecast consumer demand. Natural language processing enables the analysis of textual data from social media, reviews, and other sources, providing a deeper understanding of consumer sentiments and emerging trends. Deep learning models, with their advanced neural networks, further refine predictions by learning intricate patterns in data. Several case studies are presented to illustrate the practical applications and benefits of AI in predictive analytics. For example, retail companies utilize AI to predict inventory needs and optimize stock levels, thereby reducing costs and improving customer satisfaction. Similarly, the study discusses how ecommerce platforms analyze browsing and purchasing patterns to personalize recommendations, enhancing user engagement and boosting sales. However, the implementation of AIdriven predictive analytics also presents challenges. Data quality and integration, privacy concerns, and the need for specialized skills in data science and AI are significant hurdles that businesses must overcome. The study emphasizes the importance of addressing these challenges to fully harness the potential of AI in predictive analytics. In conclusion, predictive analytics using AI offers transformative capabilities for understanding and forecasting market trends. By providing precise and actionable insights into consumer behavior, it enables businesses to stay ahead of the competition and cater effectively to evolving market demands. The study underscores the need for continued research and development to further enhance the accuracy and applicability of AIdriven predictive analytics in diverse market contexts.
Keywords:
Predictive Analytics; Market Trends; AI; Study; Consumer Behavior
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0