Machine Learning for Transformative Marketing Strategies: Applications, Challenges, and Future Directions in the Era of Data-Driven Decision Making
Keywords:
customer segmentation, data-driven strategies, dynamic pricing, machine learning, marketing optimization, predictive analytics, sentiment analysisAbstract
The integration of machine learning (ML) into marketing has changed the way businesses interact with consumers and make strategic decisions. By enabling deeper insights into customer behavior, segmentation, and personalization, ML offers tools to optimize marketing operations and increase efficiency. This paper explores the applications of ML in marketing, focusing on predictive analytics, sentiment analysis, customer segmentation, and dynamic pricing. It further discusses the challenges of implementing ML, including data quality, ethical concerns, and the interpretability of complex models. With the rapid advancement of technologies, ML is reshaping the marketing landscape, driving a shift from intuition-driven to data-driven strategies.
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