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In providing an in-depth exploration of cutting-edge technologies and how they are used to support data-driven marketing strategies and empower organizations to make the right decisions, Predictive Analytics and Generative AI for Data-Driven Marketing Strategies includes real-world case studies and examples from diverse marketing domains. This book demonstrates how predictive analytics and generative AI have been successfully applied to solve marketing challenges and drive tangible results. This book showcases emerging trends in predictive analytics and generative AI for marketing, and their potential impact on the future of data-driven marketing. This book is meant for professionals and scholars to gather the skills and resources to use predictive analytics and generative AI effectively for marketing strategies. This book: • Examines the different predictive analytics models and algorithms, such as regression analysis, decision trees, and neural networks, and demonstrates how they may be utilized to get insightful conclusions from marketing data. • Includes generative AI techniques, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), showcasing how these techniques can generate synthetic data for marketing insights and decision-making. • Highlights the importance of data-driven marketing choices and illustrates how generative AI and predictive analytics may be quite useful in this context. • Integrates the principles of data science with marketing concepts, offering a cohesive understanding of how predictive analytics and generative AI can power data-driven marketing decisions. • Presents the recent advances in predictive analytics and generative AI and discusses how they can affect the area of data-driven marketing.
This is a digital product.
Predictive Analytics and Generative AI for Data-Driven Marketing Strategies 1st Edition and published by Chapman & Hall. The Digital and eTextbook ISBNs for Predictive Analytics and Generative AI for Data-Driven Marketing Strategies are 9781040188835, 1040188834 and the print ISBNs are 9781032748917, 1032748915. Additional ISBNs for this eTextbook include 9781003472544, 9781040188767.


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