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Machine Learning and Generative AI for Marketing

Machine Learning and Generative AI for Marketing

By : Yoon Hyup Hwang, Nicholas C. Burtch
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Machine Learning and Generative AI for Marketing

Machine Learning and Generative AI for Marketing

By: Yoon Hyup Hwang, Nicholas C. Burtch

Overview of this book

In the dynamic world of marketing, the integration of artificial intelligence (AI) and machine learning (ML) is no longer just an advantage—it's a necessity. Moreover, the rise of generative AI (GenAI) helps with the creation of highly personalized, engaging content that resonates with the target audience. This book provides a comprehensive toolkit for harnessing the power of GenAI to craft marketing strategies that not only predict customer behaviors but also captivate and convert, leading to improved cost per acquisition, boosted conversion rates, and increased net sales. Starting with the basics of Python for data analysis and progressing to sophisticated ML and GenAI models, this book is your comprehensive guide to understanding and applying AI to enhance marketing strategies. Through engaging content & hands-on examples, you'll learn how to harness the capabilities of AI to unlock deep insights into customer behaviors, craft personalized marketing messages, and drive significant business growth. Additionally, you'll explore the ethical implications of AI, ensuring that your marketing strategies are not only effective but also responsible and compliant with current standards By the conclusion of this book, you'll be equipped to design, launch, and manage marketing campaigns that are not only successful but also cutting-edge.
Table of Contents (16 chapters)
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14
Other Books You May Enjoy
15
Index

Summary

In this final chapter on ethics and governance in AI-enabled marketing, we have discussed the critical considerations and challenges associated with the use of AI technologies. We explored the ethical implications of data privacy, algorithmic bias, and the necessity for model transparency, emphasizing how these factors directly impact consumer trust and brand integrity. We also examined key regulatory frameworks, such as GDPR and CCPA, and discussed the importance of compliance in mitigating legal risks and fostering responsible AI practices.

We addressed practical strategies for ensuring ethical AI deployment in marketing, covering model explainability, bias mitigation, and balancing privacy with personalization. By establishing robust governance frameworks, including the formation of internal AI ethics committees, developing clear AI policies, and fostering continuous training and awareness, organizations can navigate the complex ethical landscape effectively. Understanding...

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