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Machine Learning and Generative AI for Marketing

You're reading from   Machine Learning and Generative AI for Marketing Take your data-driven marketing strategies to the next level using Python

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835889404
Length 482 pages
Edition 1st Edition
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Authors (2):
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Nicholas C. Burtch Nicholas C. Burtch
Author Profile Icon Nicholas C. Burtch
Nicholas C. Burtch
Yoon Hyup Hwang Yoon Hyup Hwang
Author Profile Icon Yoon Hyup Hwang
Yoon Hyup Hwang
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Table of Contents (16) Chapters Close

Preface 1. The Evolution of Marketing in the AI Era and Preparing Your Toolkit FREE CHAPTER 2. Decoding Marketing Performance with KPIs 3. Unveiling the Dynamics of Marketing Success 4. Harnessing Seasonality and Trends for Strategic Planning 5. Enhancing Customer Insight with Sentiment Analysis 6. Leveraging Predictive Analytics and A/B Testing for Customer Engagement 7. Personalized Product Recommendations 8. Segmenting Customers with Machine Learning 9. Creating Compelling Content with Zero-Shot Learning 10. Enhancing Brand Presence with Few-Shot Learning and Transfer Learning 11. Micro-Targeting with Retrieval-Augmented Generation 12. The Future Landscape of AI and ML in Marketing 13. Ethics and Governance in AI-Enabled Marketing 14. Other Books You May Enjoy
15. Index

Governance and regulatory compliance

Adhering to appropriate governance practices while maintaining regulatory compliance is paramount. In this section, we will explore some of the many aspects related to these topics, including intellectual property protection and key components of building an ethical governance framework.

We will then look at some key regulatory compliance considerations around data use and processing, highlighting key frameworks like GDPR and CCPA, as well as touch upon other global regulations and industry-specific guidelines.

Intellectual property protection

AI models rely on vast amounts of data, which often include proprietary or even copyrighted content. Ensuring the protection of intellectual property and copyright rights, both of your organization and others, is essential to avoid legal issues in the development and deployment of AI models. In the following subsections, we discuss key aspects of data and model licensing and attribution, internal...

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