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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

The Future Landscape of AI and ML in Marketing

We are now entering the final stretch of our exploration into the integration of machine learning (ML) and Generative AI (GenAI) in marketing. The landscape of AI and ML is dynamic and rapidly evolving, however, setting the stage for even more exciting changes. So far, we’ve explored how GenAI and data-driven insights have revolutionized marketing strategies, guiding us in everything from decoding marketing KPIs and obtaining detailed customer insights to crafting compelling content, enhancing brand presence, and micro-targeting consumers. This chapter aims to consolidate our understanding of key AI and ML concepts and project some of their future applications in marketing.

In particular, we will start by reconciling the GenAI fundamentals introduced in previous chapters before exploring emerging technologies such as multi-modal GenAI and advanced model architectures and tools that synthesize diverse data types for creating...

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