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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

Next-generation AI technologies in marketing

This section highlights some of the significant advancements and novel model architectures that are redefining the boundaries of what’s possible in AI for marketing. Each of these technologies helps us push the envelope in creating more dynamic, responsive, and personalized marketing solutions. The current maturity of these technologies will also be discussed and, in the case of ReAct and multi-modal GenAI, these technologies are already available in products and can be used by early adopters for their marketing strategies.

From RAG to ReAct

ReAct, short for “Reasoning and Acting,” represents a significant evolution in GenAI systems by integrating sophisticated reasoning and external tool interactions. ReAct builds upon the capabilities of RAG, ZSL, FSL, and prompt engineering, by incorporating chain-of-thought processes, which involve the AI system breaking down complex tasks into a sequence of smaller, manageable...

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