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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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Toc

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

Summary

This chapter has provided a detailed exploration of RAG and its transformative impact on precision marketing. By integrating generative AI with dynamic retrieval systems, RAG overcomes the limitations inherent in previous models like ZSL and FSL by incorporating real-time, context-specific data into content generation. This enables an unprecedented level of personalization in marketing strategies, enhancing the relevance and efficacy of marketing content tailored to individual consumer preferences and current market conditions.

We’ve used practical examples and mathematical models to demonstrate how RAG effectively combines data freshness and specificity, thereby elevating consumer engagement and optimizing conversion rates.

The discussion also covered the technical mechanisms that underpin RAG, from query generation and information retrieval to the iterative refinement of generated content. These elements ensure that the content not only resonates with the...

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