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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

Customer segmentation with product interests

We have discussed how we can build customer segments based on their purchase history in the previous section and how this can inform marketers on which segment to prioritize and strategize for the next marketing effort. Not only can we segment customers based on their purchase history, or more specifically with numerical values, but we can also find customer segments based on their product interests.

The items that customers purchase have hidden insights into what types of items each customer is interested in and what they are likely to purchase more of. There are multiple approaches to segmenting customers based on the products that they have purchased in the past, such as simply grouping by the product categories that they have purchased from. However, in this exercise, we are going to expand on the topic of the embedding vectors that we touched on in Chapter 5.

If you have not already, you may need to install Hugging Face...

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