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Machine Learning and Generative AI for Marketing

Machine Learning and Generative AI for Marketing

By : Yoon Hyup Hwang, Nicholas C. Burtch
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Machine Learning and Generative AI for Marketing

Machine Learning and Generative AI for Marketing

By: Yoon Hyup Hwang, Nicholas C. Burtch

Overview of this book

In the dynamic world of marketing, the integration of artificial intelligence (AI) and machine learning (ML) is no longer just an advantage—it's a necessity. Moreover, the rise of generative AI (GenAI) helps with the creation of highly personalized, engaging content that resonates with the target audience. This book provides a comprehensive toolkit for harnessing the power of GenAI to craft marketing strategies that not only predict customer behaviors but also captivate and convert, leading to improved cost per acquisition, boosted conversion rates, and increased net sales. Starting with the basics of Python for data analysis and progressing to sophisticated ML and GenAI models, this book is your comprehensive guide to understanding and applying AI to enhance marketing strategies. Through engaging content & hands-on examples, you'll learn how to harness the capabilities of AI to unlock deep insights into customer behaviors, craft personalized marketing messages, and drive significant business growth. Additionally, you'll explore the ethical implications of AI, ensuring that your marketing strategies are not only effective but also responsible and compliant with current standards By the conclusion of this book, you'll be equipped to design, launch, and manage marketing campaigns that are not only successful but also cutting-edge.
Table of Contents (16 chapters)
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14
Other Books You May Enjoy
15
Index

Summary

In this chapter, we discussed different ways to segment the customer base. We first looked at how new versus repeat customers contribute to revenue, as well as how monthly progressions of new and repeat customer numbers can tell us which segment or group of customers to focus on during the next marketing campaigns. Then, we discussed how the K-means clustering algorithm can be used to programmatically build and identify different customer segments. Using the sales amount, order quantity, and refunds, we experimented with how these factors can be used to build different customer segments. In lieu of doing it, we touched on silhouette scores as a criterion for finding the best number of clusters and how log transformation can be beneficial when dealing with highly skewed datasets. Lastly, we used word and sentence embedding vectors to convert the product descriptions into numerical vectors with contextual understanding and further built customer segments based on their product...

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Machine Learning and Generative AI for Marketing
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