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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 gained an in-depth understanding of the factors affecting certain customer behaviors. We explored how regression analysis can help us understand the directional relationships between various factors and the outcome of customer behavior. Using our auto insurance marketing dataset as an example, we saw how to implement the statsmodels package in Python to run regression analysis and unveil the successes behind engagement rate marketing campaigns. We also discussed how decision trees can help us identify complex interactions that result in certain outcomes. Using a bank marketing dataset as an example and the scikit-learn package in Python, we successfully built a decision tree that unveiled the hidden interactions among various factors that lead to customer conversions. Lastly, with the bank churn dataset and the dowhy package in Python, we saw how causal analysis can bring deep insights into the root causes and directional contributions to the outcome of...

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Machine Learning and Generative AI for Marketing
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