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  • Book Overview & Buying Machine Learning and Generative AI for Marketing
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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 have covered a lot about building predictive models using an Online Purchase dataset. We have explored two different tree-based models, random forest and GBDT, and how to build predictive models to forecast who is likely to convert. Using the same example, we have also discussed how we can build neural network models that are the backbone of deep learning models. There is great flexibility in how you architect the neural network model, such as wide network, deep network, or wide and deep network. We have briefly touched on the activation functions and optimizers while building neural network models, but we suggest you do some more in-depth research into how they affect the performances of neural network models. Lastly, we have discussed what A/B testing is, how to conduct A/B testing, and how to interpret the A/B testing results. We have simulated A/B testing with the models we built for a scenario where we want to choose the best model for capturing the...

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