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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
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15
Index

Predicting customer conversion with tree-based algorithms

Predictive analytics or modeling can be applied at various stages of the customer life cycle. If you recall from Chapter 2, there are largely five stages that we can break down a customer life cycle into: Awareness, Engagement, Conversion, Retention, and Loyalty, as shown in the following diagram:

Figure 6.1: Customer life cycle diagram from Chapter 2

The applicability of predictive modeling is broad, depending on your marketing goal. For example, if you have a new brand or product launch and would like to improve new product awareness via ads on social media, you can build predictive models that can help you identify the target customers who are likely to click on the ads. On the other hand, if you would like to improve product purchase conversion rates, you can build predictive models that can identify customers who are more likely to make purchases in the next X number of days and target them. This results in...

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Machine Learning and Generative AI for Marketing
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