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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
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Index

Computing and visualizing KPIs from data with Python

With the foundation of various marketing KPIs that we have discussed so far, let us dive into a potential real-world use case of some of these KPIs, how to compute and visualize them, and how to interpret them for further data-driven decision-making. We will be using an insurance product marketing dataset for our example.

From a financial point of view, the success of marketing is to bring in more revenue with less spending. The most straightforward approach in tracking and measuring this is to look at conversion rate, CLV, and CPA. The conversion rate tells us the percentage of leads converted into paying customers, CLV tells us the estimated revenue being generated per acquired customer, and CPA tells us how much it costs to acquire a paying customer. By combining these three KPIs, we can understand the ROI and contribution to the net income.

Using the above-mentioned example dataset, let’s discuss how these KPIs...

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