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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

This chapter underscored the importance of sentiment analysis in modern marketing strategies. It introduced sentiment analysis as a key tool to interpret vast quantities of unstructured text data, such as social media conversations, to refine marketing strategies, brand messaging, or customer experience. By utilizing the Twitter Airline dataset, we covered the end-to-end process needed to classify sentiment as positive or negative, using both traditional NLP and more advanced GenAI methods involving pre-trained LLMs. We then covered an array of tools for the visualization and interpretation of these results to derive actionable marketing insights. This chapter should leave you equipped with the necessary skills to harness sentiment analysis effectively, for applications ranging from brand reputation monitoring to aligning marketing messages with customer preferences.

Looking ahead to the next chapter, we will progress from understanding customer sentiment to actively shaping...

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Machine Learning and Generative AI for Marketing
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