Machine Learning and Generative AI for Marketing: Take Your Data-Driven Marketing Strategies to the Next Level Using Python
About this ebook
Key Features
Enhance customer engagement and personalization through predictive analytics and advanced segmentation techniques
Combine Python programming with the latest advancements in generative AI to create marketing content and address real-world marketing challenges
Understand cutting-edge AI concepts and their responsible use in marketing
Book Description
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.
What you will learn
Master key marketing KPIs with advanced computational techniques
Use explanatory data analysis to drive marketing decisions
Leverage ML models to predict customer behaviors, engagement levels, and customer lifetime value
Enhance customer segmentation with ML and develop highly personalized marketing campaigns
Design and execute effective A/B tests to optimize your marketing decisions
Apply natural language processing (NLP) to analyze customer feedback and sentiments
Integrate ethical AI practices to maintain privacy in data-driven marketing strategies
Who this book is for
This book targets a diverse group of professionals: Data scientists and analysts in the marketing domain looking to apply advanced AI ML techniques to solve real-world marketing challenges Machine learning engineers and software developers aiming to build or integrate AI-driven tools and applications for marketing purposes Marketing professionals, business leaders, and entrepreneurs who must understand the impact of AI on marketing Reader are presumed to have a foundational proficiency in Python and a basic to intermediate grasp of ML principles and data science methodologies.
About the author
Yoon Hyup Hwang is a data science and engineering leader who has authored multiple books on applied ML and data science. He has over a decade of experience and expertise in delivering extremely high ROI data products and solutions that result in multi-million-dollar annual recurring revenue and savings across various industries that include finance, insurance, ads and marketing, manufacturing, and supply chain. He holds an MSE degree in Computer and Information Technology from the University of Pennsylvania and a BA in Economics from the University of Chicago.
Nicholas C. Burtch, PhD, is a recognized data science researcher and thought leader with over ten years of experience in leading complex, data-driven projects. He has an extensive track record of deploying end-to-end ML solutions for understanding large-scale structured and unstructured data in industries ranging from finance to scientific research. Nick has published dozens of peer-reviewed research articles that have received thousands of citations and is a US patent holder. He received his PhD and MS in Chemical Engineering from the Georgia Institute of Technology and holds a BS in Chemical Engineering from the University of Michigan.
