Hwang - Hands-on data science for marketing: improve your marketing strategies with machine learning using Python and R

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Yoon Hyup Hwang is a seasoned data scientist in the marketing and financial sectors with expertise in predictive modeling, machine learning, statistical analysis, and data engineering. He has 8+ years' experience of building numerous machine learning models and data products using Python and R. He holds an MSE in computer and information technology from the University of Pennsylvania and a BA in economics from the University of Chicago.

In his spare time, he enjoys practicing various martial arts, snowboarding, and roasting coffee. Born and raised in Busan, South Korea, he currently works in New York and lives in New Jersey with his artist wife, Sunyoung, and a playful dog, Dali (named after Salvador Dali).

Rohan Dhupar is in the final semester of his degree computer science and engineering from the Rustamji Institute of Technology. Since November 2017, he has done a number of internships, mainly in relation to natural language processing for both US and Indian companies, focusing on machine and deep learning. He has undertaken numerous projects and achieved much in his academic life. He ranks in the top 1% of Kaggle experts, has been a Microsoft Student Partner since 2017, and has received numerous invitations from established companies to join their data science software engineering teams. He is currently working as a data scientist, focusing mainly on image processing projects, for Innovations Labs, a US firm based in India .

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The adoption of data science and machine learning for marketing has been on the rise, from small to large organizations. With data science, you can better understand the drivers behind the successes and failures of previous marketing strategies and you can better understand customer behavior and interaction with your products. With data science, you can also predict customer behavior and create better targeted and personalized marketing strategies for better cost per acquisition, higher conversion rates, and higher net sales. With this book, you will be able to apply various data science techniques to create data-driven marketing strategies.

This book serves as a practical guide to performing simple-to-advanced tasks in marketing. You will use data science to understand what drives sales and customer engagement. You will use machine learning to forecast which customer is likely to engage with products more and has the highest expected lifetime value. You will also use machine learning to understand what data tells you about different customer segments and recommend the right products for individual customers that they are most likely to purchase. By the end of this book, you will be well-versed with various data science and machine learning techniques and how they can be utilized for different marketing goals.

Personally, I would have benefited from books such as this. When I was embarking on my career in data science and marketing, there were abundant resources on theories and details of different data science and machine learning techniques, but not so much on how to use these technologies and techniques for marketing specifically. Learning about the theories was vastly different from actually utilizing and applying them to real-world business use cases in marketing. In this book, I hope to share my experience and the knowledge acquired through significant instances of trial and error in applying data science and machine learning to different marketing goals. By the end of this book, you will have a good understanding of what types of technologies and techniques are used for different marketing use cases, where to find additional resources, and what to study next after this book.