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Hands-On Data Science for Marketing: Improve your marketing strategies with machine learning using Python and R de Yoon Hyup Hwang
DescripciĆ³n - ReseƱa del editor Optimize your marketing strategies through analytics and machine learning Key Features Understand how data science drives successful marketing campaigns Use machine learning for better customer engagement, retention, and product recommendations Extract insights from your data to optimize marketing strategies and increase profitability Book Description Regardless of company size, the adoption of data science and machine learning for marketing has been rising in the industry. With this book, you will learn to implement data science techniques to understand the drivers behind the successes and failures of marketing campaigns. This book is a comprehensive guide to help you understand and predict customer behaviors and create more effectively targeted and personalized marketing strategies. This is a practical guide to performing simple-to-advanced tasks, to extract hidden insights from the data and use them to make smart business decisions. You will understand what drives sales and increases customer engagements for your products. You will learn to implement machine learning to forecast which customers are more likely to engage with the products and have high lifetime value. This book will also show you how to use machine learning techniques to understand different customer segments and recommend the right products for each customer. Apart from learning to gain insights into consumer behavior using exploratory analysis, you will also learn the concept of A/B testing and implement it using Python and R. By the end of this book, you will be experienced enough with various data science and machine learning techniques to run and manage successful marketing campaigns for your business. What you will learn Learn how to compute and visualize marketing KPIs in Python and R Master what drives successful marketing campaigns with data science Use machine learning to predict customer engagement and lifetime value Make product recommendations that customers are most likely to buy Learn how to use A/B testing for better marketing decision making Implement machine learning to understand different customer segments Who this book is for If you are a marketing professional, data scientist, engineer, or a student keen to learn how to apply data science to marketing, this book is what you need! It will be beneficial to have some basic knowledge of either Python or R to work through the examples. This book will also be beneficial for beginners as it covers basic-to-advanced data science concepts and applications in marketing with real-life examples.Table of Contents Data Science and Marketing Key Performance Indicators and Visualizations Drivers behind Marketing Engagement From Engagement to Conversion Product Analytics Recommending the Right Products Exploratory Analysis for Customer Behavior Predicting the Likelihood of Marketing Engagement Customer Lifetime Value Data-Driven Customer Segmentation Retaining Customers A/B Testing for Better Marketing Strategy What's Next? BiografĆa del autor 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).
Detalles del Libro
- Name: Hands-On Data Science for Marketing: Improve your marketing strategies with machine learning using Python and R
- Autor: Yoon Hyup Hwang
- Categoria: Libros,Libros universitarios y de estudios superiores,Ciencias informƔticas
- TamaƱo del archivo: 12 MB
- Tipos de archivo: PDF Document
- Descargada: 125 times
- Idioma: EspaƱol
- Archivos de estado: AVAILABLE
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