Dong Marketing Analytics and Data Science

Tools and Models
Erscheinungsjahr 2026
ISBN: 978-3-032-11130-2
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 233 Seiten

Reihe: Business and Management (R0)

This textbook demonstrates the application of recent advancements in data science to address various marketing issues. It provides a unique framework for transforming marketing problems into data science problems, which is a crucial first step that is often overlooked in books that focus solely on data analytical tools. It also emphasizes the intuitive understanding of the analytical tools and data science methodologies, in addition to presenting their limitations and best use cases. Students will learn why certain data science tools work well for particular marketing problems, while others may not. Finally, it explores how to translate the insights gained from these analytics tools into business decisions and how they can be used to inform the final decisions related to critical business questions.

Blending technical fields such as statistics, econometrics, and machine learning with business areas like marketing and customer understanding, this textbook provides solutions to various marketing and customer-centered questions using data analytical models and techniques, with each chapter covering a specific type of question. It will helps upper-level marketing student understand the technical aspects of data science in a way that is relevant and applicable to their future careers.

Zielgruppe

Graduate

Autoren/Hrsg.

Dong, Xiaojing

Inhaltsverzeichnis

Chapter 1: Getting Ready for Data Analytics with R and Python.- Chapter 2: The marketing questions and the data science tools.- Chapter 3: Marketing data collections and resources.- Chapter 4: Marketing resource allocation and media mix modelling.- Chapter 5: Market segmentation and clustering analysis.- Chapter 6: Targeting and propensity score modelling, part one.- Chapter 7: Targeting and propensity score modelling, part two.- Chapter 8: Forecasting and Bass model.- Chapter 9: Bayesian statistics and marketing attribution.- Chapter 10: Causal analysis and random control experiments.- Chapter 11: Causal identification using big data analytics.- Chapter 12: Customer analytics and customer lifetime value.- Chapter 13: Artificial Intelligence and its Potential Implications.

Über Autor(innen)

Xiaojing Dong is Professor of Marketing and Business Analytics at the Leavey School of Business at Santa Clara University, USA. With a PhD in Engineering and extensive training in Econometrics and Statistics, she utilizes advanced data analytics to tackle complex marketing problems. Drawing on her expertise across multiple disciplines, Dr. Dong co-founded the Master of Science in Business Analytics program at SCU and served as its founding director for four years. She is a sought-after consultant for high-tech companies seeking data science-related guidance on marketing decisions.