The machine age of customer insight

著者

    • Einhorn, Martin

書誌事項

The machine age of customer insight

edited by Martin Einhorn ... [et al.]

Emerald, 2021

1st ed

  • : print

大学図書館所蔵 件 / 全2件

この図書・雑誌をさがす

注記

Includes bibliographical references and index

内容説明・目次

内容説明

We are living in a new machine age offering unique opportunities, particularly for generating customer insights, which is radically transforming the way business value is created. Across industries, players are affected by the pace of progress of machine learning tools, novel technologies, and the abundance of data. These developments require mastering new capabilities. The Machine Age of Customer Insight explains the transformation of customer insights and demonstrates the growing impact of machine learning. Thought leaders from renowned universities in the US and Europe as well as from different industries provide a comprehensive overview. Addressing both academics and practitioners, they discuss the transformation, cutting edge tools, and success factors to thrive in the new age. The book shows how machine learning helps to understand customers better and faster. It supports everyone who considers the machine age a great opportunity to gain a competitive advantage by transforming customer insights into business value.

目次

  • Introduction
  • Martin Einhorn, Michael Loeffler, Emanuel de Bellis, Andreas Herrmann, and Pia BurghartzChapter 1. Transformation of Customer Insights
  • Martin Einhorn and Michael Loeffler Chapter 2. Intelligent Applications in the Modern Sales Organization
  • Gilberto Picareta, Martin Kloehn, and Eugenie Weissheim Chapter 3. Voice and Facial Coding in Market Research
  • Niels Neudecker, Deepak Varma, David Wright, and Robert Powell Chapter 4. Machine-Driven Content Marketing
  • Javiera M. Guedes, Akinbami Akinwale, and Maria Requeman Fontecha Chapter 5. Leveraging Customer Insights with 5G
  • Marco Ottawa Chapter 6. Overview of Machine Learning Tools
  • Brett Lantz Chapter 7. Neural Networks and Deep Learning
  • Hongming Wang, Ryszard Czerminski, and Andrew C. Jamieson Chapter 8. Classification Using Decision Tree Ensembles
  • Jochen Hartmann Chapter 9. Text Analytics and Natural Language Processing
  • Ted Kwartler Chapter 10. A Step-By-Step Guide for Data Scraping
  • Reto Hofstetter Chapter 11. Data Privacy: A Driver for a Competitive Advantage
  • Timo Jakobi, Max von Grafenstein, and Thomas Schildhauer Chapter 12. Data Collection: Welcome to the Experience Economy
  • David Mingle Chapter 13. Data Growth: Generating Business Value with Cloud Services
  • Gerrit Kazmaier Chapter 14. Data Competitions: Crowdsourcing With Data Science Platforms
  • Jenny Lena Zimmermann Chapter 15. Data Processing: Kontosensor as an Application of Predictive Analytics
  • Raimund Blache, Lars Fetzer, Rene Michel, and Tobias von Martens Chapter 16. Data Visualization: The Power of Storytelling
  • Ted Frank

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