Contribution to the book “The Machine Age of Customer Insight” that is now available!

Einhorn, Martin; Löffler, Michael; de Bellis, Emanuel; Herrmann, Andreas & Burghartz, Pia (ed.): The Machine Age of Customer Insight. Emerald Publishing Limited, 2021, – ISBN 9781839096976.

Available at: (Early birds save 30% with promo code “MACHINE30”)

The emerging machine age offers a unique opportunity to gain novel, profound customer insights and unlock immense potential in different business areas, especially for smart products that generate large amounts of data. The abundance of data and the pace of progress in turning data into actionable knowledge is affecting players in practically every sector. In 16 short and hands-on chapters, the book summarizes recent developments in business and academia and offers readers practical guidance demonstrating how machine learning techniques can help to understand customers better and faster.

Excellent authors from innovative firms and renowned universities provide a comprehensive overview of the transformation of customer insights (first part of the book), the tools needed to generate these insights (second part of the book), and the success factors to thrive in the new age (third part of the book). We are proud to have contributed alongside the outstanding editors and authors!

Reto Hofstetter presents a concise six steps guide for data scraping to exploit the business value of online data. Vast amounts of data are generated and stored on the Internet every second. Scraping online data can be highly valuable to businesses as they can be used to inform different strategic decisions. While it may often be quite easy to access these data, it is crucial to be aware of and avoid pitfalls in order to gain useful business insights. Reto’s chapter provides a hands-on read with a step-by-step guide helping to leverage the value of web-scraped data. 

Jenny Zimmermann provides an overview of data competition platforms and assesses their business potential. With the rise of machine learning, competitive data science platforms like Kaggle are also gaining momentum. Data competitions can be very valuable to companies because the platforms offer access to a large crowd of skilled data scientists who can solve their data science problems efficiently and cost-effectively. Yet, there are also downsides and pitfalls that need to be taken into account when planning to host a competition. Jenny’s chapter discusses opportunities and challenges by presenting a concrete case. 

The book provides a unique view bridging industry and academia and supports everyone who considers the machine age a great opportunity to gain a competitive advantage by transforming customer insights into business value. Have a sneak peek into the book here:


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