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Collecting customer data is increasingly becoming an automatic process at different customer touchpoints, carried out with the help of artificial intelligence. Modern telecommunication networks are necessary for collecting this data in a timely manner. This chapter describes 5G, the latest generation of mobile telecommunication networks. It outlines the current stage of development and use cases being introduced or planned by telecommunication companies worldwide. A key aspect of the chapter is to explain what 5G means for collecting customer data.

In today's economy, experiences are a distinct offering that have become the core selling point for some of the world's most successful companies. From banking and transportation, to home exercise and healthcare, companies have differentiated themselves by designing distinct experiences alongside their core goods and services. And at the heart of this transformation are the data, systems, processes, and culture needed to understand more about customers and employees in order to design unique experiences for every individual. In this chapter we explore how success in the experience economy is not simply a case of gathering more data, but instead looking at a different type of data – Experience Data. With examples and case studies from some of the world's most successful companies, we look at how the discipline of experience management (XM) and the technology available to organizations today is fundamentally changing how companies operate – and win – in the experience economy.

Digitalization is changing the assets, competencies, and value creation of the customer insight function. New data sources, methods, and technologies provide an unprecedented wealth of data and opportunity for efficiency. At the same time, it is leading to an evolution in necessary capabilities such as data synthesis, networking, and constant learning. Changes in the means of value creation have included automation of insights, more frequent evaluation of business results, and more emotional inspiration. Customer insights in the machine age drive customer centricity and go beyond the descriptive research function of previous “market research” within companies.

Machine learning and artificial intelligence (AI) have arisen as the availability of larger data sources, statistical methods, and computing power have rapidly and simultaneously evolved. The transformation is leading to a revolution that will affect virtually every industry. Businesses that are slow to adopt modern data practices are likely to be left behind with little chance to catch up.

The purpose of this chapter is to provide a brief overview of machine learning and AI in the business setting. In addition to providing historical context, the chapter also provides justification for AI investment, even in industries in which data is not the core business function. The means by which computers learn is de-mystified and various algorithms and evaluation methods are presented. Lastly, the chapter considers various ethical and practical consequences of machine learning algorithms after implementation.

Every second, vast amounts of data are generated and stored on the Internet. Data scraping makes these data accessible and usable for business and scientific purposes. Web-scraped data are of high value to businesses as they can be used to inform many strategic decisions such as pricing or market positioning. Although it is not difficult to scrape data, particularly when they come from public websites, there are six key steps that analysts should ideally consider and follow. Following these steps can help to better harness the business value of online data.

Storytelling can be the difference between your data making a true contribution or remaining unheard. Because in order to move your stakeholders to act, they need to thoroughly understand why your data matters, and often on an emotional as well as a rational level. And for that, there is no more powerful tool than storytelling.

In this chapter, we'll apply the techniques of the most powerful story form of all, movies, to data slides, and in the process, make them easy to understand and believe in.

You'll read and see techniques and examples that will help you:

• Focus your data so it's quick and clear.

• Frame it in ways that feel tangible and relatable to your stakeholders.

• Make the reason why it matters more powerful so your stakeholders will be moved to act.

• How storytelling will become even more interesting in the age of machines.

Focus your data so it's quick and clear.

Frame it in ways that feel tangible and relatable to your stakeholders.

Make the reason why it matters more powerful so your stakeholders will be moved to act.

How storytelling will become even more interesting in the age of machines.

This chapter introduces the KontoSensor, a digital service offered by Deutsche Bank since September 2018, as an example of data processing using predictive analytics. We present the motivation behind this digital service, the use cases and methods currently implemented, the way they have been created, and measures to increase the usage of the KontoSensor. With KontoSensor, Deutsche Bank offers a digital service to its clients to analyze their transactions on their current accounts using methods from predictive analytics and to inform them when irregularities are found. Twelve months after the start, 90,000 clients are already using this service and experiencing the results of data science firsthand.