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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.

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.

Content marketing is a crucial aspect of digital marketing in modern firms. By generating content that is interesting and engaging, companies have the two-fold advantage of promoting their products in a relatable way, while increasing familiarity and engagement with the brand. As data scientists at Credit Suisse, we value our content teams because their voice is the bank's voice. We strive to provide them with the best tools to increase their articles' success. With the help of machine learning, we have created digital products that allow them to improve articles before publication, recommend them to the most interested readers, and track their performance. The chapter begins with a brief introduction to content marketing, followed by an overview of our data, a review of the business challenges we have encountered, and the machine learning solutions we have developed in order to provide the best data insights to our internal and external stakeholders. We close the chapter with a brief summary of our work.

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.

Neural networks, which provide the basis for deep learning, are a class of machine learning methods that are being applied to a diverse array of fields in business, health, technology, and research. In this chapter, we survey some of the key features of deep neural networks and aspects of their design and architecture. We give an overview of some of the different kinds of networks and their applications and highlight how these architectures are used for business applications such as recommender systems. We also provide a summary of some of the considerations needed for using neural network models and future directions in the field.

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.

In the age of data, enterprises have more information available to them than ever before, yet many organizations still struggle to harness its full potential. In this chapter, we explore the data value equation and how it translates into an end-to-end data management strategy that enables enterprises to turn their business data into business value. Starting with the concept of “amount,” the chapter looks at the challenge of storing big data. The second element of the equation relates to the “quality” of data and its fundamental role in enabling confident decision-making. Finally, the third element of the equation focuses on the importance of the consumption of that data in analytics tools that not only visualize the data but proactively help users uncover, explore, and act on insights. By yielding the highest value at every stage of this equation, businesses can see more, understand more, and do more with their data.

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.