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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.

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.

Applications powered by artificial intelligence (AI) and machine learning (ML) have become a crucial factor for success in modern sales organizations. This chapter investigates how Salesforce achieves scalable AI for businesses of all sizes and explores sales applications of AI and machine learning that are most common across industries. It is divided into three sections. The first section gives an introduction to AI and machine learning. The second section shows how data and automated machine learning models provide the foundation for AI applications and explains how Salesforce achieves scalable AI and machine learning for business applications. The third section demonstrates how AI applications impact the modern sales organization and the work of sales representatives. AI does not replace humans; it allows sales organizations to better engage with prospects and customers. Sales representatives using AI outperform their counterparts that rely purely on traditional methods.

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.

Across disciplines, researchers and practitioners employ decision tree ensembles such as random forests and XGBoost with great success. What explains their popularity? This chapter showcases how marketing scholars and decision-makers can harness the power of decision tree ensembles for academic and practical applications. The author discusses the origin of decision tree ensembles, explains their theoretical underpinnings, and illustrates them empirically using a real-world telemarketing case, with the objective of predicting customer conversions. Readers unfamiliar with decision tree ensembles will learn to appreciate them for their versatility, competitive accuracy, ease of application, and computational efficiency and will gain a comprehensive understanding why decision tree ensembles contribute to every data scientist's methodological toolbox.

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.

With the rise of artificial intelligence and machine learning, competitive data science platforms like Kaggle are gaining momentum. From a host's perspective, the platforms offer access to a large crowd of data scientists who can solve their data science problems efficiently and cost-effectively. From the participant's perspective, the platforms provide the opportunity to apply their skills to real-world problems, interact with other data scientists, and win prizes. The chapter provides an overview of competitive data science platforms and assesses their potential for business and academia. A series of opportunities and challenges of data competitions are outlined, and a concrete case is illustrated. The chapter also demonstrates common pitfalls that hosts of data competitions need to be aware of by discussing the relevance of problem definition, data leakage, and metrics to evaluate different solutions.