Service analytics describes the process of capturing, processing, and analyzing the data generated from the execution of a service system to improve, extend, and personalize a service to create value for both providers and customers. This chapter explains how services, especially electronic services, generate a wealth of data which can be used for their analysis. The main tasks and methods, from areas such as data mining and machine learning, which can be used for analysis are identified. To illustrate their application, the data generated from the execution of an IT service is analyzed to extract business insights.
1. Briefly describe the main characteristics that distinguish service analytics from the general data analytics paradigm.
2. This chapter defines three levels underwhich service analyticsmethods fall based on the action performed with the discovered knowledge. Briefly describe these levels and give example of analytics methods that fall under each of them.
3. What are the differences between classification and prediction methods?
4. What is cluster analysis? What is a cluster? List the main differences between partitioning methods and hierarchical methods for cluster analysis. Give examples in each method. Describe a scenario in which application of clustering to service systems.
5. Describe the characteristics of the regression method. Define a scenario in which regression can be applied to service systems. How is regression different from cluster analysis?
6. Describe the main types of text mining methods relevant for services. Give one example for each of the following cases: (a) an application that uses document categorization techniques on the data generated by the consumption of a particular service and (b) an application that uses sentiment analysis methods on the data generated by the consumption of a particular service.